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"content_sha256": "6c2ca4a4f85cd27ef60e9ceda9c44a37093fd32f94b60b564b3e0b43e751b260" }, { "id": "adaptive-trial-simulator", "version": "1.0.0", "display_name": "adaptive-trial-simulator", "summary": "Design and simulate adaptive clinical trials with interim analyses, decision rules, and operating-characteristic summaries; use when planning adaptive designs or comparing stopping, enrichment, or sample-size re-estimation strategies.", "category": "Protocol Design", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Protocol Design/adaptive-trial-simulator" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 91, "max_score": 100, "report_url": 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and validation; use when you have designed protein sequences and need wet-lab experimental validation (e.g., binding, expression, thermostability, enzyme activity) and API-based submission/status/result retrieval.", "category": "Other", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Other/adaptyv" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 90, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Other/adaptyv/adaptyv_audit_result_v2.json", "evaluated_on": "2026-03-23", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 83, "max_score": 100 }, "dynamic_score": { "score": 94.6, "max_score": 100 } }, 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Includes temporal analysis, MedDRA coding, causality assessment using WHO-UMC or Naranjo criteria, and multi-format output.", "category": "Academic Writing", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Academic Writing/adverse-event-narrative" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "Unknown", "evaluation": { "kind": "upstream-self-assessment", "score": 90, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Academic%20Writing/adverse-event-narrative/eval_report_adverse-event-narrative_result.json", "evaluated_on": "2026-06-15", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 94, "max_score": 100 }, "dynamic_score": { "score": 86.6, "max_score": 100 } }, "release": { "path": "releases/adverse-event-narrative/1.0.1.json", "sha256": "806ef12bf53e327422ecc32317c3cb23efa501a34be8a1a4ef15dc3691832631" }, "artifact": { "path": "shards/96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b.zip", "sha256": "96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b", "bytes": 66876850, "skill_path": "adverse-event-narrative" }, "content_sha256": "74974b1a36061e72e31dde0db857d9b753425202cd5be57d9049f1be45373994" }, { "id": "aim-and-hypothesis-designer", "version": "1.0.0", "display_name": "aim-and-hypothesis-designer", "summary": "Designs primary aims, secondary aims, and testable hypotheses from broad biomedical research ideas. Use this skill when a user needs to convert a loose study idea into a tighter protocol-framing structure with clear aim hierarchy, hypothesis discipline, and separation between hypothesis-driven and exploratory components. Always keep aims answerable, non-overlapping, and aligned to the intended evidence type and study scope.", "category": "Protocol Design", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "awesome-med-research-skills/Protocol Design/aim-and-hypothesis-designer" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 89, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/awesome-med-research-skills/Protocol%20Design/aim-and-hypothesis-designer/eval_report_aim-and-hypothesis-designer_result.json", "evaluated_on": "2026-04-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 91, "max_score": 100 }, "dynamic_score": { "score": 87.2, "max_score": 100 } }, "release": { "path": "releases/aim-and-hypothesis-designer/1.0.0.json", "sha256": "eeb3068e0704adb9a3ff769f37b35038ad2207f2c4008ba4ca095979f8d5ea1f" }, "artifact": { "path": "shards/fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0.zip", "sha256": "fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0", "bytes": 1383584, "skill_path": "aim-and-hypothesis-designer" }, "content_sha256": "83d481dee2ea30345c32bad600e6feee0654744ee58fc8b9ca0425a2c9752295" }, { "id": "anatomy-quiz-master", "version": "1.0.1", "display_name": "anatomy-quiz-master", "summary": "Generate interactive anatomy quizzes for medical education with multiple.", "category": "Academic Writing", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Academic Writing/anatomy-quiz-master" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 85, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Academic%20Writing/anatomy-quiz-master/anatomy-quiz-master_audit_result_v2.json", "evaluated_on": "2026-03-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 88, "max_score": 100 }, "dynamic_score": { "score": 83.6, "max_score": 100 } }, "release": { "path": "releases/anatomy-quiz-master/1.0.1.json", "sha256": "e8e57fabfe57eb7a983e00153e656461f26ea23a51ad079f1fe464cf63b55bff" }, "artifact": { "path": "shards/96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b.zip", "sha256": "96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b", "bytes": 66876850, "skill_path": "anatomy-quiz-master" }, "content_sha256": "a86f52e949059771e64448886e3534041ff0c4170ad90b9063019b988c74d8ea" }, { "id": "animal-and-cell-validation-planner", "version": "1.0.0", "display_name": "animal-and-cell-validation-planner", "summary": "Designs cell-based and animal-based validation plans that translate computational, omics, biomarker, genetic, or clinical findings into experimentally testable validation routes. Always use this skill whenever a user wants to move from an in silico, statistical, or clinical association finding toward wet-lab validation using cell systems, organoid-like systems, xenograft or genetically relevant animal models. It should define the exact claim to test, separate mechanism-testing from association-support and translational-support goals, choose the best-fit model family, specify perturbation strategy, readouts, controls, sequencing of experiments, and four workload configurations (Lite / Standard / Advanced / Publication+) with one recommended primary plan. Never fabricate model availability, reagent availability, species relevance, assay feasibility, phenotype penetrance, expected effect sizes, validation success, or literature references.", "category": "Protocol Design", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "awesome-med-research-skills/Protocol Design/animal-and-cell-validation-planner" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 88, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/awesome-med-research-skills/Protocol%20Design/animal-and-cell-validation-planner/eval_report_animal-and-cell-validation-planner_result.json", "evaluated_on": "2026-04-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 92, "max_score": 100 }, "dynamic_score": { "score": 86, "max_score": 100 } }, "release": { "path": "releases/animal-and-cell-validation-planner/1.0.0.json", "sha256": "623f4227f9a121aa4b79d45860c14722a8cd6e62e7644faaf53724406877e7d1" }, "artifact": { "path": "shards/fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0.zip", "sha256": "fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0", "bytes": 1383584, "skill_path": "animal-and-cell-validation-planner" }, "content_sha256": "e4b3a8b3cc96213a6d1c775d67b5ea216bd7bd20cdf17f89a16adf5d600b592c" }, { "id": "anki-card-creator", "version": "1.0.1", "display_name": "anki-card-creator", "summary": "Use anki-card-creator for academic writing workflows that need structured execution, explicit assumptions, and clear output boundaries for study-card generation.", "category": "Academic Writing", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Academic Writing/anki-card-creator" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 85, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Academic%20Writing/anki-card-creator/anki-card-creator_audit_result_v2.json", "evaluated_on": "2026-03-23", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 88, "max_score": 100 }, "dynamic_score": { "score": 83.6, "max_score": 100 } }, "release": { "path": "releases/anki-card-creator/1.0.1.json", "sha256": "af29a0460ee424b4f738531974da883d13f34eeb91f10b43c5d71e9ddb57703d" }, "artifact": { "path": "shards/96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b.zip", "sha256": "96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b", "bytes": 66876850, "skill_path": "anki-card-creator" }, "content_sha256": "fd85290fd6ac995f15fe72fc400d21830be18ae45142b7cd83c076411926808a" }, { "id": "anndata", "version": "1.0.1", "display_name": "anndata", "summary": "Data structure for annotated matrices in single-cell analysis; use when reading/writing .h5ad (or zarr) and exchanging data with the scverse ecosystem.", "category": "Data Analysis", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Data Analysis/anndata" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 86, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Data%20Analysis/anndata/anndata_audit_result_v1.json", "evaluated_on": "2026-03-23", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 85, "max_score": 100 }, "dynamic_score": { "score": 86.6, "max_score": 100 } }, "release": { "path": "releases/anndata/1.0.1.json", "sha256": "05491daf2af3f9c1cb8c4f7422382bdbd5dbfed83b2291b00b88bdbe50e303df" }, "artifact": { "path": "shards/96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b.zip", "sha256": "96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b", "bytes": 66876850, "skill_path": "anndata" }, "content_sha256": "239eea4681766770978768f6b886dfda57d1f9a90f33d9336cf183a70e5a5f47" }, { "id": "arboreto", "version": "1.0.0", "display_name": "arboreto", "summary": "Infer gene regulatory networks (GRNs) from gene expression matrices using GRNBoost2 or GENIE3; use when analyzing bulk or single-cell RNA-seq to identify TF→target regulatory relationships.", "category": "Evidence Insight", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Evidence Insight/arboreto" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 85, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Evidence%20Insight/arboreto/arboreto_audit_result_v1.json", "evaluated_on": "2026-03-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 85, "max_score": 100 }, "dynamic_score": { "score": 85, "max_score": 100 } }, "release": { "path": "releases/arboreto/1.0.0.json", "sha256": "f8ea854a9db4c35e001f2a29a82812385681f97939ce2c4298ff254ad9c5a38c" }, "artifact": { "path": "shards/363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb.zip", "sha256": "363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb", "bytes": 1471244, "skill_path": "arboreto" }, "content_sha256": "af266075f6e25375ef17ba59b9a821cb463aee5d308d224b21542e2640da9349" }, { "id": "article-format-adjustment", "version": "1.0.0", "display_name": "article-format-adjustment", "summary": "Adjust academic paper formatting and convert between DOCX/LaTeX/Markdown when you need to meet a journal or school template requirement.", "category": "Other", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Other/article-format-adjustment" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 90, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Other/article-format-adjustment/article-format-adjustment_audit_result_v1.json", "evaluated_on": "2026-03-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 83, "max_score": 100 }, "dynamic_score": { "score": 94.6, "max_score": 100 } }, "release": { "path": "releases/article-format-adjustment/1.0.0.json", "sha256": "83ebbe9080680308fff37ccb37fde3f8e8bd20c1bdc1e4ea65be0c092d2eaa83" }, "artifact": { "path": "shards/1ea735cead62d521282de0bf8a672a95515d73332ea4fa7d5d2ccf72eed45995.zip", "sha256": "1ea735cead62d521282de0bf8a672a95515d73332ea4fa7d5d2ccf72eed45995", "bytes": 342914, "skill_path": "article-format-adjustment" }, "content_sha256": "ea8179dc3fd42fb92752d8852dc8eed01847dbfe5f3155ef30586b7ac73ddcd8" }, { "id": "arxiv-database", "version": "1.0.0", "display_name": "arxiv-database", "summary": "Search and retrieve scientific preprints from arXiv; use it when you need to find papers by keyword/author/category, fetch metadata (abstract, DOI, PDF URL), or download PDFs for offline reading.", "category": "Evidence Insight", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Evidence Insight/arxiv-database" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 89, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Evidence%20Insight/arxiv-database/arxiv-database_audit_result_v1.json", "evaluated_on": "2026-03-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 78, "max_score": 100 }, "dynamic_score": { "score": 95.6, "max_score": 100 } }, "release": { "path": "releases/arxiv-database/1.0.0.json", "sha256": "7a073f936726e27bffb760c3013c56e8bdc9f06a5ad15e6a0aff07d5af07fde7" }, "artifact": { "path": "shards/363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb.zip", "sha256": "363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb", "bytes": 1471244, "skill_path": "arxiv-database" }, "content_sha256": "a0f4310b3c4583cf7cda9debbbba746c107dca36e05a37f720dbc4f0f8f89964" }, { "id": "arxiv-preflight", "version": "1.0.1", "display_name": "arxiv-preflight", "summary": "Run a submission-readiness preflight on a manuscript before arXiv upload. Use when the user is preparing an arXiv submission, asks to check a paper before uploading, mentions hallucinated or fake references, leftover LLM meta-comments / prompts in text, placeholder data (TODO, TBD, XX%), AI-use disclosure, scholarly integrity, research integrity, or arXiv moderation risk — even if they don't say \"preflight\". Also trigger on phrases like \"check my paper before arXiv\", \"verify my references\", \"scan for AI artifacts\", \"scan for LLM residue\", \"is my submission ready\", or \"review .tex/.bib before submit\".", "category": "Academic Writing", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "awesome-med-research-skills/Academic Writing/arxiv-preflight" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 90, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/awesome-med-research-skills/Academic%20Writing/arxiv-preflight/eval_report_arxiv-preflight_result.json", "evaluated_on": "2026-05-21", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 92, "max_score": 100 }, "dynamic_score": { "score": 88.6, "max_score": 100 } }, "release": { "path": "releases/arxiv-preflight/1.0.1.json", "sha256": "247d79644c2888f6486971d5f288cf63110c03f0fd82c07d14d88d9c0c9f601e" }, "artifact": { "path": "shards/96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b.zip", "sha256": "96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b", "bytes": 66876850, "skill_path": "arxiv-preflight" }, "content_sha256": "dc06e84105f49ae858d31d5074d45b375c18185ff4ba19874819bab14ca336e2" }, { "id": "author-response-builder", "version": "1.0.1", "display_name": "author-response-builder", "summary": "Turns reviewer comments into structured, professional point-by-point responses linked to manuscript revisions, clarifications, rebuttals, and additional analyses.", "category": "Academic Writing", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "awesome-med-research-skills/Academic Writing/author-response-builder" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 84, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/awesome-med-research-skills/Academic%20Writing/author-response-builder/eval_report_author-response-builder_result.json", "evaluated_on": "2026-04-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 88, "max_score": 100 }, "dynamic_score": { "score": 82, "max_score": 100 } }, "release": { "path": "releases/author-response-builder/1.0.1.json", "sha256": "8c659657d5a8dc90e9823a6f07adbc27d0ecd3b20328fce2d94f8134351063cb" }, "artifact": { "path": "shards/96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b.zip", "sha256": "96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b", "bytes": 66876850, "skill_path": "author-response-builder" }, "content_sha256": "66b971c51bf6bc5be5154d55880ec7f7c0130c38a061c12272d87597900b1b7f" }, { "id": "automated-soap-note-generator", "version": "1.0.1", "display_name": "automated-soap-note-generator", "summary": "Generate structured SOAP notes from clinical narratives, transcripts, or existing notes; use when the user needs de-identified clinical documentation organized into Subjective, Objective, Assessment, and Plan sections, with clear assumptions and review points.", "category": "Academic Writing", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Academic Writing/automated-soap-note-generator" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 90, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Academic%20Writing/automated-soap-note-generator/eval_report_automated-soap-note-generator_result.json", "evaluated_on": "2026-06-15", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 96, "max_score": 100 }, "dynamic_score": { "score": 86.6, "max_score": 100 } }, "release": { "path": "releases/automated-soap-note-generator/1.0.1.json", "sha256": "86d03aa7798222aeeea0c59829d10d58a1e0778dc12662303d21fe914f19500a" }, "artifact": { "path": "shards/96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b.zip", "sha256": "96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b", "bytes": 66876850, "skill_path": "automated-soap-note-generator" }, "content_sha256": "ce64d1f2d39882a0bec5a40b5ba88b32e724df1853a346d9e5311487456424d3" }, { "id": "baseline-extraction-for-clinical-trials", "version": "1.0.0", "display_name": "baseline-extraction-for-clinical-trials", "summary": "Extracts clinical trial baseline data (study, region, participants, etc.) from article text or PMID. Checks PubMed for metadata; always falls back to LLM extraction for full details.", "category": "Data Analysis", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Data Analysis/baseline-extraction-for-clinical-trials" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 87, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Data%20Analysis/baseline-extraction-for-clinical-trials/baseline-extraction-for-clinical-trials_audit_result_v1.json", "evaluated_on": "2026-03-23", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 81, "max_score": 100 }, "dynamic_score": { "score": 90.6, "max_score": 100 } }, "release": { "path": "releases/baseline-extraction-for-clinical-trials/1.0.0.json", "sha256": "9f37928e2b1640d447c782d637e23d6db94231d9687c173cc9d4ffcdf70d872a" }, "artifact": { "path": "shards/7c8f9f8124373f70427a2a59443dcbc72f77935f64151065ea3740842f3c00e3.zip", "sha256": "7c8f9f8124373f70427a2a59443dcbc72f77935f64151065ea3740842f3c00e3", "bytes": 53660, "skill_path": "baseline-extraction-for-clinical-trials" }, "content_sha256": "c5a7356db7c3ebce8723f44b16b962bfa4d6843fcfcc732fe57c058b6be7a4d2" }, { "id": "basic-discovery-translational-opportunity-finder", "version": "1.0.0", "display_name": "basic-discovery-translational-opportunity-finder", "summary": "Finds translational opportunities that connect basic-research discoveries to clinically meaningful use cases such as diagnosis, stratification, prognosis, treatment response prediction, monitoring, or therapeutic development. Use this skill when a user wants to turn a mechanism finding, pathway signal, cellular phenotype, experimental observation, or omics discovery into a stronger translational research direction. Always separate mechanistic relevance from translational usability, and never present a basic finding as clinically actionable unless the evidence supports that level.", "category": "Evidence Insight", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "awesome-med-research-skills/Evidence Insight/basic-discovery-translational-opportunity-finder" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 82, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/awesome-med-research-skills/Evidence%20Insight/basic-discovery-translational-opportunity-finder/eval_report_basic-discovery-translational-opportunity-finder_result.json", "evaluated_on": "2026-04-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 83, "max_score": 100 }, "dynamic_score": { "score": 81.7, "max_score": 100 } }, "release": { "path": "releases/basic-discovery-translational-opportunity-finder/1.0.0.json", "sha256": "2385a0c664a962042f451bc6914a30824bdc94f4c9a715f939018afec331a02d" }, "artifact": { "path": "shards/363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb.zip", "sha256": "363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb", "bytes": 1471244, "skill_path": "basic-discovery-translational-opportunity-finder" }, "content_sha256": "b8da71a22d185c60f9618d5745cacce221e17f389e0ac092e076489ef84040c8" }, { "id": "basic-research-design", "version": "1.0.0", "display_name": "basic-research-design", "summary": "A biomedical research topic designer that generates progressive experimental subtitles and detailed research outlines based on a given subject. Use when the user wants to design a research proposal, outline experiments for a topic, or structure a biomedical study.", "category": "Protocol Design", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d915031495e1c755d272c3de817ee3fc012e8b7b", "path": "scientific-skills/Protocol Design/basic-research-design" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 87, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d915031495e1c755d272c3de817ee3fc012e8b7b/scientific-skills/Protocol%20Design/basic-research-design/basic-research-design_audit_result_v2.json", "evaluated_on": "2026-03-23", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 85, "max_score": 100 }, "dynamic_score": { "score": 88.6, "max_score": 100 } }, "release": { "path": "releases/basic-research-design/1.0.0.json", "sha256": "3062c78531ce0b1faa7c39f0495c5ee39d1b0fbecf86cf12f60f3a4a657e7055" }, "artifact": { "path": "shards/de33edee35bb8ab7aef49234e84a13f7363966440d4d5f381ae005f9e13edd20.zip", "sha256": "de33edee35bb8ab7aef49234e84a13f7363966440d4d5f381ae005f9e13edd20", "bytes": 166464, "skill_path": "basic-research-design" }, "content_sha256": "cef22ffbf901b1193c74aefb103d93abb2acf8ca98a1e591594ebf36f404ee44" }, { "id": "batch-effect-correction", "version": "1.0.1", "display_name": "batch-effect-correction", "summary": "Use when correcting batch effects in merged bulk expression matrices with sample-level batch metadata while preserving biological group structure and generating before-and-after QC plots. NOT for: single-cell integration, raw FASTQ processing, differential expression without batch labels, or datasets without biological groups.", "category": "Data Analysis", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "awesome-med-research-skills/Data Analysis/batch-effect-correction" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "license": "Unknown", "evaluation": { "kind": "upstream-self-assessment", "score": 89, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/awesome-med-research-skills/Data%20Analysis/batch-effect-correction/eval_report_batch-effect-correction_result.json", "evaluated_on": "2026-04-27", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 90, "max_score": 100 }, "dynamic_score": { "score": 88.2, "max_score": 100 } }, "release": { "path": "releases/batch-effect-correction/1.0.1.json", "sha256": "1e4e8bd91bb5d25f783f55ae549db4d2a760be901e71486f922f7d2716ef527c" }, "artifact": { "path": "shards/96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b.zip", "sha256": "96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b", "bytes": 66876850, "skill_path": "batch-effect-correction" }, "content_sha256": "f10aa3894c3b9e9c30c85c1fa5e80ee2a962c0433d6e3e23ae65f1cd8ddc2e9a" }, { "id": "benchling-integration", "version": "1.0.0", "display_name": "benchling-integration", "summary": "Integrate with the Benchling R&D platform when you need to programmatically manage registry entities, inventory, ELN entries, workflows, events, or data warehouse analytics via API/SDK.", "category": "Other", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Other/benchling-integration" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 85, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Other/benchling-integration/benchling-integration_audit_result_v2.json", "evaluated_on": "2026-03-23", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 84, "max_score": 100 }, "dynamic_score": { "score": 85.6, "max_score": 100 } }, "release": { "path": "releases/benchling-integration/1.0.0.json", "sha256": "f4868d77e71171f7a8c7f80534e1f9552ebebc30036fa53221c0c7cbd5ec8495" }, "artifact": { "path": "shards/fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0.zip", "sha256": "fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0", "bytes": 1383584, "skill_path": "benchling-integration" }, "content_sha256": "146fc4cdbc01694525d4955617fb5025dea8a75c1048bf9d632419c33760b3c8" }, { "id": "bianque", "version": "1.0.0", "display_name": "bianque", "summary": "Evidence-based medical knowledge and research mentor grounded in the Bian Que tradition. Covers clinical reasoning, diagnostic thinking (望闻问切), pharmacology, pathology, differential diagnosis, medical literature appraisal, and the philosophy of early intervention. Trigger whenever users ask about medicine, clinical science, drugs, disease mechanisms, diagnosis, lab interpretation, treatment comparison, or health sciences. Even without explicit research framing, trigger on any topic touching disease, therapeutics, or clinical decision-making. Part of the AIPOCH Medical Research Skill Hub.", "category": "Other", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "awesome-med-research-skills/Other/bianque" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 93, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/awesome-med-research-skills/Other/bianque/eval_report_bianque_result.json", "evaluated_on": "2026-04-15", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 93, "max_score": 100 }, "dynamic_score": { "score": 92.6, "max_score": 100 } }, "release": { "path": "releases/bianque/1.0.0.json", "sha256": "9079faf94bae424ff15b1588e1e44653e0def6e2839db5586975cdd6cbef645e" }, "artifact": { "path": "shards/1ea735cead62d521282de0bf8a672a95515d73332ea4fa7d5d2ccf72eed45995.zip", "sha256": "1ea735cead62d521282de0bf8a672a95515d73332ea4fa7d5d2ccf72eed45995", "bytes": 342914, "skill_path": "bianque" }, "content_sha256": "22e3efd8a9a0f61021aa588f3faebfc5b1421dfe4fcbc6f894b0fff52301f9fc" }, { "id": "bibliography", "version": "1.0.0", "display_name": "bibliography", "summary": "Classifies and organizes literature by theme, method, and conclusion; use when you need to batch-read a folder of PDF/MD/DOCX/TXT files and output a structured CSV for literature reviews and annotation management.", "category": "Other", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Other/bibliography" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 85, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Other/bibliography/bibliography_audit_result_v1.json", "evaluated_on": "2026-03-23", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 84, "max_score": 100 }, "dynamic_score": { "score": 85.6, "max_score": 100 } }, "release": { "path": "releases/bibliography/1.0.0.json", "sha256": "f643343c17392c793a5c98028a67610fc9032d5a5a929802b411b6f8fc0859e7" }, "artifact": { "path": "shards/1ea735cead62d521282de0bf8a672a95515d73332ea4fa7d5d2ccf72eed45995.zip", "sha256": "1ea735cead62d521282de0bf8a672a95515d73332ea4fa7d5d2ccf72eed45995", "bytes": 342914, "skill_path": "bibliography" }, "content_sha256": "6b2b82af66d1c181146d96f7b2398b511ec8de010931bc661787ea2b40623147" }, { "id": "bidirectional-multi-phenotype-mr-research-planner", "version": "1.0.0", "display_name": "bidirectional-multi-phenotype-mr-research-planner", "summary": "Generates complete bidirectional multi-phenotype Mendelian randomization research designs from a user-provided exposure family and outcome family. Always use this skill whenever a user wants to design, plan, or build a genome-wide causal-inference study based on publicly available GWAS summary statistics, especially when the article logic includes multiple exposures, multiple outcomes or subtypes, bidirectional MR, IV filtering, IVW as the main estimator, weighted median / MR-Egger / MR-PRESSO sensitivity analyses, leave-one-out testing, heterogeneity / pleiotropy checks, and multiple-testing control with FDR. Covers five study patterns (single-family bidirectional MR, multi-phenotype screening MR, subtype-resolved MR, phenome-style bidirectional causal map, mechanism-prioritized MR follow-up) and always outputs four workload configs (Lite / Standard / Advanced / Publication+) with recommended primary plan, step-by-step workflow, figure plan, validation strategy, minimal executable version...", "category": "Protocol Design", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "awesome-med-research-skills/Protocol Design/bidirectional-multi-phenotype-mr-research-planner" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 91, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/awesome-med-research-skills/Protocol%20Design/bidirectional-multi-phenotype-mr-research-planner/eval_report_bidirectional-multi-phenotype-mr-research-planner_result.json", "evaluated_on": "2026-04-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 94, "max_score": 100 }, "dynamic_score": { "score": 88.7, "max_score": 100 } }, "release": { "path": "releases/bidirectional-multi-phenotype-mr-research-planner/1.0.0.json", "sha256": "16cce7cc2c7ce5debb0ed5a75ed6183d79af70abab352e9ffb65fdf93391d0c7" }, "artifact": { "path": "shards/fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0.zip", "sha256": "fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0", "bytes": 1383584, "skill_path": "bidirectional-multi-phenotype-mr-research-planner" }, "content_sha256": "e1b5aadb482318697dd9c5d3740edf91247042bae9f97d9f86b94188080d50fe" }, { "id": "bio-causal-genomics-mediation-analysis", "version": "1.0.1", "display_name": "bio-causal-genomics-mediation-analysis", "summary": "Decompose genetic effects into direct and indirect paths through mediating variables using the mediation R package. Tests whether gene expression, methylation, or other molecular phenotypes mediate the effect of genetic variants on disease. Use when testing whether a molecular...", "category": "Data Analysis", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Data Analysis/bio-causal-genomics-mediation-analysis" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 86, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Data%20Analysis/bio-causal-genomics-mediation-analysis/eval_report_bio-causal-genomics-mediation-analysis_result.json", "evaluated_on": "2026-06-15", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 86, "max_score": 100 }, "dynamic_score": { "score": 86.6, "max_score": 100 } }, "release": { "path": "releases/bio-causal-genomics-mediation-analysis/1.0.1.json", "sha256": "263ccbe51a533f7bb54e259444fd0729aaf175d15bb8702f7becc341b05f144c" }, "artifact": { "path": "shards/96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b.zip", "sha256": "96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b", "bytes": 66876850, "skill_path": "bio-causal-genomics-mediation-analysis" }, "content_sha256": "2dcdfdf07e312aeca463dc0b82c107d0f260e0d357d1b7343ead9f9497282f6a" }, { "id": "bio-causal-genomics-pleiotropy-detection", "version": "1.0.1", "display_name": "bio-causal-genomics-pleiotropy-detection", "summary": "Detect and correct for horizontal pleiotropy in Mendelian randomization analyses using MR-PRESSO for outlier removal, MR-Egger regression for directional pleiotropy, and Steiger filtering for variant directionality. Use when validating MR results, detecting pleiotropic instrum...", "category": "Data Analysis", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Data Analysis/bio-causal-genomics-pleiotropy-detection" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 86, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Data%20Analysis/bio-causal-genomics-pleiotropy-detection/eval_report_bio-causal-genomics-pleiotropy-detection_result.json", "evaluated_on": "2026-06-15", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 86, "max_score": 100 }, "dynamic_score": { "score": 86.6, "max_score": 100 } }, "release": { "path": "releases/bio-causal-genomics-pleiotropy-detection/1.0.1.json", "sha256": "a6c886c2f96347061e9e658d9ad43c3a65827ced6aecbb289522f88155198408" }, "artifact": { "path": "shards/96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b.zip", "sha256": "96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b", "bytes": 66876850, "skill_path": "bio-causal-genomics-pleiotropy-detection" }, "content_sha256": "6815a34f20585a42640e2dcc3d274a8620a9e13575056182f5d0870b0f223c67" }, { "id": "biodbnet-api", "version": "1.0.0", "display_name": "biodbnet-api", "summary": "Access bioDBnet REST services for biological identifier conversion, pathway retrieval, and ortholog mapping. Use when you need to convert gene/protein IDs, find pathways, or retrieve biological annotations via bioDBnet.", "category": "Other", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Other/biodbnet-api" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 86, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Other/biodbnet-api/biodbnet-api_audit_result_v2.json", "evaluated_on": "2026-03-23", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 87, "max_score": 100 }, "dynamic_score": { "score": 86, "max_score": 100 } }, "release": { "path": "releases/biodbnet-api/1.0.0.json", "sha256": "f336e32ece643cf7b92959e8a3ab4adac31651ee2cc1edaf5ce196846ab717e4" }, "artifact": { "path": "shards/fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0.zip", "sha256": "fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0", "bytes": 1383584, "skill_path": "biodbnet-api" }, "content_sha256": "a969b00a99c7df7e662bc0b3af5604c3b38449d22cca945aeb37922d42cf5685" }, { "id": "biogrid-orcs", "version": "1.0.0", "display_name": "biogrid-orcs", "summary": "Accesses BioGRID ORCS CRISPR screen data (organisms, screens, scores). Invoke when user needs to search CRISPR screens, get vocabulary, or retrieve gene scores.", "category": "Evidence Insight", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Evidence Insight/biogrid-orcs" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 87, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Evidence%20Insight/biogrid-orcs/biogrid-orcs_audit_result_v2.json", "evaluated_on": "2026-03-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 88, "max_score": 100 }, "dynamic_score": { "score": 86.4, "max_score": 100 } }, "release": { "path": "releases/biogrid-orcs/1.0.0.json", "sha256": "d2e23a3cc2ca9bd4433dd1fc94757173bcaf12403085ce9c6a169323012a6208" }, "artifact": { "path": "shards/363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb.zip", "sha256": "363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb", "bytes": 1471244, "skill_path": "biogrid-orcs" }, "content_sha256": "406e5302c653d3a09a6a27585f0e33f6f471577aaf5d6466dbcd604248b9cb4b" }, { "id": "bioinformatics-translational-opportunity-finder", "version": "1.0.0", "display_name": "bioinformatics-translational-opportunity-finder", "summary": "Identifies translationally meaningful paths for bioinformatics findings by mapping omics or computational discoveries to diagnosis, stratification, prognosis, treatment-response, monitoring, or target-nomination use cases, while auditing bridge evidence, assayability, and validation burden. Use this skill when a user wants to know whether a bioinformatics finding can be framed as a stronger translational topic without overclaiming clinical relevance. Always separate statistical signal from translational value, and never imply clinical utility, targetability, or validation depth without explicit evidence support.", "category": "Evidence Insight", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "awesome-med-research-skills/Evidence Insight/bioinformatics-translational-opportunity-finder" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 83, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/awesome-med-research-skills/Evidence%20Insight/bioinformatics-translational-opportunity-finder/eval_report_bioinformatics-translational-opportunity-finder_result.json", "evaluated_on": "2026-04-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 86, "max_score": 100 }, "dynamic_score": { "score": 81.6, "max_score": 100 } }, "release": { "path": "releases/bioinformatics-translational-opportunity-finder/1.0.0.json", "sha256": "184b115c0e17d5d2e2d6141cc1f1f4d9106bad857f998c34ce63ef9950ac1c73" }, "artifact": { "path": "shards/363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb.zip", "sha256": "363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb", "bytes": 1471244, "skill_path": "bioinformatics-translational-opportunity-finder" }, "content_sha256": "03bb5e91d66f31861a3b112cc830426380741c99cb519aefd2ec5bd3e2e6d415" }, { "id": "biomarker-landscape-scanner", "version": "1.0.0", "display_name": "biomarker-landscape-scanner", "summary": "Scans the biomarker landscape of a disease area by biomarker type, clinical/research use case, evidence layer, validation status, and maturity level. Use this skill when a user wants a field-level biomarker evidence map rather than a generic literature summary. Always separate exploratory biomarkers from externally validated or clinically embedded biomarkers, and never imply clinical maturity without explicit evidence support.", "category": "Evidence Insight", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "awesome-med-research-skills/Evidence Insight/biomarker-landscape-scanner" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 82, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/awesome-med-research-skills/Evidence%20Insight/biomarker-landscape-scanner/eval_report_biomarker-landscape-scanner_result.json", "evaluated_on": "2026-04-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 85, "max_score": 100 }, "dynamic_score": { "score": 80.7, "max_score": 100 } }, "release": { "path": "releases/biomarker-landscape-scanner/1.0.0.json", "sha256": "f2ab69b7ae63f1c8f4720eca8a396c088bf0b9c5bb22e897205301685d361319" }, "artifact": { "path": "shards/363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb.zip", "sha256": "363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb", "bytes": 1471244, "skill_path": "biomarker-landscape-scanner" }, "content_sha256": "6fbb37b338826b0a0d3846b42e8f4ed85ac32d62a4cb022bde7f2ddce3adc9f9" }, { "id": "biomed-outline-generator", "version": "1.0.1", "display_name": "biomed-outline-generator", "summary": "Generates structured biomedical outlines for review articles, discussion sections, and thesis proposals. Use when a user provides biomedical keywords, results/discussion text, or a proposal title plus background and needs a directly usable academic writing scaffold.", "category": "Academic Writing", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Academic Writing/biomed-outline-generator" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 90, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Academic%20Writing/biomed-outline-generator/biomed-outline-generator_audit_result_v2.json", "evaluated_on": "2026-03-23", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 82, "max_score": 100 }, "dynamic_score": { "score": 95.6, "max_score": 100 } }, "release": { "path": "releases/biomed-outline-generator/1.0.1.json", "sha256": "7965e88a5966ec456bcf04e364574c9abe66abb0450264c5c5889516735fd41e" }, "artifact": { "path": "shards/96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b.zip", "sha256": "96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b", "bytes": 66876850, "skill_path": "biomed-outline-generator" }, "content_sha256": "960eafdd2fbd52bde0e85b2e4adebb6606016b8f95bb74311a6c22460f25399c" }, { "id": "biomedical-search-strategy-builder", "version": "1.0.0", "display_name": "biomedical-search-strategy-builder", "summary": "Builds professional search strategies for PubMed, Embase, Web of Science, and similar databases. Use when a user needs to construct a MeSH-based Boolean query, design a systematic review search, expand a concept with synonyms, apply study-type or date filters, or adapt a query across multiple databases. Also triggers when the user says \"help me search for papers on X\", \"build a search strategy\", \"what are the MeSH terms for\", or \"I need a systematic review search string\".", "category": "Evidence Insight", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "awesome-med-research-skills/Evidence Insight/biomedical-search-strategy-builder" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 85, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/awesome-med-research-skills/Evidence%20Insight/biomedical-search-strategy-builder/eval_report_biomedical-search-strategy-builder_result.json", "evaluated_on": "2026-04-23", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 84, "max_score": 100 }, "dynamic_score": { "score": 85.8, "max_score": 100 } }, "release": { "path": "releases/biomedical-search-strategy-builder/1.0.0.json", "sha256": "d25b2819411fcd8c824b538baa8342e25c539b602a80219ebff00830b98d3239" }, "artifact": { "path": "shards/363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb.zip", "sha256": "363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb", "bytes": 1471244, "skill_path": "biomedical-search-strategy-builder" }, "content_sha256": "50b68e42d2406c8f33ea598016c610e29adddd891cdf7bf8bdf10f73b21a7e38" }, { "id": "biopython", "version": "1.0.1", "display_name": "biopython", "summary": "A comprehensive toolbox for computational molecular biology; use it when you need programmatic sequence/structure parsing, batch bioinformatics pipelines, or automated NCBI/BLAST workflows.", "category": "Data Analysis", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Data Analysis/biopython" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 86, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Data%20Analysis/biopython/biopython_audit_result_v1.json", "evaluated_on": "2026-03-23", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 85, "max_score": 100 }, "dynamic_score": { "score": 86.6, "max_score": 100 } }, "release": { "path": "releases/biopython/1.0.1.json", "sha256": "67b971325d3e4a3724fe2e4f81d95a66b0c97f141d90e7ecf6f7360155a361ef" }, "artifact": { "path": "shards/96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b.zip", "sha256": "96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b", "bytes": 66876850, "skill_path": "biopython" }, "content_sha256": "52074238eff40a3168223a055a2a2a6df7b978bf02f3e2603d4620774ff08028" }, { "id": "biopython-advanced", "version": "1.0.0", "display_name": "biopython-advanced", "summary": "Advanced Biopython modules for motifs, population genetics, sequence utilities, restriction analysis, clustering, and GenomeDiagram visualization; use when you need extended bioinformatics analysis beyond basic sequence I/O and alignment.", "category": "Data Analysis", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Data Analysis/biopython-advanced" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 86, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Data%20Analysis/biopython-advanced/biopython-advanced_audit_result_v1.json", "evaluated_on": "2026-03-23", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 85, "max_score": 100 }, "dynamic_score": { "score": 86.6, "max_score": 100 } }, "release": { "path": "releases/biopython-advanced/1.0.0.json", "sha256": "c3e351ee4999d340670431dabcb29f67e7e04991cfebe574df6b1df4505eec4c" }, "artifact": { "path": "shards/1d2612a702320846b10cda57f9f37ce17f5e19186095f2fe96bfa6e44eb077e8.zip", "sha256": "1d2612a702320846b10cda57f9f37ce17f5e19186095f2fe96bfa6e44eb077e8", "bytes": 234009, "skill_path": "biopython-advanced" }, "content_sha256": "9818de2e083b5160258a0f5c31bc904b4e253e730e61d25fb1f6e1379ee5883d" }, { "id": "biopython-alignment", "version": "1.0.1", "display_name": "biopython-alignment", "summary": "Sequence alignment and alignment file processing with Biopython (Bio.Align/Bio.AlignIO), triggered when you need global/local pairwise alignment, MSA read/write/format conversion, or alignment statistics/filtering.", "category": "Data Analysis", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Data Analysis/biopython-alignment" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 88, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Data%20Analysis/biopython-alignment/biopython-alignment_audit_result_v1.json", "evaluated_on": "2026-03-23", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 83, "max_score": 100 }, "dynamic_score": { "score": 91.6, "max_score": 100 } }, "release": { "path": "releases/biopython-alignment/1.0.1.json", "sha256": "ecd502ed27d2b0dee40f6c635d260e83c614792c83249fec67487ad0faa82c1c" }, "artifact": { "path": "shards/96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b.zip", "sha256": "96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b", "bytes": 66876850, "skill_path": "biopython-alignment" }, "content_sha256": "59492aed3d6e606bb9dbbb12d159d66d62650e9038c83b2a0408dc6832106631" }, { "id": "biopython-entrez", "version": "1.0.0", "display_name": "biopython-entrez", "summary": "Use Bio.Entrez to access NCBI databases (e.g., PubMed/GenBank) for searching, fetching summaries, and downloading records when your workflow needs to call the NCBI E-utilities API over the network.", "category": "Evidence Insight", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Evidence Insight/biopython-entrez" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 86, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Evidence%20Insight/biopython-entrez/biopython-entrez_audit_result_v1.json", "evaluated_on": "2026-03-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 84, "max_score": 100 }, "dynamic_score": { "score": 87.6, "max_score": 100 } }, "release": { "path": "releases/biopython-entrez/1.0.0.json", "sha256": "b095a125d6a2d04d81138ee41632a3733270cdf7f54fee50d85566e17b01931e" }, "artifact": { "path": "shards/1d2612a702320846b10cda57f9f37ce17f5e19186095f2fe96bfa6e44eb077e8.zip", "sha256": "1d2612a702320846b10cda57f9f37ce17f5e19186095f2fe96bfa6e44eb077e8", "bytes": 234009, "skill_path": "biopython-entrez" }, "content_sha256": "f53d8ba8e18850290d82f221a1c76748d4fe20c17751c057df0aa545f1d73179" }, { "id": "biopython-phylo", "version": "1.0.0", "display_name": "biopython-phylo", "summary": "Use Bio.Phylo to read/write phylogenetic trees and perform visualization and statistics; 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use when you need to discover biology preprints by keywords/authors/date ranges and programmatically fetch their details.", "category": "Evidence Insight", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Evidence Insight/biorxiv-database" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 88, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Evidence%20Insight/biorxiv-database/biorxiv-database_audit_result_v2.json", "evaluated_on": "2026-03-23", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 88, "max_score": 100 }, "dynamic_score": { "score": 87.6, "max_score": 100 } }, "release": { "path": "releases/biorxiv-database/1.0.0.json", "sha256": "df98637de2a983b54289109d41f8889421220bd0fbbe16b68252254aa496d1d5" }, "artifact": { "path": "shards/363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb.zip", "sha256": "363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb", "bytes": 1471244, "skill_path": "biorxiv-database" }, "content_sha256": "dafdf0274100479134f6118a6831b19b26a07573ef06f58941341d5ed6a79047" }, { "id": "bioservices", "version": "1.0.1", "display_name": "bioservices", "summary": "Unified Python access to 40+ bioinformatics web services; use when you need to query multiple databases (e.g., UniProt/KEGG/ChEMBL/Reactome) with one consistent API in a single workflow, especially for cross-database analysis and identifier mapping.", "category": "Data Analysis", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Data Analysis/bioservices" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 87, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Data%20Analysis/bioservices/bioservices_audit_result_v1.json", "evaluated_on": "2026-03-23", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 87, "max_score": 100 }, "dynamic_score": { "score": 87.2, "max_score": 100 } }, "release": { "path": "releases/bioservices/1.0.1.json", "sha256": "adaa2400fd04ffbd0d1097774a8c1d9000b7cf24dd9641adf0662f8744d1c226" }, "artifact": { "path": "shards/96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b.zip", "sha256": "96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b", "bytes": 66876850, "skill_path": "bioservices" }, "content_sha256": "1fd57cdededb0e3c3251e6ff6988167ffc5b7516eb838dd6caa441ff55a33199" }, { "id": "biotech-pitch-deck-narrative", "version": "1.0.1", "display_name": "biotech-pitch-deck-narrative", "summary": "Use biotech-pitch-deck-narrative for academic writing workflows that need structured investor-facing storytelling, explicit assumptions, and clear output boundaries.", "category": "Academic Writing", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Academic Writing/biotech-pitch-deck-narrative" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 85, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Academic%20Writing/biotech-pitch-deck-narrative/biotech-pitch-deck-narrative_audit_result_v2.json", "evaluated_on": "2026-03-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 88, "max_score": 100 }, "dynamic_score": { "score": 83.6, "max_score": 100 } }, "release": { "path": "releases/biotech-pitch-deck-narrative/1.0.1.json", "sha256": "87570dd5a5afcb1549ad55ba4e3fb9ba2a7b63156e7ffad98034fc34fbba47a2" }, "artifact": { "path": "shards/96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b.zip", "sha256": "96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b", "bytes": 66876850, "skill_path": "biotech-pitch-deck-narrative" }, "content_sha256": "4dc469587f904cc5ceccbc8301ff99265659fd76d87c346b98a27e30209275dd" }, { "id": "blind-review-sanitizer", "version": "1.0.1", "display_name": "blind-review-sanitizer", "summary": "Use blind-review-sanitizer for academic writing workflows that need structured anonymization, explicit assumptions, and clear output boundaries for double-blind submission.", "category": "Academic Writing", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Academic Writing/blind-review-sanitizer" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 91, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Academic%20Writing/blind-review-sanitizer/eval_report_blind-review-sanitizer_result.json", "evaluated_on": "2026-06-15", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 98, "max_score": 100 }, "dynamic_score": { "score": 86.6, "max_score": 100 } }, "release": { "path": "releases/blind-review-sanitizer/1.0.1.json", "sha256": "78ece4a5e024d559d68472f86727eaae53a3b0004b7397b21aee9e6a094bd389" }, "artifact": { "path": "shards/96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b.zip", "sha256": "96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b", "bytes": 66876850, "skill_path": "blind-review-sanitizer" }, "content_sha256": "dd6d68af262e33eda545a002bebc583df2f57089636d79922b31739e9aa51631" }, { "id": "boltz2-nim", "version": "1.0.0", "display_name": "boltz2-nim", "summary": "Use Boltz2 NIM for biomolecular structure prediction and binding affinity. Invoke for Boltz2, protein structures, protein-ligand/DNA/RNA complexes, SMILES or CCD ligands, pIC50/IC50 affinity scoring, mmCIF output, hosted NVIDIA API calls, or local Docker deployment.\n", "category": "Data Analysis", "source": { "repository": "https://github.com/NVIDIA-BioNeMo/bionemo-agent-toolkit", "commit": "0e67a612e4045f007e38fa77adc8f3ebfc5616b6", "path": "skills/bionemo-agent-toolkit/skills/boltz2-nim" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "license": "Apache-2.0 AND CC-BY-4.0", "release": { "path": "releases/boltz2-nim/1.0.0.json", "sha256": "53ebfa2f15ec73f48f3c11cc122df6d7d242e927c1b1f2a00228fb258816d392" }, "artifact": { "path": "shards/d9eadedf27584c96257c396b3554a2f479383e134ebaf455716ea80dd4f8de94.zip", "sha256": "d9eadedf27584c96257c396b3554a2f479383e134ebaf455716ea80dd4f8de94", "bytes": 1161536, "skill_path": "boltz2-nim" }, "content_sha256": "c0b61cff4c93d75e339369fd030cae6e3c14b2bcd1ba307d451fa6f809c8260e" }, { "id": "brenda-database", "version": "1.0.0", "display_name": "brenda-database", "summary": "Programmatic access to the BRENDA enzyme database via the SOAP API; use when you need kinetic constants (Km, kcat, Vmax), reaction equations, enzyme properties (pH/temperature optima, stability), or enzyme discovery by EC/substrate/product.", "category": "Evidence Insight", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Evidence Insight/brenda-database" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 87, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Evidence%20Insight/brenda-database/brenda-database_audit_result_v2.json", "evaluated_on": "2026-03-23", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 88, "max_score": 100 }, "dynamic_score": { "score": 86, "max_score": 100 } }, "release": { "path": "releases/brenda-database/1.0.0.json", "sha256": "d06413fc14559ba73160eaece9df924479f11979aab50c75b7ebead18205b9ed" }, "artifact": { "path": "shards/363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb.zip", "sha256": "363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb", "bytes": 1471244, "skill_path": "brenda-database" }, "content_sha256": "e2db6da884ef18ee95d727a4329d96b94e957c6b1aeff8bd2c96119c623039a8" }, { "id": "buffer-calculator", "version": "1.0.0", "display_name": "buffer-calculator", "summary": "Calculate precise buffer recipes with accurate mass and volume measurements for molecular biology and biochemistry. Supports PBS, RIPA, and TAE with concentration scaling, stock solution preparation, pH adjustment guidance, and step-by-step protocols.", "category": "Other", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Other/buffer-calculator" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 87, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Other/buffer-calculator/buffer-calculator_audit_result_v2.json", "evaluated_on": "2026-03-19", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 86, "max_score": 100 }, "dynamic_score": { "score": 87.3, "max_score": 100 } }, "release": { "path": "releases/buffer-calculator/1.0.0.json", "sha256": "77f59fa0b93731328e2206be9cc348412d251be4f74eb050839f74c9b0d5aaf7" }, "artifact": { "path": "shards/1d2612a702320846b10cda57f9f37ce17f5e19186095f2fe96bfa6e44eb077e8.zip", "sha256": "1d2612a702320846b10cda57f9f37ce17f5e19186095f2fe96bfa6e44eb077e8", "bytes": 234009, "skill_path": "buffer-calculator" }, "content_sha256": "2bf5c122bce7e8e800399de39b8f984af956a42dcec1a48b7209ece396a00cfe" }, { "id": "bulk-omics-integrative-planner", "version": "1.0.0", "display_name": "bulk-omics-integrative-planner", "summary": "Designs complete integrated research plans for bulk transcriptomics, proteomics, metabolomics, and related omics from a user-provided biomedical direction. Always use this skill whenever a user wants to design, scope, or structure a bulk multi-omics or single-omics-plus-clinical study — including disease-focused, mechanism-focused, biomarker-focused, stratification-oriented, or translational projects. It should define the research question, choose the best-fit study pattern, recommend example datasets as reference candidates only, specify the core analysis modules and method choices, propose a validation ladder, and output four workload configurations (Lite / Standard / Advanced / Publication+). Never fabricate datasets, accession numbers, sample counts, metadata completeness, cohort availability, assay coverage, literature references, PMIDs, DOIs, or validation status. Always include the mandatory Dataset Disclaimer immediately before any workflow section that mentions datasets or public resources.", "category": "Protocol Design", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "awesome-med-research-skills/Protocol Design/bulk-omics-integrative-planner" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 90, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/awesome-med-research-skills/Protocol%20Design/bulk-omics-integrative-planner/eval_report_bulk-omics-integrative-planner_result.json", "evaluated_on": "2026-04-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 94, "max_score": 100 }, "dynamic_score": { "score": 88.1, "max_score": 100 } }, "release": { "path": "releases/bulk-omics-integrative-planner/1.0.0.json", "sha256": "1589e65ef6ab47601fd4ba57450cc8a9930e59ec784be7eb3c78f72df7116a14" }, "artifact": { "path": "shards/fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0.zip", "sha256": "fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0", "bytes": 1383584, "skill_path": "bulk-omics-integrative-planner" }, "content_sha256": "8d7735a30236c541946e02aad9f57d281f393acc28e0a256f3fcf1c75e42174f" }, { "id": "case-control-study-planner", "version": "1.0.0", "display_name": "case-control-study-planner", "summary": "Design a structured case-control study framework with explicit source population logic, control selection rules, matching decisions, exposure measurement planning, and bias-control checkpoints.", "category": "Protocol Design", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "awesome-med-research-skills/Protocol Design/case-control-study-planner" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 86, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/awesome-med-research-skills/Protocol%20Design/case-control-study-planner/eval_report_case-control-study-planner_result.json", "evaluated_on": "2026-04-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 87, "max_score": 100 }, "dynamic_score": { "score": 85, "max_score": 100 } }, "release": { "path": "releases/case-control-study-planner/1.0.0.json", "sha256": "2b1e55cf54f2a7ad6e7881ab8074a4e217bef3e7b69b13e49e1b0a3ccd8babda" }, "artifact": { "path": "shards/fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0.zip", "sha256": "fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0", "bytes": 1383584, "skill_path": "case-control-study-planner" }, "content_sha256": "ff5e75d866f5a722b2659b9a2f2a2fdf718e9d8c0f2d0d9f07a3ea098d1166ea" }, { "id": "cellosaurus-api", "version": "1.0.0", "display_name": "cellosaurus-api", "summary": "Access Cellosaurus database for cell line information and release data. Invoke when user asks to search cell lines, get cell line details by accession, or check database release info.", "category": "Evidence Insight", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Evidence Insight/cellosaurus-api" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 91, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Evidence%20Insight/cellosaurus-api/cellosaurus-api_audit_result_v2.json", "evaluated_on": "2026-03-23", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 83, "max_score": 100 }, "dynamic_score": { "score": 96, "max_score": 100 } }, "release": { "path": "releases/cellosaurus-api/1.0.0.json", "sha256": "76c78d8085dbaf0836fe0293048e688ee614ce4e7d3cfa05b46ad79c9269230a" }, "artifact": { "path": "shards/363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb.zip", "sha256": "363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb", "bytes": 1471244, "skill_path": "cellosaurus-api" }, "content_sha256": "51befee5662b093d4a5ae8194dbd8d1edd8d01c2a619d1996af28e83d842317a" }, { "id": "cellxgene-census", "version": "1.0.0", "display_name": "cellxgene-census", "summary": "Programmatically query the CZ CELLxGENE Census (61M+ cells) when you need cross-tissue, disease, or cell-type expression data for population-scale queries and reference atlas comparisons.", "category": "Evidence Insight", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Evidence Insight/cellxgene-census" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 86, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Evidence%20Insight/cellxgene-census/cellxgene-census_audit_result_v1.json", "evaluated_on": "2026-03-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 84, "max_score": 100 }, "dynamic_score": { "score": 87.6, "max_score": 100 } }, "release": { "path": "releases/cellxgene-census/1.0.0.json", "sha256": "4bea71e16e02d2803d3a4e0574d0cfa231769bc3d0e580c9414e7e91ccb48633" }, "artifact": { "path": "shards/363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb.zip", "sha256": "363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb", "bytes": 1471244, "skill_path": "cellxgene-census" }, "content_sha256": "5370ef75c2799125ffa436f7476d9354630460f7aa8a10f83d8bbf9cb1b8e88c" }, { "id": "cerna-analysis", "version": "1.0.1", "display_name": "cerna-analysis", "summary": "Use when building a ceRNA regulatory network from a key gene list by combining bundled miRNA-mRNA and miRNA-lncRNA database files, with flat-file CSV exports and PDF visualization in a single output directory. NOT for: differential expression, single-cell analysis, enrichment analysis, or workflows without a key gene list.", "category": "Data Analysis", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "awesome-med-research-skills/Data Analysis/cerna-analysis" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "license": "Unknown", "evaluation": { "kind": "upstream-self-assessment", "score": 87, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/awesome-med-research-skills/Data%20Analysis/cerna-analysis/eval_report_cerna-analysis_result.json", "evaluated_on": "2026-04-27", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 87, "max_score": 100 }, "dynamic_score": { "score": 86.7, "max_score": 100 } }, "release": { "path": "releases/cerna-analysis/1.0.1.json", "sha256": "487d2cea2948c182a0bca364bf20eee85a7b301b399b431c978eb25145aa9a31" }, "artifact": { "path": "shards/96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b.zip", "sha256": "96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b", "bytes": 66876850, "skill_path": "cerna-analysis" }, "content_sha256": "629eb88bd292820608a7714fba628d023a083929a523606193fe4a97ae93fe2d" }, { "id": "chart-style-unifier", "version": "1.0.0", "display_name": "chart-style-unifier", "summary": "Batch-unify typography (font family, size, italics) for Word table cells and embedded charts; use when you need consistent formatting across theses/reports without changing body text.", "category": "Other", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Other/chart-style-unifier" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 88, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Other/chart-style-unifier/chart-style-unifier_audit_result_v1.json", "evaluated_on": "2026-03-23", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 84, "max_score": 100 }, "dynamic_score": { "score": 90.6, "max_score": 100 } }, "release": { "path": "releases/chart-style-unifier/1.0.0.json", "sha256": "b6df244a2d334ebb7db033e048f3e78e023a6105eb1b036b3cb17c48ac49a8c4" }, "artifact": { "path": "shards/fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0.zip", "sha256": "fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0", "bytes": 1383584, "skill_path": "chart-style-unifier" }, "content_sha256": "76690928eda4da6fad0beb2e6fa660eed709d70f6c200bd79efbee43737c15b4" }, { "id": "chea-api", "version": "1.0.0", "display_name": "chea-api", "summary": "Access ChEA3 and Harmonizome ChEA data for transcription factor enrichment analysis and metadata retrieval. Use when the user needs to perform ChEA3 enrichment analysis on a gene set, get metadata about the ChEA dataset, or retrieve information about a specific transcription factor (attribute).", "category": "Evidence Insight", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Evidence Insight/chea-api" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 86, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Evidence%20Insight/chea-api/chea-api_audit_result_v2.json", "evaluated_on": "2026-03-23", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 83, "max_score": 100 }, "dynamic_score": { "score": 87.6, "max_score": 100 } }, "release": { "path": "releases/chea-api/1.0.0.json", "sha256": "3ddfa5429ff4cd989deecb89975d11a24c5312d340940553631b70ef576fd2d5" }, "artifact": { "path": "shards/363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb.zip", "sha256": "363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb", "bytes": 1471244, "skill_path": "chea-api" }, "content_sha256": "93cefacb2ba41f8a9257b95ffbee8e62c4b7bb687886b8344c3ece82be5dd9fe" }, { "id": "chembl-database", "version": "1.0.0", "display_name": "chembl-database", "summary": "Query the ChEMBL database for bioactive molecules, targets, bioactivities, and approved drugs; use this when you need to filter by physicochemical properties (e.g., MW, LogP), chemical structure (SMILES), or retrieve drug mechanism information.", "category": "Evidence Insight", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Evidence Insight/chembl-database" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 89, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Evidence%20Insight/chembl-database/chembl-database_audit_result_v1.json", "evaluated_on": "2026-03-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 83, "max_score": 100 }, "dynamic_score": { "score": 92.6, "max_score": 100 } }, "release": { "path": "releases/chembl-database/1.0.0.json", "sha256": "17ccc5c268ffa4e5288f49e3b6b66019d78e4cc359fbdd0a52117c22dd7f8c9b" }, "artifact": { "path": "shards/363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb.zip", "sha256": "363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb", "bytes": 1471244, "skill_path": "chembl-database" }, "content_sha256": "6fb47430bf53c39d27ff67069d04fef30d30afb7b1ae022ff2ffbeb682646f00" }, { "id": "chemical-storage-sorter", "version": "1.0.0", "display_name": "chemical-storage-sorter", "summary": "Sort laboratory chemicals into safe storage groups by hazard classification (acids, bases, oxidizers, flammables, toxics). Identifies incompatible pairs, generates storage plans with warnings, and supports OSHA/NFPA compliance for lab safety.", "category": "Other", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Other/chemical-storage-sorter" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 85, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Other/chemical-storage-sorter/chemical-storage-sorter_audit_result_v2.json", "evaluated_on": "2026-03-19", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 83, "max_score": 100 }, "dynamic_score": { "score": 86.3, "max_score": 100 } }, "release": { "path": "releases/chemical-storage-sorter/1.0.0.json", "sha256": "9551686dc56cc43924fd08ce6c9a1a3a548e4332e7bcb037e210a38b14e6d5da" }, "artifact": { "path": "shards/fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0.zip", "sha256": "fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0", "bytes": 1383584, "skill_path": "chemical-storage-sorter" }, "content_sha256": "5f81cc7490ebe72970f49e561ab6f6d08d0469058c67298ad15febb5175f9b84" }, { "id": "citation-management", "version": "1.0.0", "display_name": "citation-management", "summary": "Comprehensive citation management for academic research; use when you need to discover papers (Google Scholar/PubMed), extract/verify metadata (DOI/PMID/arXiv/URL), and produce validated, clean BibTeX for manuscripts.", "category": "Evidence Insight", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Evidence Insight/citation-management" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 90, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Evidence%20Insight/citation-management/eval_report_citation-management_result.json", "evaluated_on": "2026-06-15", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 96, "max_score": 100 }, "dynamic_score": { "score": 86.6, "max_score": 100 } }, "release": { "path": "releases/citation-management/1.0.0.json", "sha256": "fa3def5c4c9610201c0a1ff3802bba4d8cc1d719ba9198f72fda11ab2ff47083" }, "artifact": { "path": "shards/363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb.zip", "sha256": "363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb", "bytes": 1471244, "skill_path": "citation-management" }, "content_sha256": "cf86f76daa79df3dfb36354d747d6883dda7b2d3a554295a0044ae85e653cf87" }, { "id": "citation-network", "version": "1.0.0", "display_name": "citation-network", "summary": "Build and visualize a citation network from a source/target CSV to identify key papers, communities, and emerging hotspots; use when you have citation pairs and need fast literature review or trend analysis.", "category": "Evidence Insight", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "63c61d38c6c4bba5128f98f0b225aa44e3fe748d", "path": "scientific-skills/Evidence Insight/citation-network" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 90, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/63c61d38c6c4bba5128f98f0b225aa44e3fe748d/scientific-skills/Evidence%20Insight/citation-network/eval_report_citation-network_result.json", "evaluated_on": "2026-06-15", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 96, "max_score": 100 }, "dynamic_score": { "score": 86.6, "max_score": 100 } }, "release": { "path": "releases/citation-network/1.0.0.json", "sha256": "3485fff327fd8e6e5a91b6b0a7ebc31fd2034bbc1390201fb965eb0e070965db" }, "artifact": { "path": "shards/c7fbe00cdc768bf2001464149d72245d871d762a7fd6df72d6d22c79b240ac69.zip", "sha256": "c7fbe00cdc768bf2001464149d72245d871d762a7fd6df72d6d22c79b240ac69", "bytes": 23150, "skill_path": "citation-network" }, "content_sha256": "ad530e8a6b202c2206d41923074976ce1af5297e2d5bcb05207abe0b4179c5f2" }, { "id": "claim-strength-calibrator", "version": "1.0.1", "display_name": "claim-strength-calibrator", "summary": "Calibrates manuscript claim strength so wording matches the actual evidence level, study design, and validation status.", "category": "Academic Writing", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "awesome-med-research-skills/Academic Writing/claim-strength-calibrator" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 85, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/awesome-med-research-skills/Academic%20Writing/claim-strength-calibrator/eval_report_claim-strength-calibrator_result.json", "evaluated_on": "2026-04-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 90, "max_score": 100 }, "dynamic_score": { "score": 81.3, "max_score": 100 } }, "release": { "path": "releases/claim-strength-calibrator/1.0.1.json", "sha256": "ceed5c851660e7ee12b0f7e47f7af8c0b30e5f89c5b745a389a1cded023b045a" }, "artifact": { "path": "shards/96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b.zip", "sha256": "96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b", "bytes": 66876850, "skill_path": "claim-strength-calibrator" }, "content_sha256": "724d2b1bbdd4b75be6a19d7fa4c19d56053f0d438b7d625271da481e65b51b4d" }, { "id": "clinic-sample-size", "version": "1.0.0", "display_name": "clinic-sample-size", "summary": "Unified tool for calculating sample sizes for Diagnostic, Efficacy, Etiology, and Prognosis clinical studies. Supports various statistical methods (Sensitivity/Specificity, Log-rank, Chi-square, EPV, etc.).", "category": "Other", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Other/clinic-sample-size" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 90, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Other/clinic-sample-size/clinic-sample-size_audit_result_v2.json", "evaluated_on": "2026-03-23", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 83, "max_score": 100 }, "dynamic_score": { "score": 94.6, "max_score": 100 } }, "release": { "path": "releases/clinic-sample-size/1.0.0.json", "sha256": "05e4447ab97c5ed0b973fb88c257650fef8390af70fcd156724746cfee7f4331" }, "artifact": { "path": "shards/fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0.zip", "sha256": "fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0", "bytes": 1383584, "skill_path": "clinic-sample-size" }, "content_sha256": "e59e939a77ed32bee7c4c002613ce057bd7b10760e88ac7723139713fd6bb709" }, { "id": "clinical-cohort-protocol-designer", "version": "1.0.0", "display_name": "clinical-cohort-protocol-designer", "summary": "Designs retrospective or prospective clinical cohort study protocols for biomedical and clinical research. Always use this skill when the user needs a cohort-based study plan rather than a general study idea, evidence summary, or mechanistic experiment design. Focus on cohort appropriateness, enrollment logic, baseline time-zero definition, follow-up structure, endpoint definition, variable collection, confounding control, and a coherent primary statistical analysis line. Do not invent data availability, follow-up completeness, outcome ascertainment quality, sample size adequacy, or causal interpretability.", "category": "Protocol Design", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "awesome-med-research-skills/Protocol Design/clinical-cohort-protocol-designer" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 90, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/awesome-med-research-skills/Protocol%20Design/clinical-cohort-protocol-designer/eval_report_clinical-cohort-protocol-designer_result.json", "evaluated_on": "2026-04-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 93, "max_score": 100 }, "dynamic_score": { "score": 87.6, "max_score": 100 } }, "release": { "path": "releases/clinical-cohort-protocol-designer/1.0.0.json", "sha256": "086ac657044b801cb8c89a5a4a0fa755b3a15d35e29487c071a4c1d07200ce99" }, "artifact": { "path": "shards/fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0.zip", "sha256": "fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0", "bytes": 1383584, "skill_path": "clinical-cohort-protocol-designer" }, "content_sha256": "450e68dfb3b3e290d82fb2415094b65761040376284850c6cd31a47ce40e67dd" }, { "id": "clinical-data-cleaner", "version": "1.0.1", "display_name": "clinical-data-cleaner", "summary": "Use when cleaning clinical trial data, preparing data for FDA/EMA submission, standardizing SDTM datasets, handling missing values in clinical studies, detecting outliers in lab results, or converting raw CRF data to CDISC format. Cleans and standardizes clinical trial data fo...", "category": "Data Analysis", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Data Analysis/clinical-data-cleaner" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "Unknown", "evaluation": { "kind": "upstream-self-assessment", "score": 91, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Data%20Analysis/clinical-data-cleaner/eval_report_clinical-data-cleaner_result.json", "evaluated_on": "2026-06-15", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 97, "max_score": 100 }, "dynamic_score": { "score": 86.6, "max_score": 100 } }, "release": { "path": "releases/clinical-data-cleaner/1.0.1.json", "sha256": "87672c257c59269b2e6b1346fa4ac14f696cb26fd539ce36d690c352848c4084" }, "artifact": { "path": "shards/96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b.zip", "sha256": "96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b", "bytes": 66876850, "skill_path": "clinical-data-cleaner" }, "content_sha256": "b30112b81ad060d3db72aa47bbbd7db87fb0defcc7f9490294b44d9a790e4090" }, { "id": "clinical-decision-support", "version": "1.0.0", "display_name": "clinical-decision-support", "summary": "Prepare and validate research-only clinical decision-support evaluation, evidence-profile, cohort, survival, biomarker/model, privacy, and governance artifacts. Use for aggregate or synthetic research documentation and traceability—not patient care or live clinical operation.", "category": "Academic Writing", "source": { "repository": "https://github.com/K-Dense-AI/scientific-agent-skills", "commit": "36d8f13a1e754618794bf42f417884940077b4ae", "path": "skills/clinical-decision-support" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "K-Dense Inc." } ], "license": "MIT", "release": { "path": "releases/clinical-decision-support/1.0.0.json", "sha256": "57c6a5fb1f4706672c570c20dadd6a8fa728f043bf89d19eb4dae4a986833c1d" }, "artifact": { "path": "shards/b55fb597588b663d1d9dfe3695492a0b1a9b88845adbce48fe564656e55759c6.zip", "sha256": "b55fb597588b663d1d9dfe3695492a0b1a9b88845adbce48fe564656e55759c6", "bytes": 624089, "skill_path": "clinical-decision-support" }, "content_sha256": "ab1e084233f14bb69eded2c57337e1abecbca7f2d77296a441bc9a3e9c58d7d4" }, { "id": "clinical-diagnostic-reasoning", "version": "1.0.0", "display_name": "clinical-diagnostic-reasoning", "summary": "Identify and counteract cognitive biases in medical decision-making through systematic error analysis and contextual algorithm application. For diagnostic reasoning, treatment decisions, and", "category": "Other", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Other/clinical-diagnostic-reasoning" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 86, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Other/clinical-diagnostic-reasoning/eval_report_clinical-diagnostic-reasoning_result.json", "evaluated_on": "2026-06-15", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 86, "max_score": 100 }, "dynamic_score": { "score": 86.6, "max_score": 100 } }, "release": { "path": "releases/clinical-diagnostic-reasoning/1.0.0.json", "sha256": "95518bf52b57b30ed94ecba92934166d70785056a511427af8c4c9e9ae419a59" }, "artifact": { "path": "shards/fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0.zip", "sha256": "fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0", "bytes": 1383584, "skill_path": "clinical-diagnostic-reasoning" }, "content_sha256": "244dc313582c1ecd0bd5e6915b3070e34ac406853e534ee13cc1f7deaa3a4ac7" }, { "id": "clinical-question-clarifier", "version": "1.0.0", "display_name": "clinical-question-clarifier", "summary": "Clarifies a vague clinical or biomedical research idea into a structured, bounded, searchable, researchable, and testable question. Always use this skill whenever a user has an early-stage clinical or research thought, an over-broad topic, an ill-defined evidence question, or an unclear problem statement that must be translated into a question framing suitable for literature retrieval, evidence synthesis, gap analysis, study design, or downstream protocol planning. Never jump straight to answering the substantive medical question unless the user explicitly asks for that. Focus first on question framing, boundary setting, and downstream-ready formulation.", "category": "Evidence Insight", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "awesome-med-research-skills/Evidence Insight/clinical-question-clarifier" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 90, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/awesome-med-research-skills/Evidence%20Insight/clinical-question-clarifier/eval_report_clinical-question-clarifier_result.json", "evaluated_on": "2026-04-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 95, "max_score": 100 }, "dynamic_score": { "score": 86.4, "max_score": 100 } }, "release": { "path": "releases/clinical-question-clarifier/1.0.0.json", "sha256": "16821d9b822f43fe76276abbb0ddcea736d82c295a4ab4336ec637b3523dae6a" }, "artifact": { "path": "shards/363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb.zip", "sha256": "363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb", "bytes": 1471244, "skill_path": "clinical-question-clarifier" }, "content_sha256": "5447c59e7a2fe5622a6c8081a87cb0b5428a1b3ddc2ec8511c059c42e6d3ebaf" }, { "id": "clinical-reports", "version": "1.0.0", "display_name": "clinical-reports", "summary": "Create safety-bounded draft structures and run local deterministic checks for clinical case, diagnostic, trial, safety, and aggregate research reports. Use only with synthetic, de-identified, or aggregate inputs and verified source-fact manifests; every output requires qualified review.", "category": "Other", "source": { "repository": "https://github.com/K-Dense-AI/scientific-agent-skills", "commit": "36d8f13a1e754618794bf42f417884940077b4ae", "path": "skills/clinical-reports" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "K-Dense Inc." } ], "license": "MIT", "release": { "path": "releases/clinical-reports/1.0.0.json", "sha256": "abb080b0e386db7f22553e077f886b647568391bfe936303d1429f2b049eea2a" }, "artifact": { "path": "shards/b55fb597588b663d1d9dfe3695492a0b1a9b88845adbce48fe564656e55759c6.zip", "sha256": "b55fb597588b663d1d9dfe3695492a0b1a9b88845adbce48fe564656e55759c6", "bytes": 624089, "skill_path": "clinical-reports" }, "content_sha256": "2995a039d42e99bad69057e468f6fb810bb125036fa83798d3b2eba7d904e8c7" }, { "id": "clinical-study-info-extractor", "version": "1.0.0", "display_name": "clinical-study-info-extractor", "summary": "Batch extracts and verifies structured information (PMID, title, abstract, methodology, results, etc.) from clinical research literature using PMIDs. Use when the user wants to extract details from specific PMIDs.", "category": "Evidence Insight", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Evidence Insight/clinical-study-info-extractor" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 86, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Evidence%20Insight/clinical-study-info-extractor/clinical-study-info-extractor_audit_result_v1.json", "evaluated_on": "2026-03-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 77, "max_score": 100 }, "dynamic_score": { "score": 91.6, "max_score": 100 } }, "release": { "path": "releases/clinical-study-info-extractor/1.0.0.json", "sha256": "7e632f3a8b65b15ed64ecc7021d02814f0bfc0b42ac070c0aa5a384facc8a991" }, "artifact": { "path": "shards/363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb.zip", "sha256": "363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb", "bytes": 1471244, "skill_path": "clinical-study-info-extractor" }, "content_sha256": "63083257d8b4929fde7829bb7a6a9a2acb8223820a00883411c71cc323cbe5fa" }, { "id": "clinical-trial-protocol-skill", "version": "1.0.1", "display_name": "clinical-trial-protocol-skill", "summary": "Generate clinical trial protocols for medical devices or drugs. This skill should be used when users say \"Create a clinical trial protocol\", \"Generate protocol for [device/drug]\", \"Help me design a clinical study\", \"Research similar trials for [intervention]\", or when developi...", "category": "Protocol Design", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Protocol Design/clinical-trial-protocol-skill" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "Unknown", "evaluation": { "kind": "upstream-self-assessment", "score": 88, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Protocol%20Design/clinical-trial-protocol-skill/eval_report_clinical-trial-protocol-skill_result.json", "evaluated_on": "2026-06-15", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 91, "max_score": 100 }, "dynamic_score": { "score": 86.6, "max_score": 100 } }, "release": { "path": "releases/clinical-trial-protocol-skill/1.0.1.json", "sha256": "5e7da96fdc5a04b4dcb3fb22fcf4a8a30d1b7d0839567210061fb0b6a86e92e1" }, "artifact": { "path": "shards/96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b.zip", "sha256": "96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b", "bytes": 66876850, "skill_path": "clinical-trial-protocol-skill" }, "content_sha256": "0d06912d97b20975317984e6c01950d430cb3819fff0a6f088cc8a35ab03ff35" }, { "id": "clinicaltrials-database", "version": "1.0.0", "display_name": "clinicaltrials-database", "summary": "Query ClinicalTrials.gov via API v2. Search trials by condition, drug, location, status, or phase. Retrieve trial details by NCT ID, export data, for clinical research and patient matching.", "category": "Evidence Insight", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Evidence Insight/clinicaltrials-database" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 88, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Evidence%20Insight/clinicaltrials-database/eval_report_clinicaltrials-database_result.json", "evaluated_on": "2026-06-15", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 91, "max_score": 100 }, "dynamic_score": { "score": 86.6, "max_score": 100 } }, "release": { "path": "releases/clinicaltrials-database/1.0.0.json", "sha256": "93d1f718ae4e426bba2e37eb2b47ea889cb77ad32b9d40aa132b8d7303858c13" }, "artifact": { "path": "shards/363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb.zip", "sha256": "363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb", "bytes": 1471244, "skill_path": "clinicaltrials-database" }, "content_sha256": "472f1c370c618f6e36d51ecb32604345c8ddf36779e16af62526231d7875928a" }, { "id": "clinicaltrials-db", "version": "1.0.0", "display_name": "clinicaltrials-db", "summary": "Query the ClinicalTrials.gov API v2 to search for clinical trials, retrieve detailed study protocols, and analyze recruitment status; use when you need to find trials by condition/drug, export results, or verify study details by NCT ID.", "category": "Evidence Insight", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Evidence Insight/clinicaltrials-db" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 88, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Evidence%20Insight/clinicaltrials-db/clinicaltrials-db_audit_result_v1.json", "evaluated_on": "2026-03-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 82, "max_score": 100 }, "dynamic_score": { "score": 91.6, "max_score": 100 } }, "release": { "path": "releases/clinicaltrials-db/1.0.0.json", "sha256": "e7984d9e78acabcb7ad0f38d7ddbab584ca219384e7b6678ddd39d75b8f181fb" }, "artifact": { "path": "shards/363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb.zip", "sha256": "363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb", "bytes": 1471244, "skill_path": "clinicaltrials-db" }, "content_sha256": "b31d884805027b799aaece729da54667057e575c2a41da29158614d4584a708e" }, { "id": "clinicaltrials-gov-parser", "version": "1.0.0", "display_name": "clinicaltrials-gov-parser", "summary": "Monitor and summarize competitor clinical trial status changes from ClinicalTrials.gov.", "category": "Evidence Insight", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Evidence Insight/clinicaltrials-gov-parser" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 90, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Evidence%20Insight/clinicaltrials-gov-parser/eval_report_clinicaltrials-gov-parser_result.json", "evaluated_on": "2026-06-15", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 96, "max_score": 100 }, "dynamic_score": { "score": 86.6, "max_score": 100 } }, "release": { "path": "releases/clinicaltrials-gov-parser/1.0.0.json", "sha256": "a6b908cf3fef0f740415c851e22508569387cbc538e2fe25154ac72cdff2f7cf" }, "artifact": { "path": "shards/363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb.zip", "sha256": "363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb", "bytes": 1471244, "skill_path": "clinicaltrials-gov-parser" }, "content_sha256": "f6618a50f99eb45fe0510321bdff2fb6bcbf04d856d1c9ba07404460fbb676cf" }, { "id": "clinpgx-database", "version": "1.0.0", "display_name": "clinpgx-database", "summary": "Access ClinPGx pharmacogenomics data (successor to PharmGKB). Query gene-drug interactions, CPIC guidelines, allele functions, for precision medicine and genotype-guided dosing decisions.", "category": "Evidence Insight", "source": { "repository": "https://github.com/K-Dense-AI/scientific-agent-skills", "commit": "71add644263a56368f8680d68df504c6674dc1e5", "path": "scientific-skills/clinpgx-database" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "K-Dense Inc." } ], "license": "Unknown", "release": { "path": "releases/clinpgx-database/1.0.0.json", "sha256": "174a64c6c59cc89bf376523dbb51fced5a1657ea08e403fcf7b26fc8c0a11d12" }, "artifact": { "path": "shards/fcd3eaa3439234f967f6835adacfe5bd6e8cacef5cbe5da7a06ffa4dec4f4973.zip", "sha256": "fcd3eaa3439234f967f6835adacfe5bd6e8cacef5cbe5da7a06ffa4dec4f4973", "bytes": 110833, "skill_path": "clinpgx-database" }, "content_sha256": "37501ed3d48459d039cac290b77b1b6aa53e8da9344e1bedf8914de014493924" }, { "id": "clinvar-database", "version": "1.0.0", "display_name": "clinvar-database", "summary": "Utilities for querying the NCBI ClinVar database to retrieve variant records, clinical significance, and phenotype relationships; use when searching variants by gene/condition/significance, interpreting Pathogenic/Benign/VUS classifications, or annotating VCF files with ClinVar annotations.", "category": "Evidence Insight", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Evidence Insight/clinvar-database" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 92, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Evidence%20Insight/clinvar-database/clinvar-database_audit_result_v1.json", "evaluated_on": "2026-03-23", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 85, "max_score": 100 }, "dynamic_score": { "score": 96, "max_score": 100 } }, "release": { "path": "releases/clinvar-database/1.0.0.json", "sha256": "ffeaa278ccf4be6b8ee4107983cc3da779eb3478d2f8a4c802c37dc1fe3df8e0" }, "artifact": { "path": "shards/363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb.zip", "sha256": "363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb", "bytes": 1471244, "skill_path": "clinvar-database" }, "content_sha256": "1fac95d8513d1421e33791f97d06cac7fab80166f533f70ad8d9d1d461adf6c0" }, { "id": "co-tank-monitor", "version": "1.0.0", "display_name": "co-tank-monitor", "summary": "IoT monitoring simulation to predict CO2 tank depletion and prevent weekend gas outages in cell culture facilities. Monitors cylinder pressure, calculates consumption rates, provides early warnings, and supports automated scheduling via cron.", "category": "Other", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Other/co2-tank-monitor" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "release": { "path": "releases/co-tank-monitor/1.0.0.json", "sha256": "4073d3ae33fc257a7e0b5e3cecd8ecfe5b87ab0f5576a7bc60f00b4a59138f33" }, "artifact": { "path": "shards/fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0.zip", "sha256": "fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0", "bytes": 1383584, "skill_path": "co-tank-monitor" }, "content_sha256": "617c87e8d0c7229ff3cb9f7279ff3b543efb2480f824e07d3f40deed2e92b52b" }, { "id": "cobrapy", "version": "1.0.1", "display_name": "cobrapy", "summary": "Constraint-based reconstruction and analysis (COBRA) for metabolic models; use when you need to simulate growth/production, analyze flux ranges, or run knockout and medium studies from SBML/JSON/YAML models.", "category": "Data Analysis", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Data Analysis/cobrapy" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 86, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Data%20Analysis/cobrapy/cobrapy_audit_result_v2.json", "evaluated_on": "2026-03-23", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 85, "max_score": 100 }, "dynamic_score": { "score": 86.6, "max_score": 100 } }, "release": { "path": "releases/cobrapy/1.0.1.json", "sha256": "527412c0a7b54587c70123337267ecd89111879cc1d7c5e53bd210623d5f75b3" }, "artifact": { "path": "shards/96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b.zip", "sha256": "96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b", "bytes": 66876850, "skill_path": "cobrapy" }, "content_sha256": "dc8edf3ca19d82f8310f8062c01541a6bbd321fa98e2b8472268ef566128ad81" }, { "id": "cold-chain-risk-calculator", "version": "1.0.0", "display_name": "cold-chain-risk-calculator", "summary": "Calculate temperature excursion risks for cold chain transport. Assesses route risk, packaging suitability, and monitoring requirements for biological samples and pharmaceuticals requiring controlled-temperature shipping.", "category": "Other", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Other/cold-chain-risk-calculator" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 78, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Other/cold-chain-risk-calculator/cold-chain-risk-calculator_audit_result_v4.json", "evaluated_on": "2026-03-19", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 79, "max_score": 100 }, "dynamic_score": { "score": 76.7, "max_score": 100 } }, "release": { "path": "releases/cold-chain-risk-calculator/1.0.0.json", "sha256": "e89753f1a159aaa051cc7908b4bb6915ee01dc4eaf42142e8f9be9be39ca19e0" }, "artifact": { "path": "shards/fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0.zip", "sha256": "fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0", "bytes": 1383584, "skill_path": "cold-chain-risk-calculator" }, "content_sha256": "48404850b9a6796e0bc6c60581672b683c8f4c7d058c8d50145f8560ff4ce79b" }, { "id": "comorbidity-common-immune-biomarker-research-planner", "version": "1.0.0", "display_name": "comorbidity-common-immune-biomarker-research-planner", "summary": "Generates complete comorbidity-oriented shared-biomarker bioinformatics research designs from a user-provided disease pair and validation direction. Use when a study links two clinically related diseases through shared DEGs, enrichment, PPI hub genes, machine-learning feature selection, public diagnostic validation, gene-regulatory networks, immune infiltration, and optional downstream follow-up. Covers five study patterns (shared-DEG discovery, hub-gene prioritization, machine-learning biomarker selection, immune/regulatory interpretation, multi-layer validation) and always outputs Lite / Standard / Advanced / Publication+ with a recommended primary plan, stepwise workflow, figure plan, validation hierarchy, minimal executable version, publication upgrade path, and strictly verified literature retrieval.", "category": "Protocol Design", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "awesome-med-research-skills/Protocol Design/comorbidity-common-immune-biomarker-research-planner" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 89, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/awesome-med-research-skills/Protocol%20Design/comorbidity-common-immune-biomarker-research-planner/eval_report_comorbidity-common-immune-biomarker-research-planner_result.json", "evaluated_on": "2026-04-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 92, "max_score": 100 }, "dynamic_score": { "score": 87.7, "max_score": 100 } }, "release": { "path": "releases/comorbidity-common-immune-biomarker-research-planner/1.0.0.json", "sha256": "84b07bb772ed235e0d25db6bfe5f0273a5bcfc0950ba4b04f5490f0972e7a2b2" }, "artifact": { "path": "shards/fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0.zip", "sha256": "fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0", "bytes": 1383584, "skill_path": "comorbidity-common-immune-biomarker-research-planner" }, "content_sha256": "c52bf1390b168dac8fd616eb2ce006fc3cf13a1c5f35cbf0b18e3068c7c82910" }, { "id": "comparative-network-toxicology-shared-mechanism-reference-grounded", "version": "1.0.0", "display_name": "comparative-network-toxicology-shared-mechanism-reference-grounded", "summary": "Generates complete comparative network-toxicology research designs from a user-provided exposure pair, shared toxic phenotype, and validation direction. Use when a study centers on two related exposures under one outcome and needs target collection, shared-vs-specific target decomposition, enrichment, PPI hub prioritization, docking, optional transcriptomic cross-checks, and conservative mechanistic synthesis. Covers five study patterns and always outputs Lite / Standard / Advanced / Publication+ with a recommended primary plan, stepwise workflow, figure plan, validation hierarchy, minimal executable version, publication upgrade path, and strictly verified literature retrieval.", "category": "Protocol Design", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "awesome-med-research-skills/Protocol Design/comparative-network-toxicology-shared-mechanism-reference-grounded" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 89, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/awesome-med-research-skills/Protocol%20Design/comparative-network-toxicology-shared-mechanism-reference-grounded/eval_report_comparative-network-toxicology-shared-mechanism-reference-grounded_result.json", "evaluated_on": "2026-04-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 92, "max_score": 100 }, "dynamic_score": { "score": 87.7, "max_score": 100 } }, "release": { "path": "releases/comparative-network-toxicology-shared-mechanism-reference-grounded/1.0.0.json", "sha256": "c399a18a2c40854ec2088c6edb4f049537f5393c88ec1a28d3bb60b1503bdeea" }, "artifact": { "path": "shards/fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0.zip", "sha256": "fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0", "bytes": 1383584, "skill_path": "comparative-network-toxicology-shared-mechanism-reference-grounded" }, "content_sha256": "08277c4e8cb5cdb8ae8e20c752de9088291a2a6bed1e6b8b28eb664453ddb1a8" }, { "id": "comparison-table-gen", "version": "1.0.0", "display_name": "comparison-table-gen", "summary": "Auto-generates comparison tables for concepts, drugs, or study results.", "category": "Evidence Insight", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Evidence Insight/comparison-table-gen" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 85, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Evidence%20Insight/comparison-table-gen/comparison-table-gen_audit_result_v2.json", "evaluated_on": "2026-03-23", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 88, "max_score": 100 }, "dynamic_score": { "score": 83.6, "max_score": 100 } }, "release": { "path": "releases/comparison-table-gen/1.0.0.json", "sha256": "ae85010a97d77df040990674fc1b8b1d9f76576d4cd5cfcba9c8cc42e7dd1acf" }, "artifact": { "path": "shards/1ea735cead62d521282de0bf8a672a95515d73332ea4fa7d5d2ccf72eed45995.zip", "sha256": "1ea735cead62d521282de0bf8a672a95515d73332ea4fa7d5d2ccf72eed45995", "bytes": 342914, "skill_path": "comparison-table-gen" }, "content_sha256": "a1280fad65dbe5a35f4668cff2805847d26302282bfec1b1d6a25a136d98f5c8" }, { "id": "competitor-trial-monitor", "version": "1.0.0", "display_name": "competitor-trial-monitor", "summary": "Monitor competitor clinical trial progress and alert on market risks.", "category": "Evidence Insight", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Evidence Insight/competitor-trial-monitor" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 85, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Evidence%20Insight/competitor-trial-monitor/competitor-trial-monitor_audit_result_v2.json", "evaluated_on": "2026-03-23", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 88, "max_score": 100 }, "dynamic_score": { "score": 83.6, "max_score": 100 } }, "release": { "path": "releases/competitor-trial-monitor/1.0.0.json", "sha256": "dec965920585d9896b02085c6472609b21b4fb344e9b0a7a89905caea06dff53" }, "artifact": { "path": "shards/363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb.zip", "sha256": "363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb", "bytes": 1471244, "skill_path": "competitor-trial-monitor" }, "content_sha256": "05e3525e5c9f283e1084a6f27613db57cb062833c785a0cde39e5def9b1b3e43" }, { "id": "complexa-binder-design", "version": "1.0.0", "display_name": "complexa-binder-design", "summary": "Run a complete protein binder design campaign with NVIDIA Proteina-Complexa: resolve a target structure and hotspots from a name/sequence/PDB, co-design binder sequence+structure with reward-guided test-time search (best-of-n, beam search, FK steering, MCTS), select with the internal AF2 reward gate, then INDEPENDENTLY validate each binder by refolding the complex with Boltz2 (default) or OpenFold3 and rank on interface confidence, pLDDT, ipSAE, apo/holo stability, and hotspot contact. Use whenever the user wants de novo binders against a named target, sequence, or PDB, hotspot/epitope-targeted design, Proteina-Complexa / Complexa, or ranked validated binders from one request. Sibling of protein-binder-design (RFdiffusion + ProteinMPNN); this skill uses Proteina-Complexa.\n", "category": "Protocol Design", "source": { "repository": "https://github.com/NVIDIA-BioNeMo/bionemo-agent-toolkit", "commit": "0e67a612e4045f007e38fa77adc8f3ebfc5616b6", "path": "skills/bionemo-agent-toolkit/skills/complexa-binder-design" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "license": "Apache-2.0", "release": { "path": "releases/complexa-binder-design/1.0.0.json", "sha256": "8500c134f3dada0ccf8adca3851364fb0590c5cdcfc9bc317451bbdcb70a1512" }, "artifact": { "path": "shards/d9eadedf27584c96257c396b3554a2f479383e134ebaf455716ea80dd4f8de94.zip", "sha256": "d9eadedf27584c96257c396b3554a2f479383e134ebaf455716ea80dd4f8de94", "bytes": 1161536, "skill_path": "complexa-binder-design" }, "content_sha256": "d19e37f70655a80a421a4a1def7f73c428eb876c7e641228081c598f36b570f6" }, { "id": "complexa-target", "version": "1.0.1", "display_name": "complexa-target", "summary": "Use this skill whenever the user wants to add, register, edit, list, show, or validate a Proteina-Complexa design target for any pipeline — protein binder (default), ligand binder, or AME / enzyme scaffolding. Triggers include \"add a target\", \"define a new target for binder design\", \"register a hotspot\", \"set up a PDL1 binder target\", \"ligand binder pocket\", \"SMILES target\", \"AME task\", \"enzyme motif\", \"M0024_1nzy\", \"complexa target add\", \"complexa target show\", \"configure target X\", \"what targets are available\", \"where do hotspots live\", \"what does target_input mean\", \"chain-spec syntax\", \"binder length range\", or any question about `configs/targets/{,ligand_}targets_dict.yaml` and `configs/design_tasks/ame_dict_v2.yaml`. Also covers `complexa validate target`. This is the only skill that touches the three targets dict files.", "category": "Data Analysis", "source": { "repository": "https://github.com/NVIDIA-BioNeMo/bionemo-agent-toolkit", "commit": "0e67a612e4045f007e38fa77adc8f3ebfc5616b6", "path": "skills/bionemo-agent-toolkit/skills/complexa-target" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "license": "Unknown", "release": { "path": "releases/complexa-target/1.0.1.json", "sha256": "f272a86ff20a7d13134634baf9fb4431f60650a27541509c03643786f02af860" }, "artifact": { "path": "shards/96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b.zip", "sha256": "96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b", "bytes": 66876850, "skill_path": "complexa-target" }, "content_sha256": "3f668d79414e327b65e5171dd24179488f0436eeb1464b98e7ea6c78c169452b" }, { "id": "concept-explainer", "version": "1.0.0", "display_name": "concept-explainer", "summary": "Uses analogies to explain complex medical concepts in accessible terms.", "category": "Evidence Insight", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Evidence Insight/concept-explainer" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 85, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Evidence%20Insight/concept-explainer/concept-explainer_audit_result_v2.json", "evaluated_on": "2026-03-23", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 88, "max_score": 100 }, "dynamic_score": { "score": 83.6, "max_score": 100 } }, "release": { "path": "releases/concept-explainer/1.0.0.json", "sha256": "1038506257acc16678c57df35d2ee30312d711328ff905a3cf346a2fcf08b461" }, "artifact": { "path": "shards/363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb.zip", "sha256": "363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb", "bytes": 1471244, "skill_path": "concept-explainer" }, "content_sha256": "4afb0389672b4d112fb575df70a5ce63a4da59556af12fedd869e73792ca8a39" }, { "id": "conference-abstract-adaptor", "version": "1.0.1", "display_name": "conference-abstract-adaptor", "summary": "Adapt scientific abstracts to conference-specific word limits, section rules, and formatting constraints; use when reshaping an existing abstract for submission.", "category": "Academic Writing", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Academic Writing/conference-abstract-adaptor" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 91, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Academic%20Writing/conference-abstract-adaptor/eval_report_conference-abstract-adaptor_result.json", "evaluated_on": "2026-06-15", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 98, "max_score": 100 }, "dynamic_score": { "score": 86.6, "max_score": 100 } }, "release": { "path": "releases/conference-abstract-adaptor/1.0.1.json", "sha256": "21eda0c179e88070056ffe064f7322607a2c342da426f6ec2e75e1f882dda969" }, "artifact": { "path": "shards/96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b.zip", "sha256": "96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b", "bytes": 66876850, "skill_path": "conference-abstract-adaptor" }, "content_sha256": "331dd8b0aff7ad6a8890ce6a97ab4c6d2a27b08e296a265feef05233e04fdf28" }, { "id": "conference-abstract-writer", "version": "1.0.1", "display_name": "conference-abstract-writer", "summary": "Condenses a full study into conference-submission abstract format. Use when adapting a manuscript abstract or study summary to meet a specific conference's word limit, structured format (Background/Methods/Results/Conclusion), character limits, or required section headings. Also triggers on \"adapt my abstract for [conference]\", \"shorten my abstract to 250 words\", \"reformat for ASCO/ASGCT/SfN/AACR\", \"I need a conference abstract\", or \"cut my abstract to fit the word limit\".", "category": "Academic Writing", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "awesome-med-research-skills/Academic Writing/conference-abstract-writer" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 84, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/awesome-med-research-skills/Academic%20Writing/conference-abstract-writer/eval_report_conference-abstract-writer_result.json", "evaluated_on": "2026-04-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 88, "max_score": 100 }, "dynamic_score": { "score": 82.2, "max_score": 100 } }, "release": { "path": "releases/conference-abstract-writer/1.0.1.json", "sha256": "b9306ebb95c364b3f3154de705a056635466229a028be4f16964bf8004ef6f39" }, "artifact": { "path": "shards/96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b.zip", "sha256": "96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b", "bytes": 66876850, "skill_path": "conference-abstract-writer" }, "content_sha256": "9c70efd53df66004e50117b14d975f8c3fe64d23da5ec99fd27190fc6fee885b" }, { "id": "conference-poster-pitch", "version": "1.0.1", "display_name": "conference-poster-pitch", "summary": "Use conference poster pitch for academic writing workflows that need structured execution, explicit assumptions, and clear output boundaries.", "category": "Academic Writing", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Academic Writing/conference-poster-pitch" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 85, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Academic%20Writing/conference-poster-pitch/conference-poster-pitch_audit_result_v2.json", "evaluated_on": "2026-03-23", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 88, "max_score": 100 }, "dynamic_score": { "score": 83.6, "max_score": 100 } }, "release": { "path": "releases/conference-poster-pitch/1.0.1.json", "sha256": "22f62e369a671037678b2e6e93ae037311d1ec426f8392fc97457c1dfb867783" }, "artifact": { "path": "shards/96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b.zip", "sha256": "96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b", "bytes": 66876850, "skill_path": "conference-poster-pitch" }, "content_sha256": "ff8c9f782790bb59b25800a813dab7b130c24bcf31f61a33b793ded167c88dad" }, { "id": "conference-tweet-generator", "version": "1.0.1", "display_name": "conference-tweet-generator", "summary": "Generates academic conference tweets and summaries by filtering abstracts, translating content, and creating engaging titles. Use when you need to process conference abstracts into social media content.", "category": "Other", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Other/conference-tweet-generator" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "Unknown", "evaluation": { "kind": "upstream-self-assessment", "score": 92, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Other/conference-tweet-generator/conference-tweet-generator_audit_result_v2.json", "evaluated_on": "2026-03-23", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 88, "max_score": 100 }, "dynamic_score": { "score": 94.6, "max_score": 100 } }, "release": { "path": "releases/conference-tweet-generator/1.0.1.json", "sha256": "11627560c96a3998508522cafb7ac15847d882ae20fe932a2cae2af7823df09a" }, "artifact": { "path": "shards/96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b.zip", "sha256": "96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b", "bytes": 66876850, "skill_path": "conference-tweet-generator" }, "content_sha256": "d08b7bbd10fc643a523118272675380bd9b0c179968fd3ea7d08978eb189f195" }, { "id": "conflict-of-interest-checker", "version": "1.0.0", "display_name": "conflict-of-interest-checker", "summary": "Check for co-authorship and institutional conflicts between authors and suggested reviewers to support peer review integrity. Coauthorship and institutional conflict detection supported.", "category": "Other", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Other/conflict-of-interest-checker" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 88, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Other/conflict-of-interest-checker/conflict-of-interest-checker_audit_result_v4.json", "evaluated_on": "2026-03-19", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 87, "max_score": 100 }, "dynamic_score": { "score": 88.7, "max_score": 100 } }, "release": { "path": "releases/conflict-of-interest-checker/1.0.0.json", "sha256": "a92a6a073e03c42a98db8d66a74a49b6563a259007546e25fe2e539834cdf22e" }, "artifact": { "path": "shards/fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0.zip", "sha256": "fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0", "bytes": 1383584, "skill_path": "conflict-of-interest-checker" }, "content_sha256": "7c14412b5bf9db1e9741ca87dce585ea03b84c271565f81b45f392645a5acd41" }, { "id": "confounder-and-bias-control-planner", "version": "1.0.0", "display_name": "confounder-and-bias-control-planner", "summary": "Plans confounder control, variable adjustment logic, and bias mitigation strategies at the protocol stage for clinical, epidemiologic, translational, observational, and biomarker studies. Always use this skill when a user needs to identify major confounders, decide which variables should or should not be adjusted for, compare matching/stratification/weighting approaches, anticipate selection or measurement bias, or pressure-test a study design before execution. Focus on bias sensing, causal structure awareness, variable-role classification, and critical design review rather than generic statistical advice.", "category": "Protocol Design", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "awesome-med-research-skills/Protocol Design/confounder-and-bias-control-planner" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 89, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/awesome-med-research-skills/Protocol%20Design/confounder-and-bias-control-planner/eval_report_confounder-and-bias-control-planner_result.json", "evaluated_on": "2026-04-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 93, "max_score": 100 }, "dynamic_score": { "score": 87, "max_score": 100 } }, "release": { "path": "releases/confounder-and-bias-control-planner/1.0.0.json", "sha256": "f9be8047752c7ae913b4988333f8cefa6a7e34ec06b59196190a645db4ff73fe" }, "artifact": { "path": "shards/fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0.zip", "sha256": "fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0", "bytes": 1383584, "skill_path": "confounder-and-bias-control-planner" }, "content_sha256": "3b3b4f8f1c253de3020b5f510186c04d9b8f06b47f1f044d69404bdc1285e57a" }, { "id": "consistency-checker-across-manuscript", "version": "1.0.1", "display_name": "consistency-checker-across-manuscript", "summary": "Checks consistency across title, abstract, methods, results, figures, tables, and supplements to identify internal contradictions and version drift in biomedical manuscripts.", "category": "Academic Writing", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "awesome-med-research-skills/Academic Writing/consistency-checker-across-manuscript" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 85, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/awesome-med-research-skills/Academic%20Writing/consistency-checker-across-manuscript/eval_report_consistency-checker-across-manuscript_result.json", "evaluated_on": "2026-04-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 90, "max_score": 100 }, "dynamic_score": { "score": 82, "max_score": 100 } }, "release": { "path": "releases/consistency-checker-across-manuscript/1.0.1.json", "sha256": "ddf40f5dc56000bdfac12cc0d404c67ecb0e90e743ddb0c6570263b0598a0bb9" }, "artifact": { "path": "shards/96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b.zip", "sha256": "96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b", "bytes": 66876850, "skill_path": "consistency-checker-across-manuscript" }, "content_sha256": "ea3021dbc7018a6b249171fd5f7fff1355a1724079a2ed07560db5c36e0890d3" }, { "id": "content-proofreading", "version": "1.0.0", "display_name": "content-proofreading", "summary": "An academic proofreading skill for Chinese/English manuscripts, triggered when you need automated checks for spelling, grammar, terminology consistency, and formatting before submission.", "category": "Other", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Other/content-proofreading" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 89, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Other/content-proofreading/eval_report_content-proofreading_result.json", "evaluated_on": "2026-06-15", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 93, "max_score": 100 }, "dynamic_score": { "score": 86.6, "max_score": 100 } }, "release": { "path": "releases/content-proofreading/1.0.0.json", "sha256": "ee206e94c5ffd4e564daaa029009f9c2086aaa99e849b024676fc7ce9987c92c" }, "artifact": { "path": "shards/1ea735cead62d521282de0bf8a672a95515d73332ea4fa7d5d2ccf72eed45995.zip", "sha256": "1ea735cead62d521282de0bf8a672a95515d73332ea4fa7d5d2ccf72eed45995", "bytes": 342914, "skill_path": "content-proofreading" }, "content_sha256": "c497465fc2fc9d6920ef9bfa97a94b2d46a6d9ab5f2cd44e3d5aa400188150c1" }, { "id": "contradictory-findings-resolver", "version": "1.0.0", "display_name": "contradictory-findings-resolver", "summary": "Explains why studies on the same biomedical topic reach different or opposing conclusions by auditing differences in population, endpoint definition, sample source, assay or platform, study design, statistical model, adjustment strategy, validation chain, and bias control. It separates true contradiction from apparent contradiction caused by framing or methods. Never fabricate references, PMIDs, DOIs, trial identifiers, dataset details, platform details, study features, or conflict explanations that are not supported by the input.", "category": "Evidence Insight", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "awesome-med-research-skills/Evidence Insight/contradictory-findings-resolver" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 86, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/awesome-med-research-skills/Evidence%20Insight/contradictory-findings-resolver/eval_report_contradictory-findings-resolver_result.json", "evaluated_on": "2026-04-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 89, "max_score": 100 }, "dynamic_score": { "score": 83.6, "max_score": 100 } }, "release": { "path": "releases/contradictory-findings-resolver/1.0.0.json", "sha256": "1563890d59337e7558518a0fc340597ab6df2d33d06fbbb78fb8137cddaa9631" }, "artifact": { "path": "shards/363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb.zip", "sha256": "363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb", "bytes": 1471244, "skill_path": "contradictory-findings-resolver" }, "content_sha256": "f0f64d4f1f0c5ce63f83094281e73df157447b972fb2f48619d3f67b3ae62849" }, { "id": "conventional-non-oncology-hub-gene-research-planner", "version": "1.0.0", "display_name": "conventional-non-oncology-hub-gene-research-planner", "summary": "Generates complete conventional non-oncology bioinformatics research designs from a user-provided disease context, process-related gene family or biological theme, and validation direction. Use when a study centers on multi-dataset bulk transcriptome integration, DEG analysis, process-gene intersection, enrichment analysis, GSEA, PPI hub-gene prioritization, TF/miRNA regulatory networks, ROC-based biomarker evaluation, and immune infiltration analysis. Covers five study patterns (process-DEG discovery, enrichment/GSEA interpretation, hub-gene prioritization, regulatory-network and immune interpretation, multi-layer public validation) and always outputs Lite / Standard / Advanced / Publication+ with a recommended primary plan, stepwise workflow, figure plan, validation hierarchy, minimal executable version, publication upgrade path, and strictly verified literature retrieval.", "category": "Protocol Design", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "awesome-med-research-skills/Protocol Design/conventional-non-oncology-hub-gene-research-planner" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 89, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/awesome-med-research-skills/Protocol%20Design/conventional-non-oncology-hub-gene-research-planner/eval_report_conventional-non-oncology-hub-gene-research-planner_result.json", "evaluated_on": "2026-04-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 92, "max_score": 100 }, "dynamic_score": { "score": 87.7, "max_score": 100 } }, "release": { "path": "releases/conventional-non-oncology-hub-gene-research-planner/1.0.0.json", "sha256": "1c6ef256eb99e4c510ea85cd04a19fdeddbaa29587a26609f782d319bbf5e679" }, "artifact": { "path": "shards/fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0.zip", "sha256": "fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0", "bytes": 1383584, "skill_path": "conventional-non-oncology-hub-gene-research-planner" }, "content_sha256": "8fe5d1182e3cb753d18c4a7dfc8147d97ec5d9dbe9b5b68e2bc6cc312708ab83" }, { "id": "conventional-oncology-hub-gene-research-planner", "version": "1.0.0", "display_name": "conventional-oncology-hub-gene-research-planner", "summary": "Generates complete conventional oncology bulk-transcriptome biomarker and hub-gene research designs from a user-provided cancer type and study direction. Always use this skill whenever a user wants to design, plan, or build a tumor bioinformatics study centered on differential expression, prognostic filtering or risk modeling, PPI-based hub-gene prioritization, diagnostic/prognostic evaluation, clinical association, immune infiltration context, methylation context, and optional tissue or cell validation. Covers five study patterns (signature-first prognostic workflow, hub-gene-first biomarker workflow, hybrid signature-to-hub workflow, immune-context biomarker workflow, translational validation workflow) and always outputs four workload configs (Lite / Standard / Advanced / Publication+) with recommended primary plan, step-by-step workflow, figure plan, validation strategy, minimal executable version, publication upgrade path...", "category": "Protocol Design", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "awesome-med-research-skills/Protocol Design/conventional-oncology-hub-gene-research-planner" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 90, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/awesome-med-research-skills/Protocol%20Design/conventional-oncology-hub-gene-research-planner/eval_report_conventional-oncology-hub-gene-research-planner_result.json", "evaluated_on": "2026-04-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 93, "max_score": 100 }, "dynamic_score": { "score": 88.7, "max_score": 100 } }, "release": { "path": "releases/conventional-oncology-hub-gene-research-planner/1.0.0.json", "sha256": "63e8d4acfb48049b1bb335c01530750f52708a0a41ba519ed91f910ebc1f59f5" }, "artifact": { "path": "shards/fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0.zip", "sha256": "fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0", "bytes": 1383584, "skill_path": "conventional-oncology-hub-gene-research-planner" }, "content_sha256": "4235e9aa32ad8dba444cea7a53da32abf79a54838c5639c2c4103d941efc443b" }, { "id": "cosmic-database", "version": "1.0.0", "display_name": "cosmic-database", "summary": "Access COSMIC to download mutation datasets, query Cancer Gene Census, and retrieve mutational signatures when your genomic analysis requires curated somatic mutation resources.", "category": "Evidence Insight", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Evidence Insight/cosmic-database" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 87, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Evidence%20Insight/cosmic-database/cosmic-database_audit_result_v1.json", "evaluated_on": "2026-03-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 81, "max_score": 100 }, "dynamic_score": { "score": 91.6, "max_score": 100 } }, "release": { "path": "releases/cosmic-database/1.0.0.json", "sha256": "01746c66cd4f099783e2540a344a40263a130f31005edfc6c9f863268dcb3d66" }, "artifact": { "path": "shards/363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb.zip", "sha256": "363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb", "bytes": 1471244, "skill_path": "cosmic-database" }, "content_sha256": "5d2d4657183c3faf1b4eda888797975eef1c00edd035dbfa6c962c74335e1c95" }, { "id": "cover-letter-drafter", "version": "1.0.1", "display_name": "cover-letter-drafter", "summary": "Drafts journal-ready cover letters for manuscript submission. Use when preparing a submission package, communicating the manuscript's contributions and journal fit to editors, or tailoring the novelty framing for a specific journal's scope. Also triggers on \"write a cover letter for my paper\", \"draft a submission cover letter\", \"help me write to the editor\", or \"cover letter for [journal name]\".", "category": "Academic Writing", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "awesome-med-research-skills/Academic Writing/cover-letter-drafter" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 86, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/awesome-med-research-skills/Academic%20Writing/cover-letter-drafter/eval_report_cover-letter-drafter_result.json", "evaluated_on": "2026-04-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 91, "max_score": 100 }, "dynamic_score": { "score": 83, "max_score": 100 } }, "release": { "path": "releases/cover-letter-drafter/1.0.1.json", "sha256": "ffcf9a71c2eada478a43ecd62a0c547a14ec00657c923df59992b10068f3a5de" }, "artifact": { "path": "shards/96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b.zip", "sha256": "96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b", "bytes": 66876850, "skill_path": "cover-letter-drafter" }, "content_sha256": "5c24ea1f558c4632caf4f7041ee7ed1784ed6bb65feae51d7beecf3a4fc37788" }, { "id": "cover-letter-generator", "version": "1.0.0", "display_name": "cover-letter-generator", "summary": "Generates a journal-ready cover letter from manuscript metadata, highlights, and journal-fit notes. Use when preparing an academic submission package and you need editor-facing language that clearly states novelty, relevance, declarations, and corresponding-author details.", "category": "Academic Writing", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d915031495e1c755d272c3de817ee3fc012e8b7b", "path": "scientific-skills/Academic Writing/cover-letter-generator" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 87, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d915031495e1c755d272c3de817ee3fc012e8b7b/scientific-skills/Academic%20Writing/cover-letter-generator/cover-letter-generator_audit_result_v1.json", "evaluated_on": "2026-03-23", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 85, "max_score": 100 }, "dynamic_score": { "score": 87.6, "max_score": 100 } }, "release": { "path": "releases/cover-letter-generator/1.0.0.json", "sha256": "e16d18291ff1175e413a8e464b89b17a548542c89996d3b802ec9b4499b38712" }, "artifact": { "path": "shards/de33edee35bb8ab7aef49234e84a13f7363966440d4d5f381ae005f9e13edd20.zip", "sha256": "de33edee35bb8ab7aef49234e84a13f7363966440d4d5f381ae005f9e13edd20", "bytes": 166464, "skill_path": "cover-letter-generator" }, "content_sha256": "1f200ad9960ee54df9a2cf36422bf13dd0e81043d38fa8ea2f4aba781a67c736" }, { "id": "cross-disease-shared-biomarker-network-research-planner", "version": "1.0.0", "display_name": "cross-disease-shared-biomarker-network-research-planner", "summary": "Generates complete cross-disease shared-biomarker bioinformatics research designs from a user-provided disease pair and validation direction. Always use this skill whenever a user wants to design, plan, or build a multi-dataset study linking two related diseases through shared DEGs, enrichment, PPI hub genes, public validation, regulatory-network analysis, immune infiltration, drug-gene interaction screening, and optional qRT-PCR or cell-line validation. Covers five study patterns (shared-DEG discovery, hub-gene prioritization, regulatory-network interpretation, immune/drug follow-up, bioinformatics-plus-validation) and always outputs four workload configs (Lite / Standard / Advanced / Publication+) with recommended primary plan, step-by-step workflow, figure plan, validation strategy, minimal executable version, publication upgrade path, and a strictly verified reference literature retrieval layer with real references only.", "category": "Protocol Design", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "awesome-med-research-skills/Protocol Design/cross-disease-shared-biomarker-network-research-planner" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 90, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/awesome-med-research-skills/Protocol%20Design/cross-disease-shared-biomarker-network-research-planner/eval_report_cross-disease-shared-biomarker-network-research-planner_result.json", "evaluated_on": "2026-04-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 93, "max_score": 100 }, "dynamic_score": { "score": 87.7, "max_score": 100 } }, "release": { "path": "releases/cross-disease-shared-biomarker-network-research-planner/1.0.0.json", "sha256": "a9f467584f1ac0b43b02b887c8db2eab26874cc3b64377fee5936a69b1fcc342" }, "artifact": { "path": "shards/fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0.zip", "sha256": "fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0", "bytes": 1383584, "skill_path": "cross-disease-shared-biomarker-network-research-planner" }, "content_sha256": "b6cfc6a94a59708c8a926a2b0057b1f68c42da85505e7c61ab893d31a1b32140" }, { "id": "crossref-database", "version": "1.0.0", "display_name": "crossref-database", "summary": "Access CrossRef metadata for scholarly works; use when you need to resolve a DOI or search CrossRef to retrieve bibliographic details, citation/reference counts, or funder information for research and citation management.", "category": "Evidence Insight", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Evidence Insight/crossref-database" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 91, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Evidence%20Insight/crossref-database/crossref-database_audit_result_v2.json", "evaluated_on": "2026-03-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 83, "max_score": 100 }, "dynamic_score": { "score": 96, "max_score": 100 } }, "release": { "path": "releases/crossref-database/1.0.0.json", "sha256": "de0b3de0cf9d777699b1b1022b5abcad55fec51a24ee2a3f3a90aa64b2055442" }, "artifact": { "path": "shards/363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb.zip", "sha256": "363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb", "bytes": 1471244, "skill_path": "crossref-database" }, "content_sha256": "5dfa004890ce1a7bc25cd9e995f40313cea90acc5d22d16da377e7294387c5c9" }, { "id": "ctd-api", "version": "1.0.0", "display_name": "ctd-api", "summary": "Accesses Comparative Toxicogenomics Database (CTD) for chemical, gene, disease, and pathway interaction data. Invoke when user needs to query CTD, retrieve toxicogenomics data, or investigate chemical-disease relationships.", "category": "Evidence Insight", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Evidence Insight/ctd-api" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 87, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Evidence%20Insight/ctd-api/ctd-api_audit_result_v2.json", "evaluated_on": "2026-03-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 88, "max_score": 100 }, "dynamic_score": { "score": 86.4, "max_score": 100 } }, "release": { "path": "releases/ctd-api/1.0.0.json", "sha256": "8f88269caf6e6025b295e30d86bd661cc372cd8c31144b506866da815f4368da" }, "artifact": { "path": "shards/363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb.zip", "sha256": "363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb", "bytes": 1471244, "skill_path": "ctd-api" }, "content_sha256": "45b8d4cef976c9c40f7fa5ae2d77dc3de82a825bed9b95c7012e40615508068d" }, { "id": "d-molecule-ray-tracer", "version": "1.0.1", "display_name": "d-molecule-ray-tracer", "summary": "Generate photorealistic rendering scripts for PyMOL and UCSF ChimeraX.", "category": "Data Analysis", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Data Analysis/3d-molecule-ray-tracer" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "release": { "path": "releases/d-molecule-ray-tracer/1.0.1.json", "sha256": "5e122d3677bd4265e9378c119b5a59f6b1f38b8e14dbf80ee9bb6e98627c359e" }, "artifact": { "path": "shards/96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b.zip", "sha256": "96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b", "bytes": 66876850, "skill_path": "d-molecule-ray-tracer" }, "content_sha256": "127bda0c031f32168f3c729a4c1bec34274ab5689078c15b31b65c2d5fbb28b2" }, { "id": "data-stats-analysis", "version": "1.0.1", "display_name": "data-stats-analysis", "summary": "Perform statistical tests, hypothesis testing, correlation analysis, and multiple testing corrections using scipy and statsmodels. Works with ANY LLM provider (GPT, Gemini, Claude, etc.).", "category": "Data Analysis", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Data Analysis/data-stats-analysis" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 86, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Data%20Analysis/data-stats-analysis/eval_report_data-stats-analysis_result.json", "evaluated_on": "2026-06-15", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 86, "max_score": 100 }, "dynamic_score": { "score": 86.6, "max_score": 100 } }, "release": { "path": "releases/data-stats-analysis/1.0.1.json", "sha256": "ca0250cc8eebc2b489a3c2edb04372c249f7a3b565f28eba7ce93dfc775497f3" }, "artifact": { "path": "shards/96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b.zip", "sha256": "96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b", "bytes": 66876850, "skill_path": "data-stats-analysis" }, "content_sha256": "3e9b47f3581de4833c52f9fb920a6a13ab2b13a071bded3e427771eb3d176dad" }, { "id": "data-transform", "version": "1.0.1", "display_name": "data-transform", "summary": "Transform, clean, reshape, and preprocess data using pandas and numpy. Works with ANY LLM provider (GPT, Gemini, Claude, etc.).", "category": "Data Analysis", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Data Analysis/data-transform" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 86, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Data%20Analysis/data-transform/eval_report_data-transform_result.json", "evaluated_on": "2026-06-15", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 86, "max_score": 100 }, "dynamic_score": { "score": 86.6, "max_score": 100 } }, "release": { "path": "releases/data-transform/1.0.1.json", "sha256": "de99bba8a8a9a3bd110e0cd264993bd2c18daef3f980a340e9a5a0ba039ce094" }, "artifact": { "path": "shards/96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b.zip", "sha256": "96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b", "bytes": 66876850, "skill_path": "data-transform" }, "content_sha256": "85ff550feb4a8eeab24d1ef145af725cf37513e953cd01d3afa79346de47f578" }, { "id": "datamol", "version": "1.0.0", "display_name": "datamol", "summary": "Pythonic wrapper around RDKit with simplified interface and sensible defaults. Preferred for standard drug discovery including SMILES parsing, standardization, descriptors, fingerprints, clustering, 3D conformers, parallel processing. Returns native rdkit.Chem.Mol objects. For advanced control or custom parameters, use rdkit directly.", "category": "Other", "source": { "repository": "https://github.com/K-Dense-AI/scientific-agent-skills", "commit": "36d8f13a1e754618794bf42f417884940077b4ae", "path": "skills/datamol" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "K-Dense Inc." } ], "license": "Apache-2.0 license", "release": { "path": "releases/datamol/1.0.0.json", "sha256": "82bf08b628b44a2243afdc4230d2ca5eb19fd7a14d8e76dd8b567758546de44e" }, "artifact": { "path": "shards/fbeb5f12afe3c0c6924b0974d44144de230c07662f05cd53d487f267a6a0eb70.zip", "sha256": "fbeb5f12afe3c0c6924b0974d44144de230c07662f05cd53d487f267a6a0eb70", "bytes": 244577, "skill_path": "datamol" }, "content_sha256": "b6cd9bb189f2d3f7ddf8b957fed17ffed61c21cb049aa03121dd0d2f45291094" }, { "id": "date-calculator", "version": "1.0.0", "display_name": "date-calculator", "summary": "Calculate medical date windows including gestational age, estimated delivery dates, and follow-up visit scheduling. Produces structured JSON output for clinical research and trial coordination workflows.", "category": "Other", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Other/date-calculator" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 87, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Other/date-calculator/date-calculator_audit_result_v2.json", "evaluated_on": "2026-03-19", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 90, "max_score": 100 }, "dynamic_score": { "score": 85.5, "max_score": 100 } }, "release": { "path": "releases/date-calculator/1.0.0.json", "sha256": "6e262144c91213871dfc04965068a96bc974a25fd38fa6498dd4d7038736de3f" }, "artifact": { "path": "shards/fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0.zip", "sha256": "fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0", "bytes": 1383584, "skill_path": "date-calculator" }, "content_sha256": "e9accb1eed97d8e3dbb8e7d6d6cf24004b33ca59bfe7362787c8f8694d4241a8" }, { "id": "decision-curve-analysis", "version": "1.0.1", "display_name": "decision-curve-analysis", "summary": "Use when evaluating the clinical utility of a binary prediction model from a single clinical CSV file by fitting a logistic decision-curve model, plotting decision and clinical-impact curves, and exporting summary outputs. NOT for: survival calibration, ROC-only discrimination analysis, nomogram construction, or time-to-event outcomes.", "category": "Data Analysis", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "awesome-med-research-skills/Data Analysis/decision-curve-analysis" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "license": "Unknown", "evaluation": { "kind": "upstream-self-assessment", "score": 87, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/awesome-med-research-skills/Data%20Analysis/decision-curve-analysis/eval_report_decision-curve-analysis_result.json", "evaluated_on": "2026-04-27", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 88, "max_score": 100 }, "dynamic_score": { "score": 86, "max_score": 100 } }, "release": { "path": "releases/decision-curve-analysis/1.0.1.json", "sha256": "0d62e00f7057a5845630193575201de61d890a32ab0e722c08020cb1c7bb6460" }, "artifact": { "path": "shards/96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b.zip", "sha256": "96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b", "bytes": 66876850, "skill_path": "decision-curve-analysis" }, "content_sha256": "b86df3b54ce3e64c9fa48b01cebc7ea2ec009aae11bf2fd6f1c9f3f38032bf8a" }, { "id": "decision-tree-analysis", "version": "1.0.1", "display_name": "decision-tree-analysis", "summary": "Use when building a decision tree model in R and generating feature importance ranking outputs. Supports classification and regression, automatic task detection, parameter validation, model evaluation summaries, and exports of feature-importance tables and figures.", "category": "Data Analysis", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "awesome-med-research-skills/Data Analysis/decision-tree-analysis" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "Unknown", "evaluation": { "kind": "upstream-self-assessment", "score": 88, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/awesome-med-research-skills/Data%20Analysis/decision-tree-analysis/eval_report_decision-tree-analysis_result.json", "evaluated_on": "2026-04-20", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 90, "max_score": 100 }, "dynamic_score": { "score": 85.8, "max_score": 100 } }, "release": { "path": "releases/decision-tree-analysis/1.0.1.json", "sha256": "d0ef3dcc511d70cb57f38491a40a39e6c493b8f59d693fb095934e790475c616" }, "artifact": { "path": "shards/96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b.zip", "sha256": "96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b", "bytes": 66876850, "skill_path": "decision-tree-analysis" }, "content_sha256": "dbf2f1cd1d7926585d6388351edba5a2a3d8d79f610d47f7f91ab2a897d63293" }, { "id": "deeptools", "version": "1.0.0", "display_name": "deeptools", "summary": "NGS analysis toolkit. BAM to bigWig conversion, QC (correlation, PCA, fingerprints), heatmaps/profiles (TSS, peaks), for ChIP-seq, RNA-seq, ATAC-seq visualization.", "category": "Data Analysis", "source": { "repository": "https://github.com/K-Dense-AI/scientific-agent-skills", "commit": "36d8f13a1e754618794bf42f417884940077b4ae", "path": "skills/deeptools" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "K-Dense Inc." } ], "license": "BSD license", "release": { "path": "releases/deeptools/1.0.0.json", "sha256": "09c73d67adf92acad5ce5f0c694e58fbb4b25b7c8acdada45d7adff41b2646a3" }, "artifact": { "path": "shards/fbeb5f12afe3c0c6924b0974d44144de230c07662f05cd53d487f267a6a0eb70.zip", "sha256": "fbeb5f12afe3c0c6924b0974d44144de230c07662f05cd53d487f267a6a0eb70", "bytes": 244577, "skill_path": "deeptools" }, "content_sha256": "95f3580e13c2b15e5ad6ff396603897cbad27b9d0a1b1d7fb61375df1bb74d46" }, { "id": "deg-screening-analysis", "version": "1.0.1", "display_name": "deg-screening-analysis", "summary": "Use when screening differentially expressed genes from a bulk expression matrix between two user-specified groups, producing DEG tables, a volcano plot, and a clustered heatmap. Triggers include DEG analysis, volcano plot, clustered heatmap, limma-based two-group comparison, and case-vs-control screening. NOT for single-cell RNA-seq, multi-group contrasts, count-model workflows such as DESeq2/edgeR, or non-expression omics data.", "category": "Data Analysis", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "awesome-med-research-skills/Data Analysis/deg-screening-analysis" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "Unknown", "evaluation": { "kind": "upstream-self-assessment", "score": 90, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/awesome-med-research-skills/Data%20Analysis/deg-screening-analysis/eval_report_deg-screening-analysis_result.json", "evaluated_on": "2026-04-16", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 92, "max_score": 100 }, "dynamic_score": { "score": 89.4, "max_score": 100 } }, "release": { "path": "releases/deg-screening-analysis/1.0.1.json", "sha256": "191a3f94e61471eb06f1e2ba32446c874a07933f956408ea1e6e2c4d1161178c" }, "artifact": { "path": "shards/96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b.zip", "sha256": "96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b", "bytes": 66876850, "skill_path": "deg-screening-analysis" }, "content_sha256": "5a9b99e08989f21d3be766da96546f2391923b0750f52cfa999ba686bf944ae6" }, { "id": "dei-statement-drafter", "version": "1.0.1", "display_name": "dei-statement-drafter", "summary": "Draft Diversity, Equity, and Inclusion statements for academic applications.", "category": "Academic Writing", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Academic Writing/dei-statement-drafter" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 85, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Academic%20Writing/dei-statement-drafter/dei-statement-drafter_audit_result_v2.json", "evaluated_on": "2026-03-23", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 88, "max_score": 100 }, "dynamic_score": { "score": 83.6, "max_score": 100 } }, "release": { "path": "releases/dei-statement-drafter/1.0.1.json", "sha256": "e7ff158ed9e0bacfa57deb6002926c109f628edcdb966a92361bd288af3042d2" }, "artifact": { "path": "shards/96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b.zip", "sha256": "96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b", "bytes": 66876850, "skill_path": "dei-statement-drafter" }, "content_sha256": "ef66a4b29b32c3f645be0121aa93b0940927aa24ffb89e7028fbdca80c4cfa05" }, { "id": "diagnostic-study-quality-assessment-quadas", "version": "1.0.1", "display_name": "diagnostic-study-quality-assessment-quadas", "summary": "Analyzes clinical diagnostic accuracy studies for bias using the QUADAS-2 tool. Use when Claude needs to assess the quality, risk of bias, or applicability of diagnostic accuracy studies (e.g., \"Assess this paper using QUADAS-2\").", "category": "Data Analysis", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Data Analysis/diagnostic-study-quality-assessment-quadas-2" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "Unknown", "release": { "path": "releases/diagnostic-study-quality-assessment-quadas/1.0.1.json", "sha256": "790cb66de33353f6a98a72fef66ac714c6d6058a68a9fe6d98cc29c14f31a492" }, "artifact": { "path": "shards/96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b.zip", "sha256": "96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b", "bytes": 66876850, "skill_path": "diagnostic-study-quality-assessment-quadas" }, "content_sha256": "0da3ac77c1fb843a44d2289d0f240b3b9012865353810bb9380665e654928874" }, { "id": "diagnostic-study-quality-assessment-quadas-2", "version": "1.0.1", "display_name": "diagnostic-study-quality-assessment-quadas-2", "summary": "Analyzes clinical diagnostic accuracy studies for bias using the QUADAS-2 tool. Use when Claude needs to assess the quality, risk of bias, or applicability of diagnostic accuracy studies (e.g., \"Assess this paper using QUADAS-2\").", "category": "Evidence Insight", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Evidence Insight/diagnostic-study-quality-assessment-quadas-2" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "Unknown", "evaluation": { "kind": "upstream-self-assessment", "score": 91, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Evidence%20Insight/diagnostic-study-quality-assessment-quadas-2/eval_report_diagnostic-study-quality-assessment-quadas-2_result.json", "evaluated_on": "2026-06-15", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 98, "max_score": 100 }, "dynamic_score": { "score": 86.2, "max_score": 100 } }, "release": { "path": "releases/diagnostic-study-quality-assessment-quadas-2/1.0.1.json", "sha256": "711521e0c0796834c60bef443eb75b79ec9a1c92acb2455fa8a2a496171bd1a6" }, "artifact": { "path": "shards/96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b.zip", "sha256": "96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b", "bytes": 66876850, "skill_path": "diagnostic-study-quality-assessment-quadas-2" }, "content_sha256": "bd7257fd7f789fe43cc649f164baff770e798572ec8f9ab349a0f09df1108f76" }, { "id": "diffdock-nim", "version": "1.0.0", "display_name": "diffdock-nim", "summary": "Run DiffDock molecular docking via NVIDIA NIM to predict small-molecule binding poses against protein targets. Use for DiffDock, molecular docking, ligand docking, blind docking, SMILES or SDF ligands, ranked poses, confidence scores, hosted NVIDIA API, or local Docker deployment.\n", "category": "Data Analysis", "source": { "repository": "https://github.com/NVIDIA-BioNeMo/bionemo-agent-toolkit", "commit": "0e67a612e4045f007e38fa77adc8f3ebfc5616b6", "path": "skills/bionemo-agent-toolkit/skills/diffdock-nim" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "license": "Apache-2.0 AND CC-BY-4.0", "release": { "path": "releases/diffdock-nim/1.0.0.json", "sha256": "7350f4ab1f8afd1a3ceec3aa719e9e23f46287d46b9c69a1eecb76c0ab3d96da" }, "artifact": { "path": "shards/d9eadedf27584c96257c396b3554a2f479383e134ebaf455716ea80dd4f8de94.zip", "sha256": "d9eadedf27584c96257c396b3554a2f479383e134ebaf455716ea80dd4f8de94", "bytes": 1161536, "skill_path": "diffdock-nim" }, "content_sha256": "2a2a8a9520d5f510ef10a9b5d466ca863e6b1f2156e6acf0fa60226437a0558e" }, { "id": "differential-expression-analysis", "version": "1.0.1", "display_name": "differential-expression-analysis", "summary": "Use when analyzing bulk RNA-seq or microarray expression data to identify differentially expressed genes between two biological groups (case vs control), with volcano plots and heatmap visualization. NOT for:single-cell RNA-seq, methylation analysis, non-expression data.", "category": "Data Analysis", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "awesome-med-research-skills/Data Analysis/differential-expression-analysis" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "Unknown", "evaluation": { "kind": "upstream-self-assessment", "score": 90, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/awesome-med-research-skills/Data%20Analysis/differential-expression-analysis/eval_report_differential-expression-analysis_result.json", "evaluated_on": "2026-05-08", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 91, "max_score": 100 }, "dynamic_score": { "score": 90, "max_score": 100 } }, "release": { "path": "releases/differential-expression-analysis/1.0.1.json", "sha256": "c42b32029849d99612e37ecae16714f123d0e8a2da1d365dc3a39d06a76468ef" }, "artifact": { "path": "shards/96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b.zip", "sha256": "96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b", "bytes": 66876850, "skill_path": "differential-expression-analysis" }, "content_sha256": "61dc0014f383b9e07fe302493515226ca214744c37e5ec3dfaa85510e10313cd" }, { "id": "discussion-composer", "version": "1.0.1", "display_name": "discussion-composer", "summary": "Composes a Discussion around key findings, mechanisms, clinical relevance, and limitations. Use when writing or improving a Discussion section for any biomedical manuscript — including interpreting results, connecting to prior literature, addressing unexpected findings, framing limitations, and writing the conclusion. Also triggers on \"write my discussion\", \"help me discuss my findings\", \"how do I compare to prior studies\", \"write the limitations paragraph\", or \"draft a discussion for my paper\".", "category": "Academic Writing", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "awesome-med-research-skills/Academic Writing/discussion-composer" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 83, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/awesome-med-research-skills/Academic%20Writing/discussion-composer/eval_report_discussion-composer_result.json", "evaluated_on": "2026-04-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 86, "max_score": 100 }, "dynamic_score": { "score": 81, "max_score": 100 } }, "release": { "path": "releases/discussion-composer/1.0.1.json", "sha256": "3b5494029983881284f92968832ee6cb5a51bb8fa896bf808052e84b83c69d1a" }, "artifact": { "path": "shards/96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b.zip", "sha256": "96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b", "bytes": 66876850, "skill_path": "discussion-composer" }, "content_sha256": "f9dff89d4c07256821b4e77cb61e2276fa1175a60f83458264998b52a2dc14c3" }, { "id": "discussion-section-architect", "version": "1.0.0", "display_name": "discussion-section-architect", "summary": "Structures and writes discussion sections for academic papers and research reports. Use when writing a discussion section, interpreting research results, connecting findings to existing literature, addressing study limitations, synthesizing conclusions, or drafting any part of...", "category": "Academic Writing", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Academic Writing/discussion-section-architect" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 91, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Academic%20Writing/discussion-section-architect/eval_report_discussion-section-architect_result.json", "evaluated_on": "2026-06-15", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 98, "max_score": 100 }, "dynamic_score": { "score": 86.6, "max_score": 100 } }, "release": { "path": "releases/discussion-section-architect/1.0.0.json", "sha256": "7fdbcff4e590617497371739f1c63649d7fb5038ffac051b1f1ea2e374b2b3a9" }, "artifact": { "path": "shards/c7fbe00cdc768bf2001464149d72245d871d762a7fd6df72d6d22c79b240ac69.zip", "sha256": "c7fbe00cdc768bf2001464149d72245d871d762a7fd6df72d6d22c79b240ac69", "bytes": 23150, "skill_path": "discussion-section-architect" }, "content_sha256": "6f5f18ea54a9d20d77bf9e24c02872922414bb6f7e46240bdf1eb44f11c11602" }, { "id": "disease-mechanism-evidence-map", "version": "1.0.0", "display_name": "disease-mechanism-evidence-map", "summary": "Systematically maps mechanism evidence for a disease from molecules to pathways, cell types, tissues, biological consequences, and clinical phenotypes. Always use this skill when a user needs a layered mechanism evidence chain rather than a flat summary or immediate gap analysis. Formal literature citations must be real and verifiable.", "category": "Evidence Insight", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "awesome-med-research-skills/Evidence Insight/disease-mechanism-evidence-map" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 85, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/awesome-med-research-skills/Evidence%20Insight/disease-mechanism-evidence-map/eval_report_disease-mechanism-evidence-map_result.json", "evaluated_on": "2026-04-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 87, "max_score": 100 }, "dynamic_score": { "score": 83.6, "max_score": 100 } }, "release": { "path": "releases/disease-mechanism-evidence-map/1.0.0.json", "sha256": "f00b85fed58a415313f9eb590fd08667d5f80d5713d71715af957e8840755320" }, "artifact": { "path": "shards/363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb.zip", "sha256": "363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb", "bytes": 1471244, "skill_path": "disease-mechanism-evidence-map" }, "content_sha256": "6acf7e9d974138dada15495c3f7792bfe487e8e1a56122edd2055ded69b72cf7" }, { "id": "dnanexus-integration", "version": "1.0.0", "display_name": "dnanexus-integration", "summary": "Build and operate reproducible genomics workloads on DNAnexus with the dx CLI, dxpy, apps/applets, native workflows, dxCompiler, and Nextflow. Use for DNAnexus data transfers, dxapp.json development, execution monitoring, workflow import, and project automation.", "category": "Data Analysis", "source": { "repository": "https://github.com/K-Dense-AI/scientific-agent-skills", "commit": "36d8f13a1e754618794bf42f417884940077b4ae", "path": "skills/dnanexus-integration" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "K-Dense Inc." } ], "license": "MIT", "release": { "path": "releases/dnanexus-integration/1.0.0.json", "sha256": "70953b34e4696c01a792d1287ccf8ace03375e520103ecc7952481947494feae" }, "artifact": { "path": "shards/b55fb597588b663d1d9dfe3695492a0b1a9b88845adbce48fe564656e55759c6.zip", "sha256": "b55fb597588b663d1d9dfe3695492a0b1a9b88845adbce48fe564656e55759c6", "bytes": 624089, "skill_path": "dnanexus-integration" }, "content_sha256": "20f7e7c306b98d8e9a705ac16bf02587652c08c52bdaadca21e1341424b286e1" }, { "id": "docx-feedback-tracker", "version": "1.0.0", "display_name": "docx-feedback-tracker", "summary": "Automatically detect DOCX track-changes history, version rounds, and author feedback to generate a Markdown modification explanation file when you run it on one or more DOCX versions.", "category": "Other", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Other/docx-feedback-tracker" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 86, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Other/docx-feedback-tracker/docx-feedback-tracker_audit_result_v2.json", "evaluated_on": "2026-03-23", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 85, "max_score": 100 }, "dynamic_score": { "score": 86.2, "max_score": 100 } }, "release": { "path": "releases/docx-feedback-tracker/1.0.0.json", "sha256": "8840f89078bb9ef8f27683e4ee0f0a398d267619c469723b77dd0705804a2ebf" }, "artifact": { "path": "shards/fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0.zip", "sha256": "fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0", "bytes": 1383584, "skill_path": "docx-feedback-tracker" }, "content_sha256": "a59afd5216989205f2e160b302e2d2b6f03911821f40bb1574ed73d23a6c24e7" }, { "id": "dpi-upscaler-checker", "version": "1.0.0", "display_name": "dpi-upscaler-checker", "summary": "Check if images meet 300 DPI printing standards, and intelligently restore blurry low-resolution images using AI super-resolution technology.", "category": "Other", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Other/dpi-upscaler-checker" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 83, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Other/dpi-upscaler-checker/dpi-upscaler-checker_audit_result_v4.json", "evaluated_on": "2026-03-19", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 87, "max_score": 100 }, "dynamic_score": { "score": 79.6, "max_score": 100 } }, "release": { "path": "releases/dpi-upscaler-checker/1.0.0.json", "sha256": "861daf42f90e0b762d206e9d41c4262ad4ad4491965e641d2245c2ac27fd7332" }, "artifact": { "path": "shards/fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0.zip", "sha256": "fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0", "bytes": 1383584, "skill_path": "dpi-upscaler-checker" }, "content_sha256": "d97a0bb94c74fb262086386f29af2ea80d8ff4498350877f541dc4ffaf3cdbe1" }, { "id": "drug-discovery-pipeline", "version": "1.0.0", "display_name": "drug-discovery-pipeline", "summary": "NOTE: molecule and target inputs and your NGC_API_KEY are transmitted to external NVIDIA-hosted API endpoints on every call. Use local NIM containers for confidential or proprietary data. Run a complete computational drug discovery pipeline using NVIDIA BioNeMo NIMs: generate drug-like molecules with GenMol, dock them to a protein target with DiffDock, then predict binding affinity with Boltz2. Use this skill whenever the user wants to generate and screen small molecule drug candidates, perform hit discovery, optimize leads against a protein target, or do virtual screening combining molecule generation, docking, and affinity prediction. Triggers on: drug discovery pipeline, hit discovery, lead optimization, virtual screening, molecule generation, molecular docking, binding affinity, GenMol, DiffDock, Boltz2, SMILES, SAFE notation, NIM microservice. This is a multi-step pipeline composing three BioNeMo NIMs.\n", "category": "Protocol Design", "source": { "repository": "https://github.com/NVIDIA-BioNeMo/bionemo-agent-toolkit", "commit": "0e67a612e4045f007e38fa77adc8f3ebfc5616b6", "path": "skills/bionemo-agent-toolkit/skills/drug-discovery-pipeline" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "license": "Apache-2.0 AND CC-BY-4.0", "release": { "path": "releases/drug-discovery-pipeline/1.0.0.json", "sha256": "0bc5381ef60ff245e9c47eccc05011bc94d7ec8c5c4f67b78f4c566e4d341f3f" }, "artifact": { "path": "shards/d9eadedf27584c96257c396b3554a2f479383e134ebaf455716ea80dd4f8de94.zip", "sha256": "d9eadedf27584c96257c396b3554a2f479383e134ebaf455716ea80dd4f8de94", "bytes": 1161536, "skill_path": "drug-discovery-pipeline" }, "content_sha256": "8bf77b5ab9b1c810ee5d0852dffcca4f54e26624de3b99e7faab476126458198" }, { "id": "drug-interaction-checker", "version": "1.0.1", "display_name": "drug-interaction-checker", "summary": "Check for interactions between multiple medications, including severity classification and mechanism explanations.", "category": "Evidence Insight", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Evidence Insight/drug-interaction-checker" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "Unknown", "evaluation": { "kind": "upstream-self-assessment", "score": 85, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Evidence%20Insight/drug-interaction-checker/drug-interaction-checker_audit_result_v2.json", "evaluated_on": "2026-03-23", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 88, "max_score": 100 }, "dynamic_score": { "score": 83.6, "max_score": 100 } }, "release": { "path": "releases/drug-interaction-checker/1.0.1.json", "sha256": "4519bcb364b11f3fb0facc52a06958749ee0da03c170aabcfdaaa322422bb4ba" }, "artifact": { "path": "shards/96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b.zip", "sha256": "96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b", "bytes": 66876850, "skill_path": "drug-interaction-checker" }, "content_sha256": "1d028ddd3e6ca97a23f2301af809522510867c8610ae7633dbd207dc0f8d8bfd" }, { "id": "drug-repurposing-study-planner", "version": "1.0.0", "display_name": "drug-repurposing-study-planner", "summary": "Design evidence-discovery and validation workflows for drug repurposing studies by integrating disease mechanisms, drug-target logic, expression reversal, real-world evidence, and validation routes into a closed-loop study blueprint.", "category": "Protocol Design", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "awesome-med-research-skills/Protocol Design/drug-repurposing-study-planner" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 87, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/awesome-med-research-skills/Protocol%20Design/drug-repurposing-study-planner/eval_report_drug-repurposing-study-planner_result.json", "evaluated_on": "2026-04-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 89, "max_score": 100 }, "dynamic_score": { "score": 86.1, "max_score": 100 } }, "release": { "path": "releases/drug-repurposing-study-planner/1.0.0.json", "sha256": "935e185d857c65212c41cdd785cfc8643d608d130a75cc233c307af01693bb9c" }, "artifact": { "path": "shards/fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0.zip", "sha256": "fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0", "bytes": 1383584, "skill_path": "drug-repurposing-study-planner" }, "content_sha256": "c3d80fc192a8e67733886b08c046c0d85455268f4361b93a5402c969e3e8ad37" }, { "id": "drug-target-evidence-landscape", "version": "1.0.0", "display_name": "drug-target-evidence-landscape", "summary": "Organizes the evidence and competitive landscape around a drug, target, or pathway by separating disease relevance, tractability, preclinical evidence, clinical evidence, modality fit, and crowding. Always map what is biologically supported, what is druggable, what has actually advanced, and what remains strategically open. Never confuse target relevance with druggability, preclinical activity with clinical promise, or narrative excitement with validated development maturity. Never fabricate references, trial status, approval status, company activity, or asset metadata.", "category": "Evidence Insight", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "awesome-med-research-skills/Evidence Insight/drug-target-evidence-landscape" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 86, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/awesome-med-research-skills/Evidence%20Insight/drug-target-evidence-landscape/eval_report_drug-target-evidence-landscape_result.json", "evaluated_on": "2026-04-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 89, "max_score": 100 }, "dynamic_score": { "score": 84.6, "max_score": 100 } }, "release": { "path": "releases/drug-target-evidence-landscape/1.0.0.json", "sha256": "b0e862d2eb46cb9bd12aa407a000ea0afedb9880039276e0da1898e71db76061" }, "artifact": { "path": "shards/363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb.zip", "sha256": "363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb", "bytes": 1471244, "skill_path": "drug-target-evidence-landscape" }, "content_sha256": "fdcfa4a8509686196ce6d48432418d5aeac09828ed1df4c41126e0804941c6e4" }, { "id": "drugbank-database", "version": "1.0.0", "display_name": "drugbank-database", "summary": "Programmatic access to DrugBank drug and target data; use when you need to download, parse, and analyze DrugBank XML for properties, interactions, pathways, and pharmacology.", "category": "Evidence Insight", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Evidence Insight/drugbank-database" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 89, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Evidence%20Insight/drugbank-database/drugbank-database_audit_result_v1.json", "evaluated_on": "2026-03-23", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 87, "max_score": 100 }, "dynamic_score": { "score": 90.6, "max_score": 100 } }, "release": { "path": "releases/drugbank-database/1.0.0.json", "sha256": "015cb4a1650c3a5d3fd0843b9ccf74241808286936bc577523f5d585cfc21c1d" }, "artifact": { "path": "shards/363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb.zip", "sha256": "363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb", "bytes": 1471244, "skill_path": "drugbank-database" }, "content_sha256": "5d735f13025607ff1a83c9d8cc09ebc4334164883a15c04863b2c98673cf8df7" }, { "id": "dual-disease-shared-transcriptome-biomarker-research-planner", "version": "1.0.0", "display_name": "dual-disease-shared-transcriptome-biomarker-research-planner", "summary": "Generates complete dual-disease shared-transcriptome biomarker and hub-gene research designs from a user-provided disease pair and shared-biology direction. Always use this skill whenever a user wants to design, plan, or build a non-oncology two-disease transcriptome study centered on per-disease differential expression, shared-signal intersection or concordance, PPI-based hub-gene prioritization, diagnostic evaluation across both diseases, immune infiltration context, pathway interpretation, and optional orthogonal validation. Covers five study patterns (shared-DEG-first workflow, hub-gene-first shared-biomarker workflow, hybrid shared-biomarker compression workflow, immune-context shared-biomarker workflow, orthogonal validation workflow) and always outputs four workload configs (Lite / Standard / Advanced / Publication+) with recommended primary plan, step-by-step workflow, figure plan, validation strategy, minimal executable version, publication upgrade path...", "category": "Protocol Design", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "awesome-med-research-skills/Protocol Design/dual-disease-shared-transcriptome-biomarker-research-planner" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 89, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/awesome-med-research-skills/Protocol%20Design/dual-disease-shared-transcriptome-biomarker-research-planner/eval_report_dual-disease-shared-transcriptome-biomarker-research-planner_result.json", "evaluated_on": "2026-04-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 92, "max_score": 100 }, "dynamic_score": { "score": 87.7, "max_score": 100 } }, "release": { "path": "releases/dual-disease-shared-transcriptome-biomarker-research-planner/1.0.0.json", "sha256": "a8449e4e219449b6b4416e13370e070ca659ff34d86a336ab582bd12c756eaed" }, "artifact": { "path": "shards/fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0.zip", "sha256": "fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0", "bytes": 1383584, "skill_path": "dual-disease-shared-transcriptome-biomarker-research-planner" }, "content_sha256": "c2ecc707f48242760eabcad7244ee2c1edcda5458a0df570124fb4df51496da5" }, { "id": "dual-disease-transcriptomic-ml-planner", "version": "1.0.0", "display_name": "dual-disease-transcriptomic-ml-planner", "summary": "Generates complete dual-disease transcriptomic + machine learning research designs from a user-provided disease pair. Use when users want to identify shared DEGs, common hub genes, cross-disease biomarkers, or shared molecular mechanisms between two diseases using public GEO data. Triggers:\"shared biomarker study for two diseases\", \"dual-disease transcriptomic ML paper\", \"identify common DEGs between disease A and B\", \"cross-disease hub gene discovery\", \"shared DEG + PPI + ROC design\", \"immune infiltration shared biomarker\", or \"I want to study disease X and Y together\". Always outputs four workload configurations (Lite / Standard / Advanced / Publication+) with a recommended primary plan, step-by-step workflow, figure plan, validation strategy, minimal executable version, and publication upgrade path.", "category": "Protocol Design", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Protocol Design/dual-disease-transcriptomic-ml-planner" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "release": { "path": "releases/dual-disease-transcriptomic-ml-planner/1.0.0.json", "sha256": "4139564046a2629fd58e7705543a646a25820225ff8962848443620baa203215" }, "artifact": { "path": "shards/fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0.zip", "sha256": "fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0", "bytes": 1383584, "skill_path": "dual-disease-transcriptomic-ml-planner" }, "content_sha256": "51fcf960dd6072d72659e3225d3d7bb186a9b177d6af3b0aa45fa6f09cb90416" }, { "id": "ebm-calculator", "version": "1.0.0", "display_name": "ebm-calculator", "summary": "Evidence-Based Medicine diagnostic test calculator. Computes sensitivity, specificity, PPV, NPV, likelihood ratios, NNT, and pre/post-test probability.", "category": "Other", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Other/ebm-calculator" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 91, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Other/ebm-calculator/eval_report_ebm-calculator_result.json", "evaluated_on": "2026-06-15", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 97, "max_score": 100 }, "dynamic_score": { "score": 86.6, "max_score": 100 } }, "release": { "path": "releases/ebm-calculator/1.0.0.json", "sha256": "8533b78862c0dd2ab66e679a5cba101e26b95af4739782f5c3b200e236b546e2" }, "artifact": { "path": "shards/fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0.zip", "sha256": "fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0", "bytes": 1383584, "skill_path": "ebm-calculator" }, "content_sha256": "44d27de208ae41e68f304818a0d788113ff3f0eecbaf958c25060fd3091e772d" }, { "id": "ectd-xml-compiler", "version": "1.0.1", "display_name": "ectd-xml-compiler", "summary": "Automatically convert uploaded drug application documents (Word/PDF) into XML skeleton structure compliant with eCTD 4.0/3.2.2 specifications.", "category": "Academic Writing", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Academic Writing/ectd-xml-compiler" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 85, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Academic%20Writing/ectd-xml-compiler/ectd-xml-compiler_audit_result_v2.json", "evaluated_on": "2026-03-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 88, "max_score": 100 }, "dynamic_score": { "score": 83.6, "max_score": 100 } }, "release": { "path": "releases/ectd-xml-compiler/1.0.1.json", "sha256": "d649c4ff21bd653a70c5d07ad411a92d67f97d0fd45c7805c6d65fd4d37a2292" }, "artifact": { "path": "shards/96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b.zip", "sha256": "96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b", "bytes": 66876850, "skill_path": "ectd-xml-compiler" }, "content_sha256": "2807f63561f0726fe0ec6623dad559659e6b79f1e1a08e80374b00ff84b96ae8" }, { "id": "ehr-semantic-compressor", "version": "1.0.0", "display_name": "ehr-semantic-compressor", "summary": "1. Confirm the user objective, required inputs, and non-negotiable constraints before doing detailed work. 2. Validate that the request matches the documented scope and stop early if the task would require unsupported as.", "category": "Academic Writing", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Academic Writing/ehr-semantic-compressor" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 85, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Academic%20Writing/ehr-semantic-compressor/ehr-semantic-compressor_audit_result_v1.json", "evaluated_on": "2026-03-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 88, "max_score": 100 }, "dynamic_score": { "score": 83.6, "max_score": 100 } }, "release": { "path": "releases/ehr-semantic-compressor/1.0.0.json", "sha256": "c7d7f92780aea1d6006e6f562a746cb478ab28f04ffb9c4e1c016e8039c6db63" }, "artifact": { "path": "shards/1d2612a702320846b10cda57f9f37ce17f5e19186095f2fe96bfa6e44eb077e8.zip", "sha256": "1d2612a702320846b10cda57f9f37ce17f5e19186095f2fe96bfa6e44eb077e8", "bytes": 234009, "skill_path": "ehr-semantic-compressor" }, "content_sha256": "3c133920e9e08b6b9a701e982f52eedf69af226b9bee5667751cfff2371853e5" }, { "id": "emerging-topic-scout", "version": "1.0.1", "display_name": "emerging-topic-scout", "summary": "A real-time monitoring system for identifying \"incubation period\" research hotspots in biological and medical sciences before they are defined by mainstream journals.", "category": "Evidence Insight", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Evidence Insight/emerging-topic-scout" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "Unknown", "evaluation": { "kind": "upstream-self-assessment", "score": 85, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Evidence%20Insight/emerging-topic-scout/emerging-topic-scout_audit_result_v2.json", "evaluated_on": "2026-03-23", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 87, "max_score": 100 }, "dynamic_score": { "score": 83.6, "max_score": 100 } }, "release": { "path": "releases/emerging-topic-scout/1.0.1.json", "sha256": "395af31785ab8dfb493041f2bb7ad9e7b54283b7b642167b0ffcf32e7b18861f" }, "artifact": { "path": "shards/96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b.zip", "sha256": "96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b", "bytes": 66876850, "skill_path": "emerging-topic-scout" }, "content_sha256": "1d9cfa8aa6967594b1fad8e2cf7a11ac8307c350b41d272d9062fea2a46be9e5" }, { "id": "ena-database", "version": "1.0.0", "display_name": "ena-database", "summary": "Access the European Nucleotide Archive (ENA) via REST APIs and FTP/Aspera to search and retrieve sequences, raw reads (FASTQ), assemblies, and metadata when you have accession IDs or need metadata-driven discovery for genomics pipelines.", "category": "Evidence Insight", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Evidence Insight/ena-database" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 86, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Evidence%20Insight/ena-database/ena-database_audit_result_v1.json", "evaluated_on": "2026-03-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 84, "max_score": 100 }, "dynamic_score": { "score": 87.6, "max_score": 100 } }, "release": { "path": "releases/ena-database/1.0.0.json", "sha256": "fdb838637363ac65ba061ac59d05ccf9dd8b8589cf3b45b5476f98e9d259dbc2" }, "artifact": { "path": "shards/363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb.zip", "sha256": "363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb", "bytes": 1471244, "skill_path": "ena-database" }, "content_sha256": "56a4172a80ea3806d01c9afcb34b227b4d27970e665fefb307823dd819f85596" }, { "id": "encode-api", "version": "1.0.0", "display_name": "encode-api", "summary": "Access the ENCODE Project REST API to search for and retrieve biological data (biosamples, experiments, etc.). Use this skill when the user needs to query ENCODE data, search by keywords, or retrieve specific objects by accession ID.", "category": "Evidence Insight", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Evidence Insight/encode-api" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 89, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Evidence%20Insight/encode-api/encode-api_audit_result_v2.json", "evaluated_on": "2026-03-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 83, "max_score": 100 }, "dynamic_score": { "score": 92.6, "max_score": 100 } }, "release": { "path": "releases/encode-api/1.0.0.json", "sha256": "b176a1c40117afb61d737f82976aebb066fdf04b24e68bfcffa89bae09792e47" }, "artifact": { "path": "shards/363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb.zip", "sha256": "363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb", "bytes": 1471244, "skill_path": "encode-api" }, "content_sha256": "caee6bf00de34cb27688cdfaf69b5ba8d272749a752c1662b84c030d73616fe7" }, { "id": "encori-api", "version": "1.0.0", "display_name": "encori-api", "summary": "Access ENCORI (StarBase) database for miRNA-target, RNA-RNA, and other regulatory data. Invoke when user asks to search ENCORI or retrieve regulatory interactions.", "category": "Evidence Insight", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Evidence Insight/encori-api" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 91, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Evidence%20Insight/encori-api/encori-api_audit_result_v2.json", "evaluated_on": "2026-03-23", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 83, "max_score": 100 }, "dynamic_score": { "score": 96, "max_score": 100 } }, "release": { "path": "releases/encori-api/1.0.0.json", "sha256": "b8df9245579fa5b9bcc9ed97fe564c6598401c859a4e03f5f86ce58461391a79" }, "artifact": { "path": "shards/363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb.zip", "sha256": "363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb", "bytes": 1471244, "skill_path": "encori-api" }, "content_sha256": "06d1570ad9287a06a7cba60ab255646c186c41954b954c4229f7006a31687fdc" }, { "id": "endpoint-definition-designer", "version": "1.0.0", "display_name": "endpoint-definition-designer", "summary": "Designs primary, secondary, and exploratory endpoints for biomedical and clinical research protocols. Always use this skill when a user needs to translate study aims into operational endpoint definitions with event rules, assessment timing, composite logic, interpretability, and protocol-stage auditability. Focus on endpoint precision, feasibility, clinical meaning, ambiguity reduction, and implementation readiness rather than generic study design advice.", "category": "Protocol Design", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "awesome-med-research-skills/Protocol Design/endpoint-definition-designer" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 90, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/awesome-med-research-skills/Protocol%20Design/endpoint-definition-designer/eval_report_endpoint-definition-designer_result.json", "evaluated_on": "2026-04-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 93, "max_score": 100 }, "dynamic_score": { "score": 87.8, "max_score": 100 } }, "release": { "path": "releases/endpoint-definition-designer/1.0.0.json", "sha256": "a7d0cf4154179adb74cb1ad8f5e0693d03622422441090c7b8353d951aadb623" }, "artifact": { "path": "shards/fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0.zip", "sha256": "fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0", "bytes": 1383584, "skill_path": "endpoint-definition-designer" }, "content_sha256": "2eaa7b5b86e4d3a6ac09896e4927c276a29beace6c698a5efc14d13062451ed5" }, { "id": "ensembl-database", "version": "1.0.0", "display_name": "ensembl-database", "summary": "Access Ensembl REST API for vertebrate genomic data; use when you need gene/ID lookups, sequence retrieval, variant effect prediction (VEP), or homology/assembly coordinate mapping.", "category": "Evidence Insight", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Evidence Insight/ensembl-database" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 89, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Evidence%20Insight/ensembl-database/ensembl-database_audit_result_v1.json", "evaluated_on": "2026-03-23", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 78, "max_score": 100 }, "dynamic_score": { "score": 95.6, "max_score": 100 } }, "release": { "path": "releases/ensembl-database/1.0.0.json", "sha256": "ff0cd31d425e6805ce7ba145223eaaf52a9c0662e5e82afbadc6b14711909bf6" }, "artifact": { "path": "shards/363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb.zip", "sha256": "363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb", "bytes": 1471244, "skill_path": "ensembl-database" }, "content_sha256": "c676a5877d37464316afe4259b993b54a0392036b590b1c10c055fa72f37e68e" }, { "id": "epidemiology", "version": "1.0.1", "display_name": "epidemiology", "summary": "Performs epidemiological analyses including disease modeling (SIR/SEIR), outbreak investigation, risk factor identification, incidence/prevalence estimation, and causal inference from observational data; trigger when users discuss disease spread, public health data, or populat...", "category": "Data Analysis", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Data Analysis/epidemiology" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 86, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Data%20Analysis/epidemiology/eval_report_epidemiology_result.json", "evaluated_on": "2026-06-15", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 87, "max_score": 100 }, "dynamic_score": { "score": 86, "max_score": 100 } }, "release": { "path": "releases/epidemiology/1.0.1.json", "sha256": "32ef9a5bda3cf84e923cbbdcb79f299d0d3ef929f7b257617c22a9496501f7d4" }, "artifact": { "path": "shards/96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b.zip", "sha256": "96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b", "bytes": 66876850, "skill_path": "epidemiology" }, "content_sha256": "1f72891519172752b7272e47b862d2eb1b15c69af9622c228d33b4a1a0e9565b" }, { "id": "equipment-maintenance-log", "version": "1.0.0", "display_name": "equipment-maintenance-log", "summary": "Track lab equipment calibration dates and send maintenance reminders for pipettes, balances, centrifuges, and other instruments. Validates date formats and supports update/delete operations.", "category": "Other", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Other/equipment-maintenance-log" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 90, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Other/equipment-maintenance-log/equipment-maintenance-log_audit_result_v4.json", "evaluated_on": "2026-03-19", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 89, "max_score": 100 }, "dynamic_score": { "score": 90, "max_score": 100 } }, "release": { "path": "releases/equipment-maintenance-log/1.0.0.json", "sha256": "b332ba68dacf148f72f55fe1b5557384566206053b6e9ff68012fc2c4ed27791" }, "artifact": { "path": "shards/fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0.zip", "sha256": "fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0", "bytes": 1383584, "skill_path": "equipment-maintenance-log" }, "content_sha256": "130e104e0008e4adc43df13d8db21b32f480e28e85ad1551e7c1b55e99b8a315" }, { "id": "esm", "version": "1.0.0", "display_name": "esm", "summary": "Toolkit for protein language models (ESM3 for multimodal generative protein design; ESM C for efficient embeddings). Use when you need sequence/structure/function generation or prediction, inverse folding, protein embeddings, or scalable inference via local weights or the Forge API.", "category": "Protocol Design", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Protocol Design/esm" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 87, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Protocol%20Design/esm/esm_audit_result_v1.json", "evaluated_on": "2026-03-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 84, "max_score": 100 }, "dynamic_score": { "score": 88.6, "max_score": 100 } }, "release": { "path": "releases/esm/1.0.0.json", "sha256": "e9048284c7936eb990eae2f287903eceb384b798f1e2ea1d028a91d3b7d58bb8" }, "artifact": { "path": "shards/fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0.zip", "sha256": "fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0", "bytes": 1383584, "skill_path": "esm" }, "content_sha256": "87b8e95e7e2d14a052de080b2c8d35935f02a9d185e8de55b1ec3688e6268dd1" }, { "id": "estimate-immune-score-analysis", "version": "1.0.1", "display_name": "estimate-immune-score-analysis", "summary": "Use this skill to compute ESTIMATE immune-related microenvironment scores from a bulk expression matrix, generate an ESTIMATE score heatmap, and optionally generate group-wise ESTIMATE score boxplots plus significance tables when a sample group file is supplied. Trigger keywords: ESTIMATE, immune score, stromal score, tumor microenvironment score. NOT for: immune cell deconvolution, single-cell analysis, differential expression, clinical diagnosis.", "category": "Data Analysis", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "awesome-med-research-skills/Data Analysis/estimate-immune-score-analysis" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "license": "Unknown", "evaluation": { "kind": "upstream-self-assessment", "score": 97, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/awesome-med-research-skills/Data%20Analysis/estimate-immune-score-analysis/eval_report_estimate-immune-score-analysis_result.json", "evaluated_on": "2026-04-27", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 99, "max_score": 100 }, "dynamic_score": { "score": 95.2, "max_score": 100 } }, "release": { "path": "releases/estimate-immune-score-analysis/1.0.1.json", "sha256": "a3814afed3681f1699879ede4dcf811e7db5572aeabedaafb63960bc3abfc3e2" }, "artifact": { "path": "shards/96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b.zip", "sha256": "96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b", "bytes": 66876850, "skill_path": "estimate-immune-score-analysis" }, "content_sha256": "e27ca0fdb28a7c58fad6e72241a292f9007674e8f34d16f262bd7cce57484a98" }, { "id": "etetoolkit", "version": "1.0.1", "display_name": "etetoolkit", "summary": "ETE (Environment for Tree Exploration) toolkit for phylogenetic and hierarchical tree analysis; use it when you need to parse/manipulate Newick/NHX trees, detect duplication/speciation events, integrate NCBI taxonomy, and render publication-quality figures.", "category": "Data Analysis", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Data Analysis/etetoolkit" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 87, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Data%20Analysis/etetoolkit/etetoolkit_audit_result_v1.json", "evaluated_on": "2026-03-23", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 87, "max_score": 100 }, "dynamic_score": { "score": 87.2, "max_score": 100 } }, "release": { "path": "releases/etetoolkit/1.0.1.json", "sha256": "ff3ee19d160b947e7d9934fe72b73373901761f26ecdb91812274c3be94b41d1" }, "artifact": { "path": "shards/96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b.zip", "sha256": "96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b", "bytes": 66876850, "skill_path": "etetoolkit" }, "content_sha256": "0ef8748731cb87e41cf509ce14683685f77797ca8002b3cacbd13ae890e022c4" }, { "id": "evidence-level-ranker", "version": "1.0.0", "display_name": "evidence-level-ranker", "summary": "Ranks papers by evidence family, methodological quality tier, validation depth, and claim discipline; assigns anchor, context-setting, mechanistic support, or caution citation roles; prevents prestige-based or design-label-based ranking errors.", "category": "Evidence Insight", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "awesome-med-research-skills/Evidence Insight/evidence-level-ranker" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 82, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/awesome-med-research-skills/Evidence%20Insight/evidence-level-ranker/eval_report_evidence-level-ranker_result.json", "evaluated_on": "2026-04-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 84, "max_score": 100 }, "dynamic_score": { "score": 81.4, "max_score": 100 } }, "release": { "path": "releases/evidence-level-ranker/1.0.0.json", "sha256": "67ef27e40ff0a829a727620c4a52c3a04b4616665e85eceb0565616f8d21c494" }, "artifact": { "path": "shards/363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb.zip", "sha256": "363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb", "bytes": 1471244, "skill_path": "evidence-level-ranker" }, "content_sha256": "1e967c32d25525aed6d634a7770a248679b9c88ec66b421b20e5953d6b2771d6" }, { "id": "evo2-nim", "version": "1.0.0", "display_name": "evo2-nim", "summary": "Generate and analyze DNA sequences using NVIDIA's Evo 2 BioNeMo NIM microservice. Use for Evo2/Evo 2, DNA generation, genomic sequence generation, hosted generation, local Docker deployment, local forward passes, layer outputs, logits, sampled probabilities, and BioNeMo NIM workflows.\n", "category": "Data Analysis", "source": { "repository": "https://github.com/NVIDIA-BioNeMo/bionemo-agent-toolkit", "commit": "0e67a612e4045f007e38fa77adc8f3ebfc5616b6", "path": "skills/bionemo-agent-toolkit/skills/evo2-nim" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "license": "Apache-2.0 AND CC-BY-4.0", "release": { "path": "releases/evo2-nim/1.0.0.json", "sha256": "d4881b5958f0e970930312dadd0545378d6ae86fd77d67a6317748774e4c7dc6" }, "artifact": { "path": "shards/d9eadedf27584c96257c396b3554a2f479383e134ebaf455716ea80dd4f8de94.zip", "sha256": "d9eadedf27584c96257c396b3554a2f479383e134ebaf455716ea80dd4f8de94", "bytes": 1161536, "skill_path": "evo2-nim" }, "content_sha256": "1c911c4101e7eb57ae40ae48e3cfcaa25ba4d9069a171609cb39585f5a15d79e" }, { "id": "experiment-design", "version": "1.0.0", "display_name": "experiment-design", "summary": "Design scientific experiments including sample size calculation, randomization, control groups, blinding, and study protocols. Covers RCTs, quasi-experiments, factorial designs, A/B tests, survey design, and observational studies. Use when user asks to design an experiment, ca...", "category": "Protocol Design", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Protocol Design/experiment-design" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 87, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Protocol%20Design/experiment-design/eval_report_experiment-design_result.json", "evaluated_on": "2026-06-15", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 88, "max_score": 100 }, "dynamic_score": { "score": 86.2, "max_score": 100 } }, "release": { "path": "releases/experiment-design/1.0.0.json", "sha256": "445178a064f3474a01ee91cc935534227b89554b5f3e9532dd457b59378548c5" }, "artifact": { "path": "shards/fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0.zip", "sha256": "fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0", "bytes": 1383584, "skill_path": "experiment-design" }, "content_sha256": "461209896c8a0bd5f25fc75347029e7750c4104cf90e7bb1aa24a11cd412a50b" }, { "id": "experiment-detail-comparator", "version": "1.0.1", "display_name": "experiment-detail-comparator", "summary": "Compare experimental method details between two Zotero PDF papers, identify protocol differences (ratios, dosages, timing, conditions), search supporting literature to explain why they differ, and generate an HTML report. Use when you need a parameter-level comparison of two methods and evidence-backed reasons for discrepancies.", "category": "Other", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Other/experiment-detail-comparator" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "Unknown", "evaluation": { "kind": "upstream-self-assessment", "score": 86, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Other/experiment-detail-comparator/experiment-detail-comparator_audit_result_v1.json", "evaluated_on": "2026-03-23", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 81, "max_score": 100 }, "dynamic_score": { "score": 89.6, "max_score": 100 } }, "release": { "path": "releases/experiment-detail-comparator/1.0.1.json", "sha256": "d89384533c12dc915394db4e2852a8bfa5a941719d4e85d73d6ae3ea0ed27c5d" }, "artifact": { "path": "shards/96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b.zip", "sha256": "96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b", "bytes": 66876850, "skill_path": "experiment-detail-comparator" }, "content_sha256": "c56db9a206ca91163686cf1cb0040a483ca314502295f453b2042e22cbb6feea" }, { "id": "experimental-data-analysis", "version": "1.0.1", "display_name": "experimental-data-analysis", "summary": "Statistical analysis and reporting for experimental datasets; use when you need to interpret experimental results, test significance (t-tests/ANOVA), or generate reproducible reports.", "category": "Data Analysis", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Data Analysis/experimental-data-analysis" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 91, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Data%20Analysis/experimental-data-analysis/experimental-analysis_audit_result_v1.json", "evaluated_on": "2026-03-23", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 88, "max_score": 100 }, "dynamic_score": { "score": 93.6, "max_score": 100 } }, "release": { "path": "releases/experimental-data-analysis/1.0.1.json", "sha256": "91fce786af3279f0d486b0bbfefeba9617866ec60537a6d886d39656454b377c" }, "artifact": { "path": "shards/96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b.zip", "sha256": "96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b", "bytes": 66876850, "skill_path": "experimental-data-analysis" }, "content_sha256": "6b7b1d07fe88a1748fc5df5ff0529db226685746404b32fb90ca28ab1fd75de8" }, { "id": "expert-interview-generator", "version": "1.0.0", "display_name": "expert-interview-generator", "summary": "Generates a full expert interview article including introduction, Q&A body, and summary based on interview questions and expert background. Use when you have interview questions and an expert profile and need a polished article.", "category": "Other", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Other/expert-interview-generator" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 92, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Other/expert-interview-generator/expert-interview-generator_audit_result_v2.json", "evaluated_on": "2026-03-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 88, "max_score": 100 }, "dynamic_score": { "score": 94.6, "max_score": 100 } }, "release": { "path": "releases/expert-interview-generator/1.0.0.json", "sha256": "95af26210096bbd4c473d4ba41c729068682fcb04f87f8f6373d05d02308e6b7" }, "artifact": { "path": "shards/fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0.zip", "sha256": "fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0", "bytes": 1383584, "skill_path": "expert-interview-generator" }, "content_sha256": "c14e5ed9b409354591ea23d179bc4909b7cd9522dccab9bb3d734e68b8c45c28" }, { "id": "expert-interview-topics", "version": "1.0.0", "display_name": "expert-interview-topics", "summary": "Generates professional interview titles and questions based on expert background and topic. Provides a structured workflow for interview preparation.", "category": "Other", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Other/expert-interview-topics" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 90, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Other/expert-interview-topics/expert-interview-topics_audit_result_v2.json", "evaluated_on": "2026-03-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 83, "max_score": 100 }, "dynamic_score": { "score": 94.6, "max_score": 100 } }, "release": { "path": "releases/expert-interview-topics/1.0.0.json", "sha256": "692047babad70a0beece2131a0d3ff9a24ceffef78ea2d01979c2341495c4404" }, "artifact": { "path": "shards/fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0.zip", "sha256": "fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0", "bytes": 1383584, "skill_path": "expert-interview-topics" }, "content_sha256": "95f15e20f5c2f23e8bd32678652ed9bb35413e460032a7a5711e1add657ddf02" }, { "id": "exploratory-data-analysis", "version": "1.0.0", "display_name": "exploratory-data-analysis", "summary": "Perform comprehensive exploratory data analysis on scientific data files across 200+ file formats. This skill should be used when analyzing any scientific data file to understand its structure, content, quality, and characteristics. Automatically detects file type and generate...", "category": "Data Analysis", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Data Analysis/exploratory-data-analysis" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 88, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Data%20Analysis/exploratory-data-analysis/eval_report_exploratory-data-analysis_result.json", "evaluated_on": "2026-06-15", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 91, "max_score": 100 }, "dynamic_score": { "score": 86.6, "max_score": 100 } }, "release": { "path": "releases/exploratory-data-analysis/1.0.0.json", "sha256": "b4a16e9997bf55337aeedd4d198506c94d47a878c3466194f6f77a07d39c84b7" }, "artifact": { "path": "shards/1d2612a702320846b10cda57f9f37ce17f5e19186095f2fe96bfa6e44eb077e8.zip", "sha256": "1d2612a702320846b10cda57f9f37ce17f5e19186095f2fe96bfa6e44eb077e8", "bytes": 234009, "skill_path": "exploratory-data-analysis" }, "content_sha256": "00b4880559f130bb6f88bc30e9e3ef31836db41c18d75df9e06fb0b150ff244c" }, { "id": "external-model-validation", "version": "1.0.1", "display_name": "external-model-validation", "summary": "Use when validating an existing prognostic risk signature on an external bulk expression cohort with survival outcomes, producing risk scores, Kaplan-Meier curves, risk distribution plots, heatmap, and time-dependent ROC curves. NOT for: model training, feature selection, nomogram construction, calibration analysis, or single-cell data.", "category": "Data Analysis", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "awesome-med-research-skills/Data Analysis/external-model-validation" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "license": "Unknown", "evaluation": { "kind": "upstream-self-assessment", "score": 90, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/awesome-med-research-skills/Data%20Analysis/external-model-validation/eval_report_external-model-validation_result.json", "evaluated_on": "2026-04-27", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 95, "max_score": 100 }, "dynamic_score": { "score": 86.7, "max_score": 100 } }, "release": { "path": "releases/external-model-validation/1.0.1.json", "sha256": "09c4627e1210c63a69c4f88d768c669b4400def3c13f0b803be64864ce51572a" }, "artifact": { "path": "shards/96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b.zip", "sha256": "96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b", "bytes": 66876850, "skill_path": "external-model-validation" }, "content_sha256": "a5f7c4fd7cabbf5bb6562bac0434d8efbb769be2943a7ff08211bc004bfc20cd" }, { "id": "facs-gating-viz-style", "version": "1.0.1", "display_name": "facs-gating-viz-style", "summary": "Use facs gating viz style for data analysis workflows that need structured execution, explicit assumptions, and clear output boundaries.", "category": "Data Analysis", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Data Analysis/facs-gating-viz-style" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 86, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Data%20Analysis/facs-gating-viz-style/facs-gating-viz-style_audit_result_v2.json", "evaluated_on": "2026-03-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 83, "max_score": 100 }, "dynamic_score": { "score": 87.2, "max_score": 100 } }, "release": { "path": "releases/facs-gating-viz-style/1.0.1.json", "sha256": "5d6d701ce156f5da522478614d8bed06b5800db251b145f9f32a41211014673e" }, "artifact": { "path": "shards/96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b.zip", "sha256": "96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b", "bytes": 66876850, "skill_path": "facs-gating-viz-style" }, "content_sha256": "38819f4e308dbff5098552c25964e4e2e3acd83524a36835eb7bf02361e63b65" }, { "id": "faers-multi-drug-soc-planner", "version": "1.0.0", "display_name": "faers-multi-drug-soc-planner", "summary": "Generates complete FAERS-based multi-drug single-SOC safety comparison research designs from a user-provided drug set, comparator, and adverse event domain. Always use this skill when users want to compare safety signals across multiple drugs using FAERS or OpenFDA data within one System Organ Class (SOC) or bounded AE domain. Trigger for:\"FAERS study comparing drugs within one SOC\", \"publishable FAERS safety comparison paper\", \"compare neuropsychiatric adverse events across beta-blockers\", \"Lite/Standard/Advanced FAERS safety plans\", \"active-comparator restricted disproportionality\", \"adjusted ROR logistic regression FAERS\", \"within-class head-to-head drug comparison\", \"pharmacovigilance signal comparison\", \"single-SOC PT-level FAERS design\", or any phrasing like \"I want to compare drug X and drug Y for adverse events in FAERS\" or \"build a comparative pharmacovigilance paper\". Always output four workload configurations (Lite / Standard / Advanced / Publication+) with a recommended primary plan, step-by-step workflow, figure plan, validation strategy, minimal executable version, and publication upgrade path.", "category": "Protocol Design", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Protocol Design/faers-multi-drug-soc-planner" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "release": { "path": "releases/faers-multi-drug-soc-planner/1.0.0.json", "sha256": "28cd0a722af505cc5a61676cbb8d291147ed12863548b2fee2e642e378456198" }, "artifact": { "path": "shards/fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0.zip", "sha256": "fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0", "bytes": 1383584, "skill_path": "faers-multi-drug-soc-planner" }, "content_sha256": "ced178ddf5fb9ae4098461abdac9ad106af4a0129a9b75b78657ff832218b3a2" }, { "id": "faers-pharmacovigilance-disproportionality-research-planner", "version": "1.0.0", "display_name": "faers-pharmacovigilance-disproportionality-research-planner", "summary": "Generates complete FAERS-style pharmacovigilance disproportionality research designs from a user-provided drug class, comparator strategy, adverse-event domain, and patient-group stratification. Always use this skill whenever a user wants to design, plan, or build a spontaneous-report safety signal study using FAERS or a similar pharmacovigilance database, especially when the article logic includes product selection, indication-group stratification, MedDRA-based adverse-event extraction, serious-case filtering, suspect-drug and concomitant-exclusion logic, reporting odds ratio analysis, comparator-drug benchmarking, cross-drug comparison, and cautious signal interpretation without causal overclaiming. Covers five study patterns (single-drug disproportionality workflow, multi-drug class comparison workflow, indication-stratified workflow, comparator-controlled signal screening workflow...", "category": "Protocol Design", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "awesome-med-research-skills/Protocol Design/faers-pharmacovigilance-disproportionality-research-planner" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 89, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/awesome-med-research-skills/Protocol%20Design/faers-pharmacovigilance-disproportionality-research-planner/eval_report_faers-pharmacovigilance-disproportionality-research-planner_result.json", "evaluated_on": "2026-04-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 92, "max_score": 100 }, "dynamic_score": { "score": 87.7, "max_score": 100 } }, "release": { "path": "releases/faers-pharmacovigilance-disproportionality-research-planner/1.0.0.json", "sha256": "bf9388dc806472acbc8e60ba6f9f41299d09f518f2513664e8730165f2f9dcc5" }, "artifact": { "path": "shards/fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0.zip", "sha256": "fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0", "bytes": 1383584, "skill_path": "faers-pharmacovigilance-disproportionality-research-planner" }, "content_sha256": "7b606a2fc5887c65908d3baf6913db646e6f1e54b00608f83b52d8e6b6f0c71e" }, { "id": "faq-generator", "version": "1.0.1", "display_name": "faq-generator", "summary": "Generates FAQ lists from complex medical policies or protocols. Trigger when user provides medical documents, policies, or protocols and requests FAQ generation, patient education materials, or simplified explanations.", "category": "Academic Writing", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Academic Writing/faq-generator" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 85, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Academic%20Writing/faq-generator/faq-generator_audit_result_v1.json", "evaluated_on": "2026-03-23", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 88, "max_score": 100 }, "dynamic_score": { "score": 83.6, "max_score": 100 } }, "release": { "path": "releases/faq-generator/1.0.1.json", "sha256": "7c6908c093b69f1769a63b64679cf4c67b25495fc9e32190ccf78074d9e70138" }, "artifact": { "path": "shards/96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b.zip", "sha256": "96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b", "bytes": 66876850, "skill_path": "faq-generator" }, "content_sha256": "7b4ec0d27bb73a5a4a51993574093fc51d48a80a0c67734ea1c575709438a00b" }, { "id": "fda-database", "version": "1.0.0", "display_name": "fda-database", "summary": "Query openFDA API for drugs, devices, adverse events, recalls, regulatory submissions (510k, PMA), substance identification (UNII), for FDA regulatory data analysis and safety research.", "category": "Evidence Insight", "source": { "repository": "https://github.com/K-Dense-AI/scientific-agent-skills", "commit": "71add644263a56368f8680d68df504c6674dc1e5", "path": "scientific-skills/fda-database" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "K-Dense Inc." } ], "license": "Unknown", "release": { "path": "releases/fda-database/1.0.0.json", "sha256": "cb62023e7ca4b35679f23a229d14f89335ee1c3636f6b63070c15d93a475b1c2" }, "artifact": { "path": "shards/fcd3eaa3439234f967f6835adacfe5bd6e8cacef5cbe5da7a06ffa4dec4f4973.zip", "sha256": "fcd3eaa3439234f967f6835adacfe5bd6e8cacef5cbe5da7a06ffa4dec4f4973", "bytes": 110833, "skill_path": "fda-database" }, "content_sha256": "e4c50c44ad37d3ded46730616b8f9c7ee5502886018379967caf5658afdb17c2" }, { "id": "feasibility-aware-study-planner", "version": "1.0.0", "display_name": "feasibility-aware-study-planner", "summary": "Designs a realistic, execution-aware biomedical study version under explicit constraints of samples, time, budget, data access, lab capacity, team skill, and validation resources. Always use this skill when the user has a real study idea, a candidate route, or a partially framed project but cannot assume ideal conditions. If critical feasibility inputs are missing, first clarify what resources are currently available, what resources may be obtainable, and what resources are realistically unavailable. Do not invent access, capabilities, collaborations, or validation resources. Focus first on feasibility-constrained study framing, route narrowing, dependency control, and minimum viable study design.", "category": "Protocol Design", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "awesome-med-research-skills/Protocol Design/feasibility-aware-study-planner" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 90, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/awesome-med-research-skills/Protocol%20Design/feasibility-aware-study-planner/eval_report_feasibility-aware-study-planner_result.json", "evaluated_on": "2026-04-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 92, "max_score": 100 }, "dynamic_score": { "score": 88.4, "max_score": 100 } }, "release": { "path": "releases/feasibility-aware-study-planner/1.0.0.json", "sha256": "bdfdbd0c9f1ccef8af14e81957e9677edf3b556915323f1e104b7b7339e6ae78" }, "artifact": { "path": "shards/fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0.zip", "sha256": "fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0", "bytes": 1383584, "skill_path": "feasibility-aware-study-planner" }, "content_sha256": "dcaa07ec8b0af1b432dac0ff7b6f1bd37a3486b411de49d89440c70308deaa0e" }, { "id": "figure-first-paper-reader", "version": "1.0.0", "display_name": "figure-first-paper-reader", "summary": "Reads a paper figure by figure before re-integrating the full narrative, so the user can identify the core findings quickly and check whether each visual actually supports the authors' main claims. Always separate figure content, figure-linked claim, evidentiary strength, and unsupported interpretation. Never fabricate references, PMIDs, DOIs, figure content, panel labels, result values, or study details that were not actually provided.", "category": "Evidence Insight", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "awesome-med-research-skills/Evidence Insight/figure-first-paper-reader" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 87, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/awesome-med-research-skills/Evidence%20Insight/figure-first-paper-reader/eval_report_figure-first-paper-reader_result.json", "evaluated_on": "2026-04-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 91, "max_score": 100 }, "dynamic_score": { "score": 84.6, "max_score": 100 } }, "release": { "path": "releases/figure-first-paper-reader/1.0.0.json", "sha256": "611cd6220fc75d25a5af84f0618fded5a8824bf16e223c8efc341258a4a81138" }, "artifact": { "path": "shards/363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb.zip", "sha256": "363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb", "bytes": 1471244, "skill_path": "figure-first-paper-reader" }, "content_sha256": "8840646e7572a6b53f8769c4fac323816a127f726b77000183e0b62a753e55cb" }, { "id": "figure-legend-gen", "version": "1.0.1", "display_name": "figure-legend-gen", "summary": "Generate standardized figure legends for scientific figures and charts; use when preparing publication-ready legends that summarize design, variables, sample size, and key statistical notes.", "category": "Academic Writing", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Academic Writing/figure-legend-gen" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 91, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Academic%20Writing/figure-legend-gen/eval_report_figure-legend-gen_result.json", "evaluated_on": "2026-06-15", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 98, "max_score": 100 }, "dynamic_score": { "score": 86.6, "max_score": 100 } }, "release": { "path": "releases/figure-legend-gen/1.0.1.json", "sha256": "fbe5f0934e03c361c865508ac4675f9e957205aa1a5cf91b4be45b5a0bc499f7" }, "artifact": { "path": "shards/96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b.zip", "sha256": "96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b", "bytes": 66876850, "skill_path": "figure-legend-gen" }, "content_sha256": "6042d0f1b748795c3cd7df4b0eead326112ef70f4e422cefd6b6186f2e35d215" }, { "id": "figure-legend-writer", "version": "1.0.1", "display_name": "figure-legend-writer", "summary": "Writes complete, publication-grade figure legends that can stand on their own. Use when writing or revising figure legends for any scientific figure — bar charts, line graphs, scatter plots, box plots, heatmaps, survival curves, flow cytometry plots, western blots, microscopy images, or schematic diagrams. Also triggers on \"write a figure legend for\", \"help me describe this figure\", \"my figure needs a legend\", \"write Figure 1 legend\", or \"what should a figure legend include\".", "category": "Academic Writing", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "awesome-med-research-skills/Academic Writing/figure-legend-writer" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 88, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/awesome-med-research-skills/Academic%20Writing/figure-legend-writer/eval_report_figure-legend-writer_result.json", "evaluated_on": "2026-04-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 94, "max_score": 100 }, "dynamic_score": { "score": 83.7, "max_score": 100 } }, "release": { "path": "releases/figure-legend-writer/1.0.1.json", "sha256": "a935de9935ac50afa45c67efb57e99e7dffa8bdeed1a27a0f8392b87b69ecb34" }, "artifact": { "path": "shards/96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b.zip", "sha256": "96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b", "bytes": 66876850, "skill_path": "figure-legend-writer" }, "content_sha256": "6b289ef483f82a9b8163e17828114dd80044c460282ef5e15f40a49660c1b514" }, { "id": "figure-reference-checker", "version": "1.0.1", "display_name": "figure-reference-checker", "summary": "Use figure reference checker for academic writing workflows that need structured execution, explicit assumptions, and clear output boundaries.", "category": "Academic Writing", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Academic Writing/figure-reference-checker" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 92, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Academic%20Writing/figure-reference-checker/eval_report_figure-reference-checker_result.json", "evaluated_on": "2026-06-15", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 99, "max_score": 100 }, "dynamic_score": { "score": 86.6, "max_score": 100 } }, "release": { "path": "releases/figure-reference-checker/1.0.1.json", "sha256": "53b839ee8bdb966a8c6521bea109e049f6345c32302e2b201a8d7b925f7c8ef2" }, "artifact": { "path": "shards/96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b.zip", "sha256": "96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b", "bytes": 66876850, "skill_path": "figure-reference-checker" }, "content_sha256": "7ec736220fd74abaa0c1b23ac06a40838d001dd45fb0dc08ecd4c7d828f42244" }, { "id": "file-security-toolkit", "version": "1.0.0", "display_name": "file-security-toolkit", "summary": "Encrypt/decrypt local files, redact sensitive information in documents, and validate password strength when handling private data or preparing files for sharing.", "category": "Other", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Other/file-security-toolkit" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 86, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Other/file-security-toolkit/file-security-toolkit_audit_result_v1.json", "evaluated_on": "2026-03-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 75, "max_score": 100 }, "dynamic_score": { "score": 93.6, "max_score": 100 } }, "release": { "path": "releases/file-security-toolkit/1.0.0.json", "sha256": "37a2578ce4f62b6690ae4f73a62224b3713d11fb9b053c7f33c6ef0e97ba6ea6" }, "artifact": { "path": "shards/fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0.zip", "sha256": "fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0", "bytes": 1383584, "skill_path": "file-security-toolkit" }, "content_sha256": "b63bd7b8084292bc192e233232ed107efea117144e2394c75904ce0ed6f7bdeb" }, { "id": "find-paper-references", "version": "1.0.0", "display_name": "find-paper-references", "summary": "Automatically find references for academic paper Markdown files. Reads full paper text, identifies each knowledge point requiring citation (epidemiological data, mechanism descriptions, existing research conclusions, etc.), searches PubMed for 3-5 most relevant articles per kn...", "category": "Academic Writing", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Academic Writing/find-paper-references" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 88, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Academic%20Writing/find-paper-references/eval_report_find-paper-references_result.json", "evaluated_on": "2026-06-15", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 89, "max_score": 100 }, "dynamic_score": { "score": 86.6, "max_score": 100 } }, "release": { "path": "releases/find-paper-references/1.0.0.json", "sha256": "a086b95ffaeba614274043a14c1b658c28a910385cb1ab4130dba4ca37914b9e" }, "artifact": { "path": "shards/1ea735cead62d521282de0bf8a672a95515d73332ea4fa7d5d2ccf72eed45995.zip", "sha256": "1ea735cead62d521282de0bf8a672a95515d73332ea4fa7d5d2ccf72eed45995", "bytes": 342914, "skill_path": "find-paper-references" }, "content_sha256": "c3a06aaae7009ee6245ee2d60c85fa098f5def53cc3ed3f3d6dcd4921e8ec4f1" }, { "id": "flowio", "version": "1.0.1", "display_name": "flowio", "summary": "Parse Flow Cytometry Standard (FCS) files v2.0–3.1 and extract events/metadata for preprocessing workflows (e.g., when you need NumPy arrays, channel info, or CSV/DataFrame export from cytometry files).", "category": "Data Analysis", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Data Analysis/flowio" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 86, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Data%20Analysis/flowio/flowio_audit_result_v1.json", "evaluated_on": "2026-03-23", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 85, "max_score": 100 }, "dynamic_score": { "score": 86.6, "max_score": 100 } }, "release": { "path": "releases/flowio/1.0.1.json", "sha256": "91bcefa90a4f3213fc3dcdc185a892341cc2e89a5b69d4c926a76c9c0fe7a32d" }, "artifact": { "path": "shards/96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b.zip", "sha256": "96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b", "bytes": 66876850, "skill_path": "flowio" }, "content_sha256": "f3b273e2934fead7faafdbabea1db40b975b1863e347c397b427dc41f0dfe834" }, { "id": "fulltext-fetcher", "version": "1.0.0", "display_name": "fulltext-fetcher", "summary": "Fetch and save the original HTML of scientific literature webpages when given a URL, DOI, or PubMed PMID (triggered when you need archival-grade page HTML for downstream parsing or review).", "category": "Other", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Other/fulltext-fetcher" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 90, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Other/fulltext-fetcher/fulltext-fetcher_audit_result_v2.json", "evaluated_on": "2026-03-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 83, "max_score": 100 }, "dynamic_score": { "score": 94.6, "max_score": 100 } }, "release": { "path": "releases/fulltext-fetcher/1.0.0.json", "sha256": "b822567f68206f3d3174d8606a01244b10aa7233b5b20b9701e14b3348f75743" }, "artifact": { "path": "shards/fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0.zip", "sha256": "fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0", "bytes": 1383584, "skill_path": "fulltext-fetcher" }, "content_sha256": "5bec4fbe1457e14a3ef489841397d4abfa47129dd1fab5fd1bef94e32893af69" }, { "id": "gene-database", "version": "1.0.0", "display_name": "gene-database", "summary": "Query the NCBI Gene database via E-utilities and the NCBI Datasets API; use it when you need to search genes by symbol/ID and retrieve annotations (RefSeq, GO, location, phenotype) for single or batch gene lists.", "category": "Evidence Insight", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Evidence Insight/gene-database" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 92, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Evidence%20Insight/gene-database/gene-database_audit_result_v1.json", "evaluated_on": "2026-03-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 87, "max_score": 100 }, "dynamic_score": { "score": 96, "max_score": 100 } }, "release": { "path": "releases/gene-database/1.0.0.json", "sha256": "1744a69c5562d44b5cec02155ed3248450853dfedb010a3ec152284734a7eed1" }, "artifact": { "path": "shards/363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb.zip", "sha256": "363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb", "bytes": 1471244, "skill_path": "gene-database" }, "content_sha256": "0318f905947436035ad37ae5fd805c8a2fa7cb148917472c8ad0a92aefa63412" }, { "id": "gene-info", "version": "1.0.0", "display_name": "gene-info", "summary": "Retrieves comprehensive gene information including PubMed publication counts, NCBI summaries, and Ensembl transcript data. Supports batch processing and file input. Invoke when the user asks for gene details, publication statistics, or needs to analyze a list of genes.", "category": "Evidence Insight", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Evidence Insight/gene-info" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 91, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Evidence%20Insight/gene-info/gene-info_audit_result_v2.json", "evaluated_on": "2026-03-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 83, "max_score": 100 }, "dynamic_score": { "score": 96, "max_score": 100 } }, "release": { "path": "releases/gene-info/1.0.0.json", "sha256": "bd383d8e4500b6c2577db9acafb7da858b56f60d7dac901a24d9ec9b00aaf0d0" }, "artifact": { "path": "shards/363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb.zip", "sha256": "363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb", "bytes": 1471244, "skill_path": "gene-info" }, "content_sha256": "63bea6ced69b7bb3629573e635d5d7d4e1123dfa340be207504cba322d52c23c" }, { "id": "gene-protein-expression-matrix-normalization", "version": "1.0.1", "display_name": "gene-protein-expression-matrix-normalization", "summary": "Use when normalizing bulk gene or protein expression matrices with log2 transform, z-score standardization, or min-max scaling before downstream visualization or exploratory analysis. NOT for count-model normalization such as TPM/DESeq2 size factors, batch correction, or single-cell preprocessing.", "category": "Data Analysis", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "awesome-med-research-skills/Data Analysis/gene-protein-expression-matrix-normalization" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "Unknown", "evaluation": { "kind": "upstream-self-assessment", "score": 93, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/awesome-med-research-skills/Data%20Analysis/gene-protein-expression-matrix-normalization/eval_report_gene-protein-expression-matrix-normalization_result.json", "evaluated_on": "2026-05-08", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 93, "max_score": 100 }, "dynamic_score": { "score": 92.2, "max_score": 100 } }, "release": { "path": "releases/gene-protein-expression-matrix-normalization/1.0.1.json", "sha256": "facaf3727bd1ed6b8bfb781a307fd51b0a0d2323b9b12b3b3caaa8a74238b916" }, "artifact": { "path": "shards/96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b.zip", "sha256": "96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b", "bytes": 66876850, "skill_path": "gene-protein-expression-matrix-normalization" }, "content_sha256": "15d6f8024aca1a96ffe1cf586d883335b741daea917f73871ad61fc1551e5313" }, { "id": "gene-structure-mapper", "version": "1.0.1", "display_name": "gene-structure-mapper", "summary": "Use gene structure mapper for data analysis workflows that need structured execution, explicit assumptions, and clear output boundaries.", "category": "Data Analysis", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Data Analysis/gene-structure-mapper" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 86, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Data%20Analysis/gene-structure-mapper/gene-structure-mapper_audit_result_v2.json", "evaluated_on": "2026-03-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 83, "max_score": 100 }, "dynamic_score": { "score": 87.2, "max_score": 100 } }, "release": { "path": "releases/gene-structure-mapper/1.0.1.json", "sha256": "3e937340ceae0ae9c19e12df245094b6d4197191b5d20e8d3ec1e0dca40b0ecc" }, "artifact": { "path": "shards/96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b.zip", "sha256": "96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b", "bytes": 66876850, "skill_path": "gene-structure-mapper" }, "content_sha256": "6867b8d22b2e5e8fdd22671e512bd6a238f70ef060adea48091b939390d18458" }, { "id": "generic-phenotype-scoring-research-planner", "version": "1.0.0", "display_name": "generic-phenotype-scoring-research-planner", "summary": "Generates complete phenotype-scoring bioinformatics research designs for any disease context and any user-defined phenotype, pathway, process, signature, or molecular program. Use when a study centers on gene-set or feature-set definition, intersection with DEGs or candidate features, phenotype scoring, feature selection, diagnostic or stratification assessment, immune or cellular-resolution interpretation, network analysis, and optional orthogonal validation. Covers five study patterns (signature discovery, phenotype scoring, feature selection, immune/cellular interpretation, multi-layer validation) and always outputs Lite / Standard / Advanced / Publication+ with a recommended primary plan, stepwise workflow, figure plan, validation hierarchy, minimal executable version, publication upgrade path, and strictly verified literature retrieval.", "category": "Protocol Design", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "awesome-med-research-skills/Protocol Design/generic-phenotype-scoring-research-planner" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 89, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/awesome-med-research-skills/Protocol%20Design/generic-phenotype-scoring-research-planner/eval_report_generic-phenotype-scoring-research-planner_result.json", "evaluated_on": "2026-04-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 91, "max_score": 100 }, "dynamic_score": { "score": 87.7, "max_score": 100 } }, "release": { "path": "releases/generic-phenotype-scoring-research-planner/1.0.0.json", "sha256": "73111ed15f028b1b93bac10b9595470ba4b7a1a21d6d38a3275bec7dfb2440ae" }, "artifact": { "path": "shards/fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0.zip", "sha256": "fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0", "bytes": 1383584, "skill_path": "generic-phenotype-scoring-research-planner" }, "content_sha256": "678e5cc15a9ea137261ed1a623480bf6372436c085afc266a82e90f31a0fc225" }, { "id": "geniml", "version": "1.0.1", "display_name": "geniml", "summary": "Machine learning toolkit for genomic interval (BED) data; use it when you need to tokenize BED collections and train embeddings for regions/cells/labels, build consensus peak universes, or run similarity search and downstream ML on chromatin accessibility datasets.", "category": "Data Analysis", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Data Analysis/geniml" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 86, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Data%20Analysis/geniml/geniml_audit_result_v1.json", "evaluated_on": "2026-03-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 84, "max_score": 100 }, "dynamic_score": { "score": 86.6, "max_score": 100 } }, "release": { "path": "releases/geniml/1.0.1.json", "sha256": "88fe4994a75cbcfd63b8f55b7e50391b1ef084e240f712653d2e0c675f772d2c" }, "artifact": { "path": "shards/96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b.zip", "sha256": "96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b", "bytes": 66876850, "skill_path": "geniml" }, "content_sha256": "f3ba57b6e8ee3e77936f72aaba5ba83174f7e1c1da1192e9102eb34b14639c0e" }, { "id": "genmol-nim", "version": "1.0.0", "display_name": "genmol-nim", "summary": "Generate novel drug-like molecules using the GenMol NIM microservice. Use for de novo generation, scaffold decoration, motif extension, lead optimization, SAFE notation, QED or LogP ranking, hosted NVIDIA API calls, or local Docker deployment. GenMol takes SAFE notation in the smiles field, not ordinary SMILES.\n", "category": "Data Analysis", "source": { "repository": "https://github.com/NVIDIA-BioNeMo/bionemo-agent-toolkit", "commit": "0e67a612e4045f007e38fa77adc8f3ebfc5616b6", "path": "skills/bionemo-agent-toolkit/skills/genmol-nim" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "license": "Apache-2.0 AND CC-BY-4.0", "release": { "path": "releases/genmol-nim/1.0.0.json", "sha256": "878441b130c4e7e33efd8c21e546076f1fbdf459a2118afdc31ce000498be2f3" }, "artifact": { "path": "shards/d9eadedf27584c96257c396b3554a2f479383e134ebaf455716ea80dd4f8de94.zip", "sha256": "d9eadedf27584c96257c396b3554a2f479383e134ebaf455716ea80dd4f8de94", "bytes": 1161536, "skill_path": "genmol-nim" }, "content_sha256": "dc889571c47b9c7f660fdd8005e85365c8db65e49e0049d9cc178b1b8d9a6167" }, { "id": "genomics-workflow-acceleration", "version": "1.0.0", "display_name": "genomics-workflow-acceleration", "summary": "Use when accelerating existing genomics workflows with NVIDIA Parabricks, improving runtime or price/performance, converting pipeline steps to GPUs, or comparing CPU and GPU workflow outputs. Adds optional GPU steps in-place with runtime toggles (default off). Do NOT use for individual pbrun command routing — use parabricks.", "category": "Protocol Design", "source": { "repository": "https://github.com/NVIDIA-BioNeMo/bionemo-agent-toolkit", "commit": "0e67a612e4045f007e38fa77adc8f3ebfc5616b6", "path": "skills/bionemo-agent-toolkit/skills/genomics-workflow-acceleration" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "license": "CC-BY-4.0 AND Apache-2.0", "release": { "path": "releases/genomics-workflow-acceleration/1.0.0.json", "sha256": "08890d00bb716285102c1ef9bdc2fc3b2f4cce488e3e896cc07697a01232b0db" }, "artifact": { "path": "shards/d9eadedf27584c96257c396b3554a2f479383e134ebaf455716ea80dd4f8de94.zip", "sha256": "d9eadedf27584c96257c396b3554a2f479383e134ebaf455716ea80dd4f8de94", "bytes": 1161536, "skill_path": "genomics-workflow-acceleration" }, "content_sha256": "3e911da2991c57d20ec073763bff72189e5b5bf799bea8fa97748a431f0b8198" }, { "id": "geo-search-api", "version": "1.0.0", "display_name": "geo-search-api", "summary": "Search for gene expression DataSets and Profiles in the NCBI GEO database. Use this skill when the user wants to find microarray, RNA-seq, or other genomic data by keywords, organism, author, or specific fields.", "category": "Evidence Insight", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Evidence Insight/geo-search-api" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 87, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Evidence%20Insight/geo-search-api/geo-search-api_audit_result_v2.json", "evaluated_on": "2026-03-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 88, "max_score": 100 }, "dynamic_score": { "score": 86.4, "max_score": 100 } }, "release": { "path": "releases/geo-search-api/1.0.0.json", "sha256": "a8daf61ba9ec74a4d4a9e8f418be0413a0aa152f3eb146b39ec6d29511233432" }, "artifact": { "path": "shards/1d2612a702320846b10cda57f9f37ce17f5e19186095f2fe96bfa6e44eb077e8.zip", "sha256": "1d2612a702320846b10cda57f9f37ce17f5e19186095f2fe96bfa6e44eb077e8", "bytes": 234009, "skill_path": "geo-search-api" }, "content_sha256": "8e8b81588931a685f0a1dd1898336ac160e95060edea798c53e0da13163c0beb" }, { "id": "geopandas", "version": "1.0.1", "display_name": "geopandas", "summary": "A Python library for reading, writing, and analyzing geospatial vector data; use it when you need spatial operations (buffer/overlay/join), CRS reprojection, or map visualization on formats like Shapefile/GeoJSON/GeoPackage or PostGIS.", "category": "Data Analysis", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Data Analysis/geopandas" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 86, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Data%20Analysis/geopandas/geopandas_audit_result_v2.json", "evaluated_on": "2026-03-23", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 85, "max_score": 100 }, "dynamic_score": { "score": 86.6, "max_score": 100 } }, "release": { "path": "releases/geopandas/1.0.1.json", "sha256": "cf872e5d4f146b44ca9b3839bea241cf175ff072eeea69e59982dba172369a9e" }, "artifact": { "path": "shards/96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b.zip", "sha256": "96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b", "bytes": 66876850, "skill_path": "geopandas" }, "content_sha256": "4e2a5fa03e93a48aa04fb27f4ca2b22579778e3d25b1ecf2562e941a3bbfe169" }, { "id": "get-available-resources", "version": "1.0.0", "display_name": "Compute Resource Detection", "summary": "This skill should be used at the start of any computationally intensive scientific task to detect and report available system resources (CPU cores, GPUs, memory, disk space). It creates a JSON file with resource information and strategic recommendations that inform computational approach decisions such as whether to use parallel processing (joblib, multiprocessing), out-of-core computing (Dask, Zarr), GPU acceleration (PyTorch, JAX), or memory-efficient strategies. Use this skill before running analyses, training models, processing large datasets, or any task where resource constraints matter.", "category": "Other", "source": { "repository": "https://github.com/K-Dense-AI/scientific-agent-skills", "commit": "f4b05302fa4e2c440e3853cd094f4ddeb78ffc7f", "path": "scientific-skills/get-available-resources" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "K-Dense Inc." } ], "license": "MIT license", "release": { "path": "releases/get-available-resources/1.0.0.json", "sha256": "89b27cab4fb324e4e35873056716385f04a2971f02598e6542e0c6c2fe7d07bf" }, "artifact": { "path": "shards/99a60df6cc99b54f308f288ccebccde256dcc3925503b3cde49ceadd90857ec9.zip", "sha256": "99a60df6cc99b54f308f288ccebccde256dcc3925503b3cde49ceadd90857ec9", "bytes": 18851, "skill_path": "get-available-resources" }, "content_sha256": "8cdf54661d79283177adb06c6685a70e9c2629c28b6fe26736a811a076574334" }, { "id": "gget", "version": "1.0.0", "display_name": "gget", "summary": "Unified CLI/Python interface for querying genomic, proteomic, structure, and expression data across 20+ bioinformatics databases; use when you need fast, scriptable retrieval by gene/protein IDs or keywords.", "category": "Evidence Insight", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Evidence Insight/gget" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 85, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Evidence%20Insight/gget/gget_audit_result_v1.json", "evaluated_on": "2026-03-23", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 85, "max_score": 100 }, "dynamic_score": { "score": 85.6, "max_score": 100 } }, "release": { "path": "releases/gget/1.0.0.json", "sha256": "8215d2934edd924eeeeba71af074d273e416e6efd0d801533d92e93d5d38ae84" }, "artifact": { "path": "shards/363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb.zip", "sha256": "363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb", "bytes": 1471244, "skill_path": "gget" }, "content_sha256": "9fc1459e7c744d45a8ebc89e85b9178da00bc1e15f1cea7d4b896bdb76db2575" }, { "id": "grant-budget-justification", "version": "1.0.1", "display_name": "grant-budget-justification", "summary": "Use grant budget justification for academic writing workflows that need structured execution, explicit assumptions, and clear output boundaries.", "category": "Academic Writing", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Academic Writing/grant-budget-justification" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 91, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Academic%20Writing/grant-budget-justification/eval_report_grant-budget-justification_result.json", "evaluated_on": "2026-06-15", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 98, "max_score": 100 }, "dynamic_score": { "score": 86.6, "max_score": 100 } }, "release": { "path": "releases/grant-budget-justification/1.0.1.json", "sha256": "1893378e6db5678be231bdc32531f2df4b2af5d91fd82961eb3b101a4d6a7bb9" }, "artifact": { "path": "shards/96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b.zip", "sha256": "96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b", "bytes": 66876850, "skill_path": "grant-budget-justification" }, "content_sha256": "64811a6eec3b7c26b6759a0c965d5b8835d20d4a9d4eceefe979509da6dd8eb1" }, { "id": "grant-gantt-chart-gen", "version": "1.0.0", "display_name": "grant-gantt-chart-gen", "summary": "Use grant gantt chart gen for evidence insight workflows that need structured execution, explicit assumptions, and clear output boundaries.", "category": "Evidence Insight", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Evidence Insight/grant-gantt-chart-gen" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 85, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Evidence%20Insight/grant-gantt-chart-gen/grant-gantt-chart-gen_audit_result_v2.json", "evaluated_on": "2026-03-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 88, "max_score": 100 }, "dynamic_score": { "score": 83.6, "max_score": 100 } }, "release": { "path": "releases/grant-gantt-chart-gen/1.0.0.json", "sha256": "930b2f7533a19ad78439575454e27269203b5c3e6e98cf905441592960417b9c" }, "artifact": { "path": "shards/363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb.zip", "sha256": "363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb", "bytes": 1471244, "skill_path": "grant-gantt-chart-gen" }, "content_sha256": "759586d08b01c70bcc3932835837abc5915ba6a62a9fe54e950f4081e70af25f" }, { "id": "grant-mock-reviewer", "version": "1.0.1", "display_name": "grant-mock-reviewer", "summary": "Simulate structured grant peer review for biomedical proposals; use when stress-testing significance, innovation, approach, feasibility, and reviewer-facing weaknesses before submission.", "category": "Academic Writing", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Academic Writing/grant-mock-reviewer" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 90, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Academic%20Writing/grant-mock-reviewer/eval_report_grant-mock-reviewer_result.json", "evaluated_on": "2026-06-15", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 96, "max_score": 100 }, "dynamic_score": { "score": 86.6, "max_score": 100 } }, "release": { "path": "releases/grant-mock-reviewer/1.0.1.json", "sha256": "818a6208c304e0042245082019132ed3343360b1ccf608dc3a1baf60da1dc483" }, "artifact": { "path": "shards/96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b.zip", "sha256": "96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b", "bytes": 66876850, "skill_path": "grant-mock-reviewer" }, "content_sha256": "1f36b257b2f6e39530c834627566381ffa1437d90b1a98873ff5b22eda862586" }, { "id": "grant-proposal-assistant", "version": "1.0.1", "display_name": "grant-proposal-assistant", "summary": "Assist with biomedical grant proposal drafting, structure, and revision; use when preparing fundable proposal sections, aligning aims and methods, or improving reviewer-facing clarity.", "category": "Academic Writing", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Academic Writing/grant-proposal-assistant" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 91, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Academic%20Writing/grant-proposal-assistant/eval_report_grant-proposal-assistant_result.json", "evaluated_on": "2026-06-15", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 97, "max_score": 100 }, "dynamic_score": { "score": 86.6, "max_score": 100 } }, "release": { "path": "releases/grant-proposal-assistant/1.0.1.json", "sha256": "c274669f610c743dfdb8afbcb025cd1f18203caee8c595f8cc397f1996aafa17" }, "artifact": { "path": "shards/96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b.zip", "sha256": "96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b", "bytes": 66876850, "skill_path": "grant-proposal-assistant" }, "content_sha256": "e588a1a15c987fdcd95ebb294b8e1f8d3ec89f5e5d0df31c28fe2d1f8446cb96" }, { "id": "grant-specific-aims-writer", "version": "1.0.1", "display_name": "grant-specific-aims-writer", "summary": "Writes Specific Aims pages for grant applications. Use when drafting or revising the Specific Aims page (NIH R01/R21/R03), NSF Project Summary, or equivalent for any major funding agency. Also triggers on \"write my specific aims\", \"help me draft specific aims for NIH\", \"what should a specific aims page include\", \"NSF project summary\", \"write my grant aims\", or \"how do I structure an R01\".", "category": "Academic Writing", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "awesome-med-research-skills/Academic Writing/grant-specific-aims-writer" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 86, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/awesome-med-research-skills/Academic%20Writing/grant-specific-aims-writer/eval_report_grant-specific-aims-writer_result.json", "evaluated_on": "2026-04-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 92, "max_score": 100 }, "dynamic_score": { "score": 82.1, "max_score": 100 } }, "release": { "path": "releases/grant-specific-aims-writer/1.0.1.json", "sha256": "257c61dae03291394a71a57f6ec99e893d185d9cf533f470c76b9b58bb0378b8" }, "artifact": { "path": "shards/96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b.zip", "sha256": "96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b", "bytes": 66876850, "skill_path": "grant-specific-aims-writer" }, "content_sha256": "10ad884bda86076f56e83b7bf74fdda88abc090ccaec356f972eaeb0d9fe3fae" }, { "id": "graphical-abstract-generator", "version": "1.0.1", "display_name": "graphical-abstract-generator", "summary": "Converts a biomedical study storyline into a graphical abstract and, when direct image capability is available, generates the graphical abstract directly; otherwise it falls back to prompts, Mermaid flowcharts, or designer-facing briefs.", "category": "Academic Writing", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "awesome-med-research-skills/Academic Writing/graphical-abstract-generator" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 86, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/awesome-med-research-skills/Academic%20Writing/graphical-abstract-generator/eval_report_graphical-abstract-generator_result.json", "evaluated_on": "2026-04-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 92, "max_score": 100 }, "dynamic_score": { "score": 82.3, "max_score": 100 } }, "release": { "path": "releases/graphical-abstract-generator/1.0.1.json", "sha256": "0a80c93add7280d5bda07c709826de27502716dfd7e73971bd86cba026914e57" }, "artifact": { "path": "shards/96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b.zip", "sha256": "96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b", "bytes": 66876850, "skill_path": "graphical-abstract-generator" }, "content_sha256": "fcbe9c132a531af6334f1f5871ab6f4c6acf72eeabf22ec5ec2a7368af494948" }, { "id": "graphical-abstract-wizard", "version": "1.0.1", "display_name": "graphical-abstract-wizard", "summary": "Generate graphical abstract layout recommendations based on paper abstracts.", "category": "Data Analysis", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Data Analysis/graphical-abstract-wizard" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "Unknown", "evaluation": { "kind": "upstream-self-assessment", "score": 86, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Data%20Analysis/graphical-abstract-wizard/graphical-abstract-wizard_audit_result_v2.json", "evaluated_on": "2026-03-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 83, "max_score": 100 }, "dynamic_score": { "score": 87.2, "max_score": 100 } }, "release": { "path": "releases/graphical-abstract-wizard/1.0.1.json", "sha256": "b5b4832327dbc2878842d37a4063b2750990246d73d14db3c3b4c375f80e6f13" }, "artifact": { "path": "shards/96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b.zip", "sha256": "96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b", "bytes": 66876850, "skill_path": "graphical-abstract-wizard" }, "content_sha256": "e26ced03c5ff5b0f62a57c9be3818702fb97080e724de183694174c57e8da55e" }, { "id": "gsea", "version": "1.0.1", "display_name": "gsea", "summary": "Run GSEA on a ranked gene list and produce the enrichment table, running-score table, and enrichment plots.", "category": "Data Analysis", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "awesome-med-research-skills/Data Analysis/gsea" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "Unknown", "evaluation": { "kind": "upstream-self-assessment", "score": 90, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/awesome-med-research-skills/Data%20Analysis/gsea/eval_report_gsea_result.json", "evaluated_on": "2026-04-15", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 91, "max_score": 100 }, "dynamic_score": { "score": 89.2, "max_score": 100 } }, "release": { "path": "releases/gsea/1.0.1.json", "sha256": "da88352fcec46d5fd8194fd6638788a543c5583b476e3a03787cef2941883725" }, "artifact": { "path": "shards/96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b.zip", "sha256": "96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b", "bytes": 66876850, "skill_path": "gsea" }, "content_sha256": "39ffe092ff6760d41668691cc8fc94a9ff511e0a253acf353c5f9fad83978d2d" }, { "id": "gsva-analysis-and-visualization", "version": "1.0.1", "display_name": "gsva-analysis-and-visualization", "summary": "Use this skill to run GSVA or ssGSEA pathway-level differential analysis from a bulk expression matrix and a sample group file, then generate a heatmap from the saved GSVA result object. Trigger keywords: GSVA, ssGSEA, pathway enrichment, KEGG pathway analysis, MSigDB. NOT for: gene-level differential expression, single-cell analysis, methylation analysis, clinical diagnosis.", "category": "Data Analysis", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "awesome-med-research-skills/Data Analysis/gsva-analysis-and-visualization" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "license": "Unknown", "evaluation": { "kind": "upstream-self-assessment", "score": 95, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/awesome-med-research-skills/Data%20Analysis/gsva-analysis-and-visualization/eval_report_gsva-analysis-and-visualization_result.json", "evaluated_on": "2026-04-27", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 100, "max_score": 100 }, "dynamic_score": { "score": 92.1, "max_score": 100 } }, "release": { "path": "releases/gsva-analysis-and-visualization/1.0.1.json", "sha256": "cc997b2c24b1f50b754be747ec399d285658ae2a8eebe186fcd837a9290db0f7" }, "artifact": { "path": "shards/96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b.zip", "sha256": "96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b", "bytes": 66876850, "skill_path": "gsva-analysis-and-visualization" }, "content_sha256": "4830b37cd0f88d1c9653af15c843a0d2cbc02d3c10f793d1d846c023e0e4601d" }, { "id": "gtars", "version": "1.0.1", "display_name": "gtars", "summary": "A high-performance Rust toolkit (with Python bindings and a CLI) for genomic interval analysis; use it when you need fast overlap queries, coverage track generation, genomic tokenization for ML, reference sequence verification, or fragment processing.", "category": "Data Analysis", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Data Analysis/gtars" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 85, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Data%20Analysis/gtars/gtars_audit_result_v1.json", "evaluated_on": "2026-03-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 83, "max_score": 100 }, "dynamic_score": { "score": 86.6, "max_score": 100 } }, "release": { "path": "releases/gtars/1.0.1.json", "sha256": "796c510d112e6852a6128f5d86375c8bb486b86ebac8c1882f96797c62518653" }, "artifact": { "path": "shards/96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b.zip", "sha256": "96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b", "bytes": 66876850, "skill_path": "gtars" }, "content_sha256": "d78fb0430fa7d05d5c266f4edd95d81da2a20b35fe75e66bcceea2ffc69aae94" }, { "id": "gwas-database", "version": "1.0.0", "display_name": "gwas-database", "summary": "Query NHGRI-EBI GWAS Catalog for SNP-trait associations. Search variants by rs ID, disease/trait, gene, retrieve p-values and summary statistics, for genetic epidemiology and polygenic risk scores.", "category": "Evidence Insight", "source": { "repository": "https://github.com/K-Dense-AI/scientific-agent-skills", "commit": "71add644263a56368f8680d68df504c6674dc1e5", "path": "scientific-skills/gwas-database" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "K-Dense Inc." } ], "license": "Unknown", "release": { "path": "releases/gwas-database/1.0.0.json", "sha256": "c9823d5043bb92bbc9363cd31cd81b25ff0737d643e1ddbfdcb2a1986a927511" }, "artifact": { "path": "shards/fcd3eaa3439234f967f6835adacfe5bd6e8cacef5cbe5da7a06ffa4dec4f4973.zip", "sha256": "fcd3eaa3439234f967f6835adacfe5bd6e8cacef5cbe5da7a06ffa4dec4f4973", "bytes": 110833, "skill_path": "gwas-database" }, "content_sha256": "32f1b79266f1e410d62e3c0ba8b50e7899ee6aebd63bad3d5955c14e13cd1b7d" }, { "id": "heatmap-beautifier", "version": "1.0.1", "display_name": "heatmap-beautifier", "summary": "Professional beautification tool for gene expression heatmaps, automatically adds clustering trees, color annotation tracks, and intelligently optimizes label layout.", "category": "Data Analysis", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Data Analysis/heatmap-beautifier" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 85, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Data%20Analysis/heatmap-beautifier/heatmap-beautifier_audit_result_v2.json", "evaluated_on": "2026-03-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 87, "max_score": 100 }, "dynamic_score": { "score": 84.2, "max_score": 100 } }, "release": { "path": "releases/heatmap-beautifier/1.0.1.json", "sha256": "5947b596e5507b8c38284497b00d1a40c867e88f5e22bae816622206ee73d6f3" }, "artifact": { "path": "shards/96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b.zip", "sha256": "96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b", "bytes": 66876850, "skill_path": "heatmap-beautifier" }, "content_sha256": "de71d2d40433baf8cd2ab2af2fb7b8b3922beeec6b5a2b84b08a30cc495a4767" }, { "id": "hgnc-api", "version": "1.0.0", "display_name": "hgnc-api", "summary": "Access the HGNC (HUGO Gene Nomenclature Committee) database to search for and retrieve gene information including symbols, names, IDs, and other metadata.", "category": "Evidence Insight", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Evidence Insight/hgnc-api" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 86, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Evidence%20Insight/hgnc-api/hgnc-api_audit_result_v1.json", "evaluated_on": "2026-03-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 72, "max_score": 100 }, "dynamic_score": { "score": 94.6, "max_score": 100 } }, "release": { "path": "releases/hgnc-api/1.0.0.json", "sha256": "e1c8b40e044dbce73a82cd390a3fe9cda1b2f4e12207dbcb039eb18273b7abaa" }, "artifact": { "path": "shards/363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb.zip", "sha256": "363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb", "bytes": 1471244, "skill_path": "hgnc-api" }, "content_sha256": "185d648f2df1e743e86a07786ad13813b2fe05ab0204f5a23aaef5d060edd5d1" }, { "id": "hierarchical-clustering-plot", "version": "1.0.1", "display_name": "hierarchical-clustering-plot", "summary": "Use when building a sample-level hierarchical clustering dendrogram from a bulk expression matrix and sample annotation table, especially for QC, batch inspection, or sample similarity assessment. Trigger keywords: hierarchical clustering, dendrogram, sample QC, batch inspection, sample similarity. NOT for: differential expression testing, gene clustering heatmaps, single-cell clustering workflows.", "category": "Data Analysis", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "awesome-med-research-skills/Data Analysis/hierarchical-clustering-plot" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "license": "Unknown", "release": { "path": "releases/hierarchical-clustering-plot/1.0.1.json", "sha256": "969df9752ef7bf94f2583c5988686e660cbad040ad7c9bc4b8046fb1c54d5499" }, "artifact": { "path": "shards/96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b.zip", "sha256": "96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b", "bytes": 66876850, "skill_path": "hierarchical-clustering-plot" }, "content_sha256": "7bf5b79e96575815ca15873e69ca169f36e11a4ff0c0dbcddb2ea9a1da1b0ebc" }, { "id": "high-value-paper-screener", "version": "1.0.0", "display_name": "high-value-paper-screener", "summary": "Quickly judges whether a biomedical paper is worth deep reading by screening for question fit, design quality, sample adequacy, methodological novelty, and reproducibility value.", "category": "Evidence Insight", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "awesome-med-research-skills/Evidence Insight/high-value-paper-screener" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 88, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/awesome-med-research-skills/Evidence%20Insight/high-value-paper-screener/eval_report_high-value-paper-screener_result.json", "evaluated_on": "2026-04-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 93, "max_score": 100 }, "dynamic_score": { "score": 84.3, "max_score": 100 } }, "release": { "path": "releases/high-value-paper-screener/1.0.0.json", "sha256": "191dadc17e7ccd41e252d3f44218e140153c40c8b9c2bbe6675d73de62b9341e" }, "artifact": { "path": "shards/363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb.zip", "sha256": "363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb", "bytes": 1471244, "skill_path": "high-value-paper-screener" }, "content_sha256": "dab586a6a6813934536121b8bd4cda0a0eac22fb146f1f54679686b98dfbc380" }, { "id": "hippocrates", "version": "1.0.0", "display_name": "hippocrates", "summary": "Evidence-based medical knowledge and research mentor. Trigger this skill when users ask any question related to medicine, clinical science, pharmacology, pathology, epidemiology, or health sciences. Covers: disease mechanisms, drug actions and interactions, differential diagnosis reasoning, clinical guideline interpretation, medical literature appraisal (including GRADE assessment), treatment comparisons, lab and imaging interpretation, public health analysis, and medical terminology. Even if the user doesn't explicitly say \"evidence-based\" or \"medical research,\" trigger this skill whenever the topic touches health, disease, drugs, or therapeutics. Part of the AIPOCH Medical Research Skill Hub.\n", "category": "Other", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Other/hippocrates" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 91, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Other/hippocrates/eval_report_hippocrates_result_v1.json", "evaluated_on": "2026-04-08", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 92, "max_score": 100 }, "dynamic_score": { "score": 90.3, "max_score": 100 } }, "release": { "path": "releases/hippocrates/1.0.0.json", "sha256": "6a25f3e326849e90cc483a42aa657f973b05ab391e89d16dcb0a03dacb38ca50" }, "artifact": { "path": "shards/1ea735cead62d521282de0bf8a672a95515d73332ea4fa7d5d2ccf72eed45995.zip", "sha256": "1ea735cead62d521282de0bf8a672a95515d73332ea4fa7d5d2ccf72eed45995", "bytes": 342914, "skill_path": "hippocrates" }, "content_sha256": "f93c3c38c08523b3a87a0d9192bccd23b9497a69e3b5c7398a777402e1ccc058" }, { "id": "histolab", "version": "1.0.1", "display_name": "histolab", "summary": "Digital pathology image processing toolkit for whole slide images (WSI). Supports WSI formats (SVS, TIFF, NDPI), tile-based analysis, and histological image preprocessing workflows.", "category": "Data Analysis", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Data Analysis/histolab" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 88, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Data%20Analysis/histolab/eval_report_histolab_result.json", "evaluated_on": "2026-06-15", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 91, "max_score": 100 }, "dynamic_score": { "score": 86.6, "max_score": 100 } }, "release": { "path": "releases/histolab/1.0.1.json", "sha256": "c6e4910c9b4aa214ab75cda9415c7fb6b9c676d983548c39b30ef142ea84fe8e" }, "artifact": { "path": "shards/96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b.zip", "sha256": "96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b", "bytes": 66876850, "skill_path": "histolab" }, "content_sha256": "4460dca931de9c73708a83de4676abdda97b5294960deb7abd6226cdb2411879" }, { "id": "hmdb-database", "version": "1.0.0", "display_name": "hmdb-database", "summary": "Access the Human Metabolome Database (HMDB) to search metabolites by name/structure/ID and extract chemical/biological/clinical fields when you need metabolomics research data or automated HMDB XML mining.", "category": "Evidence Insight", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Evidence Insight/hmdb-database" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 90, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Evidence%20Insight/hmdb-database/hmdb-database_audit_result_v1.json", "evaluated_on": "2026-03-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 87, "max_score": 100 }, "dynamic_score": { "score": 92.6, "max_score": 100 } }, "release": { "path": "releases/hmdb-database/1.0.0.json", "sha256": "967797d5cb1a4dc923403a045b394ffc4fb6514f7c9ce8312e142c59331fdd20" }, "artifact": { "path": "shards/363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb.zip", "sha256": "363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb", "bytes": 1471244, "skill_path": "hmdb-database" }, "content_sha256": "affee6842d0e13ac53fe7940df8de6c2cd5e274a7d4092105c8a33c954697269" }, { "id": "html-to-pdf", "version": "1.0.1", "display_name": "html-to-pdf", "summary": "Convert HTML files or URLs to high-fidelity PDFs using Puppeteer; auto-detects or forces RTL for Hebrew/Arabic when RTL content is present.", "category": "Other", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Other/html-to-pdf" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "Unknown", "evaluation": { "kind": "upstream-self-assessment", "score": 90, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Other/html-to-pdf/html-to-pdf_audit_result_v2.json", "evaluated_on": "2026-03-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 83, "max_score": 100 }, "dynamic_score": { "score": 94.6, "max_score": 100 } }, "release": { "path": "releases/html-to-pdf/1.0.1.json", "sha256": "c21ba3d60add82b4ad549b3d25c926e2ced1d88fe6d468345ea0bdac2db2859a" }, "artifact": { "path": "shards/96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b.zip", "sha256": "96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b", "bytes": 66876850, "skill_path": "html-to-pdf" }, "content_sha256": "f64212d3224ef8ed780ec5b748349f79459ca6cdcc4f835ec584a59e4d9803f2" }, { "id": "hypogenic", "version": "1.0.0", "display_name": "hypogenic", "summary": "Automated LLM-driven hypothesis generation and testing for tabular datasets; use when you need systematic exploration of empirical patterns (e.g., fraud detection, content analysis) and want to combine literature insights with data-driven hypothesis evaluation.", "category": "Protocol Design", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Protocol Design/hypogenic" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 87, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Protocol%20Design/hypogenic/hypogenic_audit_result_v1.json", "evaluated_on": "2026-03-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 84, "max_score": 100 }, "dynamic_score": { "score": 88.6, "max_score": 100 } }, "release": { "path": "releases/hypogenic/1.0.0.json", "sha256": "4320e1286d2c2683d4c2635f318a8b8abfb2941a19522a711aac61ca74294b14" }, "artifact": { "path": "shards/fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0.zip", "sha256": "fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0", "bytes": 1383584, "skill_path": "hypogenic" }, "content_sha256": "2c2d3d1cd8db1c404d8ad4dffe85d6c95b139385a5250766d2010fd6f0232771" }, { "id": "hypothesis-generation", "version": "1.0.0", "display_name": "hypothesis-generation", "summary": "Structured scientific hypothesis formulation from observations; 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Always use this skill when a user needs to translate a target population into operational screening rules tied to chart fields, time windows, tests, procedures, prior therapies, exclusions, and reviewable edge cases. Focus on protocol-stage precision, ambiguity reduction, auditability, and screening reproducibility rather than generic study design advice.", "category": "Protocol Design", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "awesome-med-research-skills/Protocol Design/inclusion-exclusion-criteria-builder" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 90, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/awesome-med-research-skills/Protocol%20Design/inclusion-exclusion-criteria-builder/eval_report_inclusion-exclusion-criteria-builder_result.json", "evaluated_on": "2026-04-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 92, "max_score": 100 }, "dynamic_score": { "score": 88.4, "max_score": 100 } }, "release": { "path": "releases/inclusion-exclusion-criteria-builder/1.0.0.json", "sha256": "775fc83e90b726ae62d2f8e2cb6cade8f218374a13c0872c744ecc2275c5d114" }, "artifact": { "path": "shards/fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0.zip", "sha256": "fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0", "bytes": 1383584, "skill_path": "inclusion-exclusion-criteria-builder" }, "content_sha256": "be41c14ed319ee29e997061a883fe7608a0b31853ba34f4ab138887a45a005d2" }, { "id": "inplasy-registration-helper", "version": "1.0.0", "display_name": "inplasy-registration-helper", "summary": "Assists researchers in generating INPLASY registration content for meta-analyses from a title and optional protocol. 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Use when you need to perform a documentation gap analysis, draft or update a Quality Manual, create required procedures/work instructions, build Medical Device Files (MDF), interpret ISO 13485 clauses, or identify missing documents for certification (often triggered by ISO 13485, QMS certification, FDA QMSR, EU MDR, or quality system documentation requests).", "category": "Other", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Other/iso-13485-certification" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "Unknown", "release": { "path": "releases/iso-certification/1.0.1.json", "sha256": "56e5b99e628aeca27b1b52c75612a48585fd0788e0590b315e295e59a461a675" }, "artifact": { "path": "shards/96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b.zip", "sha256": "96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b", "bytes": 66876850, "skill_path": "iso-certification" }, "content_sha256": "c36deed1d2433878a9c4351049ffe6bdc3a28538866b9cc729b853d4038f6020" }, { "id": "jaspar-api", "version": "1.0.0", "display_name": "jaspar-api", "summary": "Access JASPAR database for transcription factor binding profiles (matrices), collections, and species via REST API. Use when user wants to search for transcription factors, retrieve matrix details (PFM/PWM), infer profiles from protein sequences, or explore JASPAR collections.", "category": "Evidence Insight", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Evidence Insight/jaspar-api" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 88, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Evidence%20Insight/jaspar-api/jaspar-api_audit_result_v1.json", "evaluated_on": "2026-03-23", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 77, "max_score": 100 }, "dynamic_score": { "score": 95.6, "max_score": 100 } }, "release": { "path": "releases/jaspar-api/1.0.0.json", "sha256": "b64fe893a3d3a7adcd70e29746dc930da6f6e6b078ac92f9b8b1fd9902e8a054" }, "artifact": { "path": "shards/363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb.zip", "sha256": "363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb", "bytes": 1471244, "skill_path": "jaspar-api" }, "content_sha256": "59569d80c386e22fba27be6b454d40dfc80b7e322214a8707cdaabb544d0173a" }, { "id": "journal-club-presenter", "version": "1.0.1", "display_name": "journal-club-presenter", "summary": "Generate journal club slides with background, critique, and discussion.", "category": "Academic Writing", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Academic Writing/journal-club-presenter" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 85, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Academic%20Writing/journal-club-presenter/journal-club-presenter_audit_result_v2.json", "evaluated_on": "2026-03-23", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 88, "max_score": 100 }, "dynamic_score": { "score": 83.6, "max_score": 100 } }, "release": { "path": "releases/journal-club-presenter/1.0.1.json", "sha256": "4135dc87058406ca1dfd822f79a5dc2b5f8fd44106f920197d75fd426ee35099" }, "artifact": { "path": "shards/96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b.zip", "sha256": "96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b", "bytes": 66876850, "skill_path": "journal-club-presenter" }, "content_sha256": "74150836aac3b99fa7e643a18531e71ec54d3ec268b37f6bec3264235b619a5b" }, { "id": "journal-matchmaker", "version": "1.0.1", "display_name": "journal-matchmaker", "summary": "Analyzes academic paper abstracts to recommend optimal journals for submission, considering impact factors, scope alignment, and domain expertise.", "category": "Evidence Insight", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Evidence Insight/journal-matchmaker" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "Unknown", "evaluation": { "kind": "upstream-self-assessment", "score": 85, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Evidence%20Insight/journal-matchmaker/journal-matchmaker_audit_result_v2.json", "evaluated_on": "2026-03-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 88, "max_score": 100 }, "dynamic_score": { "score": 83.6, "max_score": 100 } }, "release": { "path": "releases/journal-matchmaker/1.0.1.json", "sha256": "d3e647615294927c28456604d7f295a3763b6f8a908310743e9221b2960d931f" }, "artifact": { "path": "shards/96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b.zip", "sha256": "96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b", "bytes": 66876850, "skill_path": "journal-matchmaker" }, "content_sha256": "cfb5a75c65677e6e90ca076f20ce08a95b39673c64ea60484226a9543ecb2994" }, { "id": "journal-recommender", "version": "1.0.0", "display_name": "journal-recommender", "summary": "Recommend academic journals based on manuscript topic, abstract, and impact factor expectations. Use when the user wants to find suitable journals for their research manuscript, especially when they provide a topic, abstract, and target Impact Factor.", "category": "Other", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Other/journal-recommender" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 87, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Other/journal-recommender/eval_report_journal-recommender_result.json", "evaluated_on": "2026-06-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 94, "max_score": 100 }, "dynamic_score": { "score": 83, "max_score": 100 } }, "release": { "path": "releases/journal-recommender/1.0.0.json", "sha256": "93a98212b769d205868ee3a2029470aac719ded8def9d79eff9525bd12c2160a" }, "artifact": { "path": "shards/fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0.zip", "sha256": "fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0", "bytes": 1383584, "skill_path": "journal-recommender" }, "content_sha256": "27ada483cef2f9ceb7357d32f9326e4164affc32ff0bdf01edb47a8a5b6b9c85" }, { "id": "journal-skills", "version": "1.0.0", "display_name": "journal-skills", "summary": "Recommends target journals for manuscript submission by analyzing the paper topic/abstract and the journal distribution of similar PubMed literature; use when users ask for journal recommendation/matching, submission strategy, PubMed search, or similar-literature statistics.", "category": "Evidence Insight", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Evidence Insight/journal-skills" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 91, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Evidence%20Insight/journal-skills/eval_report_journal-skills_result.json", "evaluated_on": "2026-06-15", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 99, "max_score": 100 }, "dynamic_score": { "score": 86.2, "max_score": 100 } }, "release": { "path": "releases/journal-skills/1.0.0.json", "sha256": "c8039bff3adc24ee8612112e79fea4eb25d88a67178fd4d7e950ee23dccf2ab9" }, "artifact": { "path": "shards/1ea735cead62d521282de0bf8a672a95515d73332ea4fa7d5d2ccf72eed45995.zip", "sha256": "1ea735cead62d521282de0bf8a672a95515d73332ea4fa7d5d2ccf72eed45995", "bytes": 342914, "skill_path": "journal-skills" }, "content_sha256": "6fa09cc90691b0eb993ea576119943d0d7b1ffbf900f57986428500a0142bd42" }, { "id": "kegg-api", "version": "1.0.1", "display_name": "kegg-api", "summary": "Access the KEGG database API to retrieve biological data (genes, pathways, compounds, drugs). Invoke when the user asks to search, list, or get details from KEGG.", "category": "Evidence Insight", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Evidence Insight/kegg-api" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "Unknown", "evaluation": { "kind": "upstream-self-assessment", "score": 87, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Evidence%20Insight/kegg-api/kegg-api_audit_result_v2.json", "evaluated_on": "2026-03-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 88, "max_score": 100 }, "dynamic_score": { "score": 86.4, "max_score": 100 } }, "release": { "path": "releases/kegg-api/1.0.1.json", "sha256": "b4e6195e4749eb0d274dfec2ba3f1227ac6f27063fbb402a78bd76857edbd27c" }, "artifact": { "path": "shards/96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b.zip", "sha256": "96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b", "bytes": 66876850, "skill_path": "kegg-api" }, "content_sha256": "e33047474198f6431cc2e34fc7d046a2b6c6baba6d28981518295644a08a0104" }, { "id": "kegg-database", "version": "1.0.0", "display_name": "kegg-database", "summary": "Direct access to KEGG via the REST API for academic-only pathway/gene/compound/drug queries; use when you need precise HTTP-level control or targeted KEGG ID mapping.", "category": "Evidence Insight", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Evidence Insight/kegg-database" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 89, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Evidence%20Insight/kegg-database/kegg-database_audit_result_v1.json", "evaluated_on": "2026-03-23", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 83, "max_score": 100 }, "dynamic_score": { "score": 92.6, "max_score": 100 } }, "release": { "path": "releases/kegg-database/1.0.0.json", "sha256": "000b989e82fafe1674331a085a4d27fdd4085f1ec7e9b9545bf24a71522c9154" }, "artifact": { "path": "shards/363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb.zip", "sha256": "363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb", "bytes": 1471244, "skill_path": "kegg-database" }, "content_sha256": "07ab3b07b6ed1cdac8003065bf3b1c8d5d7a149a319d88e3c7421a051ff4eefa" }, { "id": "kermt-add-cmim-pretrain", "version": "1.0.0", "display_name": "kermt-add-cmim-pretrain", "summary": "Convert a grover_base checkpoint (encoder-only or encoder + vocab heads) into a hybrid checkpoint by adding a randomly-initialized cMIM decoder + latent_dist, then continue pretraining on the user's corpus as hybrid (vocab + contrast). Effectively kermt-continue-pretrain with a one-time ckpt-conversion step prepended.", "category": "Data Analysis", "source": { "repository": "https://github.com/NVIDIA-BioNeMo/bionemo-agent-toolkit", "commit": "0e67a612e4045f007e38fa77adc8f3ebfc5616b6", "path": "skills/bionemo-agent-toolkit/skills/kermt-add-cmim-pretrain" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "license": "Apache-2.0", "release": { "path": "releases/kermt-add-cmim-pretrain/1.0.0.json", "sha256": "5421cb231356885d79156bc900b4b3c16a60d4725c28698aeab294c57e42a073" }, "artifact": { "path": "shards/d9eadedf27584c96257c396b3554a2f479383e134ebaf455716ea80dd4f8de94.zip", "sha256": "d9eadedf27584c96257c396b3554a2f479383e134ebaf455716ea80dd4f8de94", "bytes": 1161536, "skill_path": "kermt-add-cmim-pretrain" }, "content_sha256": "4c038fe30d33012ddd0412709a62c905c6c9b332b83ab8e72cea4fa34cf7e7c9" }, { "id": "kermt-continue-pretrain", "version": "1.0.0", "display_name": "kermt-continue-pretrain", "summary": "Continue pretraining from an existing KERMT checkpoint. The skill validates the user's checkpoint and pretrain CSV, prepares the data into shard/vocab/features form, then launches pretrain_ddp.py inside the kermt container (detached for long runs). Auto-dispatches `--pretrain_mode` based on the checkpoint type (grover_base vocab-only, cmim, or hybrid).", "category": "Data Analysis", "source": { "repository": "https://github.com/NVIDIA-BioNeMo/bionemo-agent-toolkit", "commit": "0e67a612e4045f007e38fa77adc8f3ebfc5616b6", "path": "skills/bionemo-agent-toolkit/skills/kermt-continue-pretrain" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "license": "Apache-2.0", "release": { "path": "releases/kermt-continue-pretrain/1.0.0.json", "sha256": "6683c2b726d8a114831bea0cb7277e2eff8e64bdbfc594a229e538ebb20ce6c4" }, "artifact": { "path": "shards/d9eadedf27584c96257c396b3554a2f479383e134ebaf455716ea80dd4f8de94.zip", "sha256": "d9eadedf27584c96257c396b3554a2f479383e134ebaf455716ea80dd4f8de94", "bytes": 1161536, "skill_path": "kermt-continue-pretrain" }, "content_sha256": "1f0a211935162a1fd7e2b9dde63b569efa27d0274a825224f8f41e76fa1c5eda" }, { "id": "kermt-embed", "version": "1.0.0", "display_name": "kermt-embed", "summary": "Extract per-molecule embeddings from any encoder-bearing KERMT checkpoint (grover_base / cmim / hybrid / finetuned). Writes one .npy per readout type (atom_from_atom, bond_from_atom, atom_from_bond, bond_from_bond) plus canonical_smiles.npy and validity.npy. Calls task/extract_embeddings.py (which featurizes SMILES on the fly — no pre-computed features needed).", "category": "Data Analysis", "source": { "repository": "https://github.com/NVIDIA-BioNeMo/bionemo-agent-toolkit", "commit": "0e67a612e4045f007e38fa77adc8f3ebfc5616b6", "path": "skills/bionemo-agent-toolkit/skills/kermt-embed" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "license": "Apache-2.0", "release": { "path": "releases/kermt-embed/1.0.0.json", "sha256": "47a8483f4f22cae58c675bdbae297782ddc4b424b9eb028cf19aa52434bdab5e" }, "artifact": { "path": "shards/d9eadedf27584c96257c396b3554a2f479383e134ebaf455716ea80dd4f8de94.zip", "sha256": "d9eadedf27584c96257c396b3554a2f479383e134ebaf455716ea80dd4f8de94", "bytes": 1161536, "skill_path": "kermt-embed" }, "content_sha256": "07b3aa664492b716dffc985105686f9d0e526de8da54c2bfc708f6f22279dbbb" }, { "id": "kermt-finetune", "version": "1.0.0", "display_name": "kermt-finetune", "summary": "Finetune a pretrained KERMT encoder on a labeled CSV. The skill validates the input checkpoint (must be a pretrain ckpt — grover_base / cmim / hybrid), validates the labeled CSV, prepares the data (clean + features + optional split), then launches main.py finetune inside the kermt container (detached for hours-scale runs). Hyperparameters come from agent/config/defaults_finetune.json with per-flag CLI override.", "category": "Data Analysis", "source": { "repository": "https://github.com/NVIDIA-BioNeMo/bionemo-agent-toolkit", "commit": "0e67a612e4045f007e38fa77adc8f3ebfc5616b6", "path": "skills/bionemo-agent-toolkit/skills/kermt-finetune" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "license": "Apache-2.0", "release": { "path": "releases/kermt-finetune/1.0.0.json", "sha256": "d4ba52073a4dab48463696092f96c769563b6109d02a1c7d5fe3b062ad291040" }, "artifact": { "path": "shards/d9eadedf27584c96257c396b3554a2f479383e134ebaf455716ea80dd4f8de94.zip", "sha256": "d9eadedf27584c96257c396b3554a2f479383e134ebaf455716ea80dd4f8de94", "bytes": 1161536, "skill_path": "kermt-finetune" }, "content_sha256": "5cb7b64ae8d697b0be798656dad355876354eccc66f93a766d2ea321bc2ccdf6" }, { "id": "kermt-infer", "version": "1.0.0", "display_name": "kermt-infer", "summary": "Run predictions with a finetuned KERMT checkpoint on a SMILES-only CSV. The skill validates that the input ckpt has task FFN heads (refuses pretrain ckpts with a redirect to kermt-finetune), validates the CSV, prepares the data (clean + rdkit_2d features), then launches main.py predict inside the kermt container (blocking, minutes-scale).", "category": "Data Analysis", "source": { "repository": "https://github.com/NVIDIA-BioNeMo/bionemo-agent-toolkit", "commit": "0e67a612e4045f007e38fa77adc8f3ebfc5616b6", "path": "skills/bionemo-agent-toolkit/skills/kermt-infer" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "license": "Apache-2.0", "release": { "path": "releases/kermt-infer/1.0.0.json", "sha256": "26e18b992f92e2a5d886eb42ade05fdfecc4ced2d2e3797266f06040e9d3aa8d" }, "artifact": { "path": "shards/d9eadedf27584c96257c396b3554a2f479383e134ebaf455716ea80dd4f8de94.zip", "sha256": "d9eadedf27584c96257c396b3554a2f479383e134ebaf455716ea80dd4f8de94", "bytes": 1161536, "skill_path": "kermt-infer" }, "content_sha256": "aca3791dd5e1cae6bd79e4f8f2c02ec804258264a427cc97cc6b1ebf9d8f81eb" }, { "id": "kermt-monitor", "version": "1.0.0", "display_name": "kermt-monitor", "summary": "Check progress for a detached KERMT run (pretrain, finetune, or any kermt_run_detached invocation). Reads run.json, queries docker for container state, tails the pretrain/finetune log, and parses progress lines (epoch, step, val loss).", "category": "Other", "source": { "repository": "https://github.com/NVIDIA-BioNeMo/bionemo-agent-toolkit", "commit": "0e67a612e4045f007e38fa77adc8f3ebfc5616b6", "path": "skills/bionemo-agent-toolkit/skills/kermt-monitor" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "license": "Apache-2.0", "release": { "path": "releases/kermt-monitor/1.0.0.json", "sha256": "bac216565d7b88961f72353f08d47076cdabddfbcf57d4af94ab135caddb0004" }, "artifact": { "path": "shards/d9eadedf27584c96257c396b3554a2f479383e134ebaf455716ea80dd4f8de94.zip", "sha256": "d9eadedf27584c96257c396b3554a2f479383e134ebaf455716ea80dd4f8de94", "bytes": 1161536, "skill_path": "kermt-monitor" }, "content_sha256": "afc368be876b69f388e89ca52e8aafe8b4af44ed5c75cbce6be1c85c8b85aa2e" }, { "id": "kermt-pretrain-scratch", "version": "1.0.0", "display_name": "kermt-pretrain-scratch", "summary": "Pretrain a fresh KERMT model from scratch on a user-provided corpus. Builds a new vocabulary from the corpus, instantiates the model architecture from defaults, and launches pretrain_ddp.py inside the kermt container (detached for long runs). Unlike kermt-continue-pretrain, no starting checkpoint is loaded — the model is randomly initialized.", "category": "Data Analysis", "source": { "repository": "https://github.com/NVIDIA-BioNeMo/bionemo-agent-toolkit", "commit": "0e67a612e4045f007e38fa77adc8f3ebfc5616b6", "path": "skills/bionemo-agent-toolkit/skills/kermt-pretrain-scratch" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "license": "Apache-2.0", "release": { "path": "releases/kermt-pretrain-scratch/1.0.0.json", "sha256": "6b9cce5a2578a6d87872667ad932ebce8a76342411713809b7fce49fb885c5d0" }, "artifact": { "path": "shards/d9eadedf27584c96257c396b3554a2f479383e134ebaf455716ea80dd4f8de94.zip", "sha256": "d9eadedf27584c96257c396b3554a2f479383e134ebaf455716ea80dd4f8de94", "bytes": 1161536, "skill_path": "kermt-pretrain-scratch" }, "content_sha256": "b7b8c3fa3ab60947129e9c8487d5eb4f7c1fc97375f467e31be8d4c97ece0774" }, { "id": "kermt-setup", "version": "1.0.0", "display_name": "kermt-setup", "summary": "Bootstrap the KERMT agent environment — verify host docker + nvidia-container-toolkit, build the kermt:latest image from the repo's Dockerfile if it doesn't yet exist, and run a GPU smoke test inside the container. Every other kermt-* skill depends on this; invoke it first.", "category": "Other", "source": { "repository": "https://github.com/NVIDIA-BioNeMo/bionemo-agent-toolkit", "commit": "0e67a612e4045f007e38fa77adc8f3ebfc5616b6", "path": "skills/bionemo-agent-toolkit/skills/kermt-setup" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "license": "Apache-2.0", "release": { "path": "releases/kermt-setup/1.0.0.json", "sha256": "b1b196c3d7f22a81cac9a986a9375cdce3b985e5b75d91f182b32037a396bb95" }, "artifact": { "path": "shards/d9eadedf27584c96257c396b3554a2f479383e134ebaf455716ea80dd4f8de94.zip", "sha256": "d9eadedf27584c96257c396b3554a2f479383e134ebaf455716ea80dd4f8de94", "bytes": 1161536, "skill_path": "kermt-setup" }, "content_sha256": "49d2a57aa53fc470c69bf2af3dfa1ea53204cbfb397712561ec3a3a6d7646250" }, { "id": "keyword-velocity-tracker", "version": "1.0.0", "display_name": "keyword-velocity-tracker", "summary": "Calculate literature growth velocity and acceleration to assess research.", "category": "Evidence Insight", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Evidence Insight/keyword-velocity-tracker" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 85, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Evidence%20Insight/keyword-velocity-tracker/keyword-velocity-tracker_audit_result_v2.json", "evaluated_on": "2026-03-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 88, "max_score": 100 }, "dynamic_score": { "score": 83.6, "max_score": 100 } }, "release": { "path": "releases/keyword-velocity-tracker/1.0.0.json", "sha256": "8874f9c723e22a137bdb9546d403d3c6eb64201f99bdb10b378875008a74831b" }, "artifact": { "path": "shards/363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb.zip", "sha256": "363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb", "bytes": 1471244, "skill_path": "keyword-velocity-tracker" }, "content_sha256": "3d7f6063374179a893d03e5a8dbe5674db4f89d50bf17d33fe019dbe92ebd6c8" }, { "id": "km-survival-curve", "version": "1.0.1", "display_name": "km-survival-curve", "summary": "Use when generating Kaplan-Meier survival curves from tabular survival data containing time, event status, and a precomputed risk group. Supports command-line parameter input, parameter validation, automatic time-unit handling, single-file PDF figure export, and session metadata capture.", "category": "Data Analysis", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "awesome-med-research-skills/Data Analysis/km-survival-curve" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "Unknown", "evaluation": { "kind": "upstream-self-assessment", "score": 92, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/awesome-med-research-skills/Data%20Analysis/km-survival-curve/eval_report_km-survival-curve_result.json", "evaluated_on": "2026-04-20", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 92, "max_score": 100 }, "dynamic_score": { "score": 91.2, "max_score": 100 } }, "release": { "path": "releases/km-survival-curve/1.0.1.json", "sha256": "ddea47f6de2e7bbda50dfbb4f79e0773f62e4ac7c5dd776e469e62a6ae237cea" }, "artifact": { "path": "shards/96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b.zip", "sha256": "96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b", "bytes": 66876850, "skill_path": "km-survival-curve" }, "content_sha256": "9b0e66156ba902b5444f80315588816ff41318c04d6fb63a108e7e7275260dad" }, { "id": "knn-imputation", "version": "1.0.1", "display_name": "knn-imputation", "summary": "Use when filtering genes with high missingness and then imputing missing values in a bulk expression matrix with group-aware KNN through DMwR2, where donor samples are restricted by one annotation column before imputation. For strata with 10 or fewer samples, the script falls back to row-wise direct filling with mean or median. NOT for: single-cell data, multi-column stratification, non-tabular inputs, network access, or interactive workflows.", "category": "Data Analysis", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "awesome-med-research-skills/Data Analysis/knn-imputation" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "license": "Unknown", "evaluation": { "kind": "upstream-self-assessment", "score": 95, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/awesome-med-research-skills/Data%20Analysis/knn-imputation/eval_report_knn-imputation_result.json", "evaluated_on": "2026-04-27", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 98, "max_score": 100 }, "dynamic_score": { "score": 92.7, "max_score": 100 } }, "release": { "path": "releases/knn-imputation/1.0.1.json", "sha256": "d6d3b096ad8e65485662218dc44e90fef223434adf126e03a115a6d3b867b3ef" }, "artifact": { "path": "shards/96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b.zip", "sha256": "96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b", "bytes": 66876850, "skill_path": "knn-imputation" }, "content_sha256": "60b30ca0e3d0ecd4631653fe4e32f50e4148316ac7f64bed63cdcf5922fcb790" }, { "id": "knowledge-base-search", "version": "1.0.0", "display_name": "knowledge-base-search", "summary": "Search and locate relevant content within a local knowledge base (files, indices, or PageIndex). Use when you need verifiable citations (file + page/paragraph) to support answers from local sources.", "category": "Other", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Other/knowledge-base-search" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 86, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Other/knowledge-base-search/knowledge-base-search_audit_result_v1.json", "evaluated_on": "2026-03-23", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 85, "max_score": 100 }, "dynamic_score": { "score": 86.2, "max_score": 100 } }, "release": { "path": "releases/knowledge-base-search/1.0.0.json", "sha256": "e9675bcd2305c8e8874b3cd1e6b70c4a4c2ce911090bae81ecc2be39d5086550" }, "artifact": { "path": "shards/1ea735cead62d521282de0bf8a672a95515d73332ea4fa7d5d2ccf72eed45995.zip", "sha256": "1ea735cead62d521282de0bf8a672a95515d73332ea4fa7d5d2ccf72eed45995", "bytes": 342914, "skill_path": "knowledge-base-search" }, "content_sha256": "f56451532430cca9c460696dfb5b8eb0d87abbe7ab31f38909c7d2a84639ca79" }, { "id": "lab-budget-forecaster", "version": "1.0.1", "display_name": "lab-budget-forecaster", "summary": "Use lab budget forecaster for data analysis workflows that need structured execution, explicit assumptions, and clear output boundaries.", "category": "Data Analysis", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Data Analysis/lab-budget-forecaster" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 86, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Data%20Analysis/lab-budget-forecaster/lab-budget-forecaster_audit_result_v2.json", "evaluated_on": "2026-03-23", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 83, "max_score": 100 }, "dynamic_score": { "score": 87.2, "max_score": 100 } }, "release": { "path": "releases/lab-budget-forecaster/1.0.1.json", "sha256": "f129d36edd2a19bc39726f831e4603bff2941e414bc8ce4216b22d5204b5e9d7" }, "artifact": { "path": "shards/96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b.zip", "sha256": "96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b", "bytes": 66876850, "skill_path": "lab-budget-forecaster" }, "content_sha256": "bde4b3e291fc3a3c07d9323cdca75752c06d203c3eb261cbfd576fa6366ea18c" }, { "id": "lab-inventory-predictor", "version": "1.0.0", "display_name": "lab-inventory-predictor", "summary": "Predict depletion time of critical lab reagents based on historical usage frequency, and automatically generate purchase alerts when stock falls below safety thresholds.", "category": "Other", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Other/lab-inventory-predictor" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 83, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Other/lab-inventory-predictor/lab-inventory-predictor_audit_result_v4.json", "evaluated_on": "2026-03-19", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 85, "max_score": 100 }, "dynamic_score": { "score": 81.2, "max_score": 100 } }, "release": { "path": "releases/lab-inventory-predictor/1.0.0.json", "sha256": "8b48850dce8eebd6e8e4a2414fafa4f631b4b47a3e3938c4464ec013086175c6" }, "artifact": { "path": "shards/fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0.zip", "sha256": "fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0", "bytes": 1383584, "skill_path": "lab-inventory-predictor" }, "content_sha256": "3c1a2ca09bd1f07291eb90f4e16ec37f883e2d832504ccda569e6240d69f93f7" }, { "id": "lab-prep-calculations", "version": "1.0.0", "display_name": "lab-prep-calculations", "summary": "Perform pre-experiment calculations and generate step-by-step preparation protocols for dilutions, solution preparation, dissolution, and % concentrations when planning formulations and lab prep workflows.", "category": "Other", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Other/Lab-Prep-Calculations" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 86, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Other/Lab-Prep-Calculations/Lab-Prep-Calculations_audit_result_v2.json", "evaluated_on": "2026-03-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 85, "max_score": 100 }, "dynamic_score": { "score": 86.2, "max_score": 100 } }, "release": { "path": "releases/lab-prep-calculations/1.0.0.json", "sha256": "2a2e82132d5a6e9fc7cea71cffc0856dc80863d5b84cc7677f10da9c0b36dd12" }, "artifact": { "path": "shards/fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0.zip", "sha256": "fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0", "bytes": 1383584, "skill_path": "lab-prep-calculations" }, "content_sha256": "142b30547bdf56345f12a11c8fbaf0fc517bc61132ab404608d29950d8e4b85d" }, { "id": "labarchive-integration", "version": "1.0.1", "display_name": "labarchive-integration", "summary": "Converts LabArchives notebook data, entry metadata, and authorized ELN exports into manuscript-ready academic writing outputs such as Methods sections, data-availability statements, reproducibility appendices, experiment timelines, and submission support notes. Optional bundled scripts can be used to collect or validate source notebook data before writing.", "category": "Academic Writing", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Academic Writing/labarchive-integration" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 90, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Academic%20Writing/labarchive-integration/labarchive-integration_audit_result_v1.json", "evaluated_on": "2026-03-23", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 88, "max_score": 100 }, "dynamic_score": { "score": 90.6, "max_score": 100 } }, "release": { "path": "releases/labarchive-integration/1.0.1.json", "sha256": "8150dfd56eaea8152bc71f83574139cd93e04b935dc4b0779cd9fcc37acfcf72" }, "artifact": { "path": "shards/96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b.zip", "sha256": "96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b", "bytes": 66876850, "skill_path": "labarchive-integration" }, "content_sha256": "161facb2b8dac3681b16e151fe0826256554df8fb439de70576fe255d51e5c87" }, { "id": "lamindb", "version": "1.0.0", "display_name": "lamindb", "summary": "Use when working with LaminDB, the open-source lineage-native lakehouse for biological datasets and models. Covers setup, artifact registration, query/search, lineage tracking, validation, ontology-backed annotation with Bionty, collections, branches, storage, and workflow integrations.", "category": "Data Analysis", "source": { "repository": "https://github.com/K-Dense-AI/scientific-agent-skills", "commit": "36d8f13a1e754618794bf42f417884940077b4ae", "path": "skills/lamindb" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "K-Dense Inc." } ], "license": "Apache-2.0 license", "release": { "path": "releases/lamindb/1.0.0.json", "sha256": "bfadc9a0c9a6800d9e2e504e5552c27f535c41824816a86840ca330c72e24816" }, "artifact": { "path": "shards/fbeb5f12afe3c0c6924b0974d44144de230c07662f05cd53d487f267a6a0eb70.zip", "sha256": "fbeb5f12afe3c0c6924b0974d44144de230c07662f05cd53d487f267a6a0eb70", "bytes": 244577, "skill_path": "lamindb" }, "content_sha256": "23ea6afd6b793863a6807f60cf5ed1d848c7f00d29b23240d22fb81d00bb8c0c" }, { "id": "latex-manuscript-format-converter", "version": "1.0.1", "display_name": "latex-manuscript-format-converter", "summary": "Converts existing manuscript content into LaTeX format aligned with a target journal, conference, or template while preserving manuscript meaning and structural integrity.", "category": "Academic Writing", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "awesome-med-research-skills/Academic Writing/latex-manuscript-format-converter" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 89, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/awesome-med-research-skills/Academic%20Writing/latex-manuscript-format-converter/eval_report_latex-manuscript-format-converter_result.json", "evaluated_on": "2026-04-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 96, "max_score": 100 }, "dynamic_score": { "score": 84.7, "max_score": 100 } }, "release": { "path": "releases/latex-manuscript-format-converter/1.0.1.json", "sha256": "73656cde96458dccd8bff994f707e57c7ec90663a64bef45b9564d40890dc548" }, "artifact": { "path": "shards/96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b.zip", "sha256": "96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b", "bytes": 66876850, "skill_path": "latex-manuscript-format-converter" }, "content_sha256": "755ced63b41261e4cec69efeb8446e98c290547d29258b7cf0999be66dfdf91f" }, { "id": "latex-posters", "version": "1.0.0", "display_name": "latex-posters", "summary": "Creates academic-poster writing packages for LaTeX using beamerposter, tikzposter, or baposter. Use when a user needs poster-ready section copy, figure plans, captions, and package-specific layout decisions for conference or thesis posters.", "category": "Academic Writing", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Academic Writing/latex-posters" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 86, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Academic%20Writing/latex-posters/latex-posters_audit_result_v1.json", "evaluated_on": "2026-03-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 83, "max_score": 100 }, "dynamic_score": { "score": 87.6, "max_score": 100 } }, "release": { "path": "releases/latex-posters/1.0.0.json", "sha256": "9a531f4fb4a5559ca3bf2fee9daaf2be84c146442dc55a33d3797b3f3bb6c0d9" }, "artifact": { "path": "shards/1ea735cead62d521282de0bf8a672a95515d73332ea4fa7d5d2ccf72eed45995.zip", "sha256": "1ea735cead62d521282de0bf8a672a95515d73332ea4fa7d5d2ccf72eed45995", "bytes": 342914, "skill_path": "latex-posters" }, "content_sha256": "40bd4af3f5820b6b66ec30dafc8a2f7c7ecc84997c2bd68ca667d7d2bd99954a" }, { "id": "lay-press-release-writer", "version": "1.0.1", "display_name": "lay-press-release-writer", "summary": "Transform academic papers into university press releases for general.", "category": "Academic Writing", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Academic Writing/lay-press-release-writer" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 85, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Academic%20Writing/lay-press-release-writer/lay-press-release-writer_audit_result_v2.json", "evaluated_on": "2026-03-23", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 88, "max_score": 100 }, "dynamic_score": { "score": 83.6, "max_score": 100 } }, "release": { "path": "releases/lay-press-release-writer/1.0.1.json", "sha256": "484641e513fa0e0f6e63c47a8275f1ee8b1943460db947ef1969e4371457a4df" }, "artifact": { "path": "shards/96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b.zip", "sha256": "96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b", "bytes": 66876850, "skill_path": "lay-press-release-writer" }, "content_sha256": "d99df5dcde157c88a83352b3710c42cdbfcf1f7eb4444f732a71783898715d57" }, { "id": "lay-summary-for-cross-disciplinary-teams", "version": "1.0.1", "display_name": "lay-summary-for-cross-disciplinary-teams", "summary": "Rewrites technical research content into a structured lay summary that cross-disciplinary teams can quickly understand and act on. Use when the user wants to explain research to colleagues outside their specialty — clinicians, wet-lab scientists, bioinformaticians, product managers, or leadership. Trigger on: \"lay summary\", \"explain my research to the team\", \"non-technical summary\", \"cross-disciplinary summary\", \"translate my findings\", \"align our team on the study\", or any request to communicate research goals, findings, or next steps to a mixed or non-specialist audience. Part of the AIPOCH Academic Writing skill hub. Sits midstream: after research content is clarified, before downstream deliverables like slide decks or graphical abstracts.", "category": "Academic Writing", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "awesome-med-research-skills/Academic Writing/lay-summary-for-cross-disciplinary-teams" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 90, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/awesome-med-research-skills/Academic%20Writing/lay-summary-for-cross-disciplinary-teams/eval_report_Lay-Summary-for-Cross-Disciplinary-Teams_result.json", "evaluated_on": "2026-04-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 92, "max_score": 100 }, "dynamic_score": { "score": 88.2, "max_score": 100 } }, "release": { "path": "releases/lay-summary-for-cross-disciplinary-teams/1.0.1.json", "sha256": "613ca3994f226a85e0a99a67040a998cfe92a7da947486d2e47ba021ed1ef9f9" }, "artifact": { "path": "shards/96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b.zip", "sha256": "96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b", "bytes": 66876850, "skill_path": "lay-summary-for-cross-disciplinary-teams" }, "content_sha256": "85702cbbd747a31713e114a6cd6144ed9c977b91cc00d2496a3b45de5e169abb" }, { "id": "lay-summary-gen", "version": "1.0.1", "display_name": "lay-summary-gen", "summary": "Converts complex medical abstracts into plain language summaries for.", "category": "Academic Writing", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Academic Writing/lay-summary-gen" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 85, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Academic%20Writing/lay-summary-gen/lay-summary-gen_audit_result_v1.json", "evaluated_on": "2026-03-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 88, "max_score": 100 }, "dynamic_score": { "score": 83.6, "max_score": 100 } }, "release": { "path": "releases/lay-summary-gen/1.0.1.json", "sha256": "c738913ef6ef9d37cfce277bbf4a42f1ae5dcdfb156f3591315cd439ae6a7a35" }, "artifact": { "path": "shards/96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b.zip", "sha256": "96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b", "bytes": 66876850, "skill_path": "lay-summary-gen" }, "content_sha256": "568b2dcd850f9535c7106691a8641965ab64d76f79fad0cf50e9e0075790fe8c" }, { "id": "lightgbm-analysis", "version": "1.0.1", "display_name": "lightgbm-analysis", "summary": "Use when training a LightGBM model on tabular data in R and returning model metrics, feature importance ranking tables, and feature importance plots.", "category": "Data Analysis", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "awesome-med-research-skills/Data Analysis/LightGBM-analysis" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "Unknown", "evaluation": { "kind": "upstream-self-assessment", "score": 86, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/awesome-med-research-skills/Data%20Analysis/LightGBM-analysis/eval_report_lightgbm-analysis_result.json", "evaluated_on": "2026-04-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 90, "max_score": 100 }, "dynamic_score": { "score": 83, "max_score": 100 } }, "release": { "path": "releases/lightgbm-analysis/1.0.1.json", "sha256": "e45c24c3c8ea43f52b70cfbfb06b9b6caa03b35d79e566554aa5ac1efccf76b2" }, "artifact": { "path": "shards/96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b.zip", "sha256": "96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b", "bytes": 66876850, "skill_path": "lightgbm-analysis" }, "content_sha256": "f79af0433f4511837afdfde5cc1a1a854538b47f9a4dbfc10e04b948a20f2afd" }, { "id": "limitation-and-risk-writer", "version": "1.0.1", "display_name": "limitation-and-risk-writer", "summary": "Acknowledges limitations in sample, design, measurement, and validation in a professional way that improves credibility without undermining the whole paper. Use when writing the limitations paragraph of a Discussion section, preparing a grant risk assessment, responding to reviewers about study weaknesses, or framing scope boundaries for a paper. Also triggers on \"write my limitations\", \"how should I address the limitation of\", \"reviewer said my sample is too small\", or \"help me word this limitation\".", "category": "Academic Writing", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "awesome-med-research-skills/Academic Writing/limitation-and-risk-writer" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 90, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/awesome-med-research-skills/Academic%20Writing/limitation-and-risk-writer/eval_report_limitation-and-risk-writer_result.json", "evaluated_on": "2026-04-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 92, "max_score": 100 }, "dynamic_score": { "score": 88.6, "max_score": 100 } }, "release": { "path": "releases/limitation-and-risk-writer/1.0.1.json", "sha256": "1c0117a4ecde4286fa98b9b0fb414a725757487d2e064d2192dc9e2fa8ffc2bb" }, "artifact": { "path": "shards/96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b.zip", "sha256": "96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b", "bytes": 66876850, "skill_path": "limitation-and-risk-writer" }, "content_sha256": "95a8005290389abd55280074bca98be7cd65b70453309e43e067997db9be341d" }, { "id": "lipinski-rule-filter", "version": "1.0.1", "display_name": "lipinski-rule-filter", "summary": "Filter compound libraries based on Lipinski's Rule of Five for drug-likeness.", "category": "Data Analysis", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Data Analysis/lipinski-rule-filter" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 86, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Data%20Analysis/lipinski-rule-filter/lipinski-rule-filter_audit_result_v2.json", "evaluated_on": "2026-03-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 88, "max_score": 100 }, "dynamic_score": { "score": 84.4, "max_score": 100 } }, "release": { "path": "releases/lipinski-rule-filter/1.0.1.json", "sha256": "5627b429ca45edeba2fda92a1141dca058efeac7dcc7ccaf2f1fbe7648702c33" }, "artifact": { "path": "shards/96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b.zip", "sha256": "96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b", "bytes": 66876850, "skill_path": "lipinski-rule-filter" }, "content_sha256": "267968e289b8c4f9cd010e35467c887d21130c3cb44202b065e2c6e83a8458c5" }, { "id": "litbase", "version": "1.0.1", "display_name": "litbase", "summary": "Academic paper reading and research development system for biomedical researchers. Finds papers via Semantic Scholar, reads with structured notes, tracks discussion insights, and synthesizes literature into a Research Foundation Document (RFD) for downstream protocol design skills. 8 commands: /setup /feed /read /discuss /recap /update /sync /propose", "category": "Evidence Insight", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "awesome-med-research-skills/Evidence Insight/litbase" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "Unknown", "evaluation": { "kind": "upstream-self-assessment", "score": 87, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/awesome-med-research-skills/Evidence%20Insight/litbase/eval_report_litbase_result.json", "evaluated_on": "2026-04-22", "evaluator_version": 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use when you need reproducible spectral filtering and similarity scoring for metabolomics workflows.", "category": "Data Analysis", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Data Analysis/matchms" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 85, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Data%20Analysis/matchms/matchms_audit_result_v1.json", "evaluated_on": "2026-03-23", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 83, "max_score": 100 }, "dynamic_score": { "score": 86.6, "max_score": 100 } }, "release": { "path": "releases/matchms/1.0.1.json", "sha256": "84ff7ce3a8233518528fd87a93c2fd2e4fd31830e4d2a86357f4067f663c18ab" }, "artifact": { "path": "shards/96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b.zip", "sha256": "96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b", "bytes": 66876850, "skill_path": "matchms" }, "content_sha256": "f7ceea7868f9898b49f2cab18d34854353c86b88d24ce857215fea535d5a1f07" }, { "id": "mechanism-flowchart", "version": "1.0.1", "display_name": "mechanism-flowchart", "summary": "Generates Mermaid flowchart code and visual diagrams for pathophysiological.", "category": "Data Analysis", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Data Analysis/mechanism-flowchart" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 89, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Data%20Analysis/mechanism-flowchart/mechanism-flowchart_audit_result_v2.json", "evaluated_on": "2026-03-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 88, "max_score": 100 }, "dynamic_score": { "score": 89.6, "max_score": 100 } }, "release": { "path": "releases/mechanism-flowchart/1.0.1.json", "sha256": "ae00ffcf8ee034535bca58058ef1aca280e907e0e400e813943ee34d2ca26909" }, "artifact": { "path": "shards/96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b.zip", "sha256": "96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b", "bytes": 66876850, "skill_path": "mechanism-flowchart" }, "content_sha256": "918be95b39258c7e8b724e995896ee73512e92a7414ef2da6e670390036e3de8" }, { "id": "mechanism-to-validation-planner", "version": "1.0.0", "display_name": "mechanism-to-validation-planner", "summary": "Extends a mechanistic or association-level biomedical finding into a staged validation pathway that moves from descriptive evidence toward stronger functional support, mechanistic specificity, and clinical relevance. Use this skill when a user has a pathway, biomarker, cell-state, target, mechanism, or association finding and needs to decide what should be validated next, in what order, and which evidence layers are necessary versus optional. Do not default to maximal validation stacks. Build a structured validation ladder with a primary route, stronger upgrade route, and optional extensions.", "category": "Protocol Design", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "awesome-med-research-skills/Protocol Design/mechanism-to-validation-planner" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 89, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/awesome-med-research-skills/Protocol%20Design/mechanism-to-validation-planner/eval_report_mechanism-to-validation-planner_result.json", "evaluated_on": "2026-04-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 91, "max_score": 100 }, "dynamic_score": { "score": 87.4, "max_score": 100 } }, "release": { "path": "releases/mechanism-to-validation-planner/1.0.0.json", "sha256": "ef3efd052d74f6fb5fc4a37b07902c3f08363eea17fadb62dae994d66bb8a2f2" }, "artifact": { "path": "shards/fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0.zip", "sha256": "fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0", "bytes": 1383584, "skill_path": "mechanism-to-validation-planner" }, "content_sha256": "c60dd871766d41996eccb2453424ad618852e6bbb832d9cc54f847c427d814d3" }, { "id": "medchem", "version": "1.0.0", "display_name": "medchem", "summary": "Medicinal chemistry screening filters for compound prioritization; use when you need to apply drug-likeness rules, PAINS/structural alerts, and complexity metrics to triage or optimize libraries.", "category": "Evidence Insight", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Evidence Insight/medchem" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 87, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Evidence%20Insight/medchem/medchem_audit_result_v1.json", "evaluated_on": "2026-03-23", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 87, "max_score": 100 }, "dynamic_score": { "score": 87.6, "max_score": 100 } }, "release": { "path": "releases/medchem/1.0.0.json", "sha256": "0cbf8ec25662a35aa6f82d69ed2877c0be690be4db4baa3b04c2514952b3ac60" }, "artifact": { "path": "shards/1d2612a702320846b10cda57f9f37ce17f5e19186095f2fe96bfa6e44eb077e8.zip", "sha256": "1d2612a702320846b10cda57f9f37ce17f5e19186095f2fe96bfa6e44eb077e8", "bytes": 234009, "skill_path": "medchem" }, "content_sha256": "dd630278b7cc144ed3cbe8a224f85e0f34dbe9d4264f20a4555b7c9ff53e1f1d" }, { "id": "medical-case-interpreter", "version": "1.0.0", "display_name": "medical-case-interpreter", "summary": "Generates compliant medical case report articles for WeChat.", "category": "Other", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Other/medical-case-interpreter" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 90, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Other/medical-case-interpreter/medical-case-interpreter_audit_result_v2.json", "evaluated_on": "2026-03-23", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 83, "max_score": 100 }, "dynamic_score": { "score": 94.6, "max_score": 100 } }, "release": { "path": "releases/medical-case-interpreter/1.0.0.json", "sha256": "a198e3501ba65c1a4e192d9b249cbab159554df1e0563baac9e2bfc282cecdb9" }, "artifact": { "path": "shards/fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0.zip", "sha256": "fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0", "bytes": 1383584, "skill_path": "medical-case-interpreter" }, "content_sha256": "2b9f645a0f489cc26689569800dd07e63a5b81da5ecbbe28a20b7d6ce7c86c01" }, { "id": "medical-case-report-generator", "version": "1.0.0", "display_name": "medical-case-report-generator", "summary": "Generates a patient-friendly medical case report tweet from case images and disease name. 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"name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 85, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Academic%20Writing/medical-cv-resume-builder/medical-cv-resume-builder_audit_result_v1.json", "evaluated_on": "2026-03-23", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 88, "max_score": 100 }, "dynamic_score": { "score": 83.6, "max_score": 100 } }, "release": { "path": "releases/medical-cv-resume-builder/1.0.1.json", "sha256": "04b1f9c18be470403fc5f13ef3ca7450c963502f2dca86a9083c2da7838bd58e" }, "artifact": { "path": "shards/96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b.zip", "sha256": "96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b", "bytes": 66876850, "skill_path": "medical-cv-resume-builder" }, "content_sha256": "d80b8ce3ebbb17987181439d3fd1e14842de2da28f6a6430317e2c51bcafdbf3" }, { "id": "medical-device-mdr-auditor", "version": "1.0.1", "display_name": "medical-device-mdr-auditor", "summary": "Audit medical device technical files against EU MDR 2017/745 regulations.", "category": "Academic Writing", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Academic Writing/medical-device-mdr-auditor" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 85, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Academic%20Writing/medical-device-mdr-auditor/medical-device-mdr-auditor_audit_result_v2.json", "evaluated_on": "2026-03-23", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 88, "max_score": 100 }, "dynamic_score": { "score": 83.6, "max_score": 100 } }, "release": { "path": "releases/medical-device-mdr-auditor/1.0.1.json", "sha256": "c2825fe0f1d7c0374446d591e1dec93322abbef6a97dba55bf2cd48a7d315187" }, "artifact": { "path": "shards/96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b.zip", "sha256": "96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b", "bytes": 66876850, "skill_path": "medical-device-mdr-auditor" }, "content_sha256": "31f25e7e7e624842deca888056776b34632bc93fe08816c913f6f461e4ab8767" }, { "id": "medical-email-polisher", "version": "1.0.1", "display_name": "medical-email-polisher", "summary": "Transforms rough email drafts into polished, professional medical correspondence.", "category": "Academic Writing", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Academic Writing/medical-email-polisher" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 85, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Academic%20Writing/medical-email-polisher/medical-email-polisher_audit_result_v1.json", "evaluated_on": "2026-03-23", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 88, "max_score": 100 }, "dynamic_score": { "score": 83.6, "max_score": 100 } }, "release": { "path": "releases/medical-email-polisher/1.0.1.json", "sha256": "d064302a193ea3e620bf7d203d16c732559b8335e7bd66d7ed809ecba035c5b5" }, "artifact": { "path": "shards/96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b.zip", "sha256": "96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b", "bytes": 66876850, "skill_path": "medical-email-polisher" }, "content_sha256": "73a095a7ca782803e9b22462420804aa3104b8b2a2c01c747d9366b7f24dc14e" }, { "id": "medical-english-precision-editor", "version": "1.0.1", "display_name": "medical-english-precision-editor", "summary": "Improves medical English precision without changing the underlying facts, evidence boundaries, or intended scientific meaning.", "category": "Academic Writing", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "awesome-med-research-skills/Academic Writing/medical-english-precision-editor" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 91, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/awesome-med-research-skills/Academic%20Writing/medical-english-precision-editor/eval_report_medical-english-precision-editor_result.json", "evaluated_on": "2026-04-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 94, "max_score": 100 }, "dynamic_score": { "score": 88.4, "max_score": 100 } }, "release": { "path": "releases/medical-english-precision-editor/1.0.1.json", "sha256": "cba946c7fe24f8b9f42cbcffe4e0f386d3aec6ba8e396471facb4d2ae798c417" }, "artifact": { "path": "shards/96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b.zip", "sha256": "96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b", "bytes": 66876850, "skill_path": "medical-english-precision-editor" }, "content_sha256": "6c82a18ea9d8bcdd5a7281285230544452cb38e4dc8c6c24aa559f683ff673c0" }, { "id": "medical-imaging-review", "version": "1.0.1", "display_name": "medical-imaging-review", "summary": "Write comprehensive literature reviews for medical imaging AI research. Use when writing survey papers, systematic reviews, or literature analyses on topics like segmentation, detection, classification in CT, MRI, X-ray, ultrasound, or pathology imaging. Triggers on requests f...", "category": "Academic Writing", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Academic Writing/medical-imaging-review" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 88, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Academic%20Writing/medical-imaging-review/eval_report_medical-imaging-review_result.json", "evaluated_on": "2026-06-15", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 91, "max_score": 100 }, "dynamic_score": { "score": 86.6, "max_score": 100 } }, "release": { "path": "releases/medical-imaging-review/1.0.1.json", "sha256": "2b9d14f00ed1954cf804471656d00e7cafaf36047c84b36fca2cc5da36171cd6" }, "artifact": { "path": "shards/96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b.zip", "sha256": "96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b", "bytes": 66876850, "skill_path": "medical-imaging-review" }, "content_sha256": "a877c2820a502fa526c76af0eabdde9169f00f78197dbfb6d2e875cd167bdd71" }, { "id": "medical-research-algorithm-matcher", "version": "1.0.0", "display_name": "medical-research-algorithm-matcher", "summary": "Matches a user’s biomedical research direction, disease problem, study aim, data modality, and resource constraints to the most relevant recent algorithms and method papers. Always search real recent algorithm literature first, prioritize the last 12 months, expand to 1–3 years only when needed, and add canonical baselines only when necessary. Every formal algorithm recommendation must include the verified primary method paper, plus published downstream papers that actually cite/use the algorithm when such papers are found, with DOI when available. Never fabricate papers, algorithm names, authors, journals, years, DOI, PMID, links, or benchmark claims. If no directly verified algorithm paper is found, say so explicitly.", "category": "Protocol Design", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "awesome-med-research-skills/Protocol Design/medical-research-algorithm-matcher" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 89, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/awesome-med-research-skills/Protocol%20Design/medical-research-algorithm-matcher/eval_report_medical-research-algorithm-matcher_result.json", "evaluated_on": "2026-04-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 91, "max_score": 100 }, "dynamic_score": { "score": 88.4, "max_score": 100 } }, "release": { "path": "releases/medical-research-algorithm-matcher/1.0.0.json", "sha256": "b9f1df816684f82d3a16d21567d860e3a9b0ce6262abf71c2cc4bd71384e7afb" }, "artifact": { "path": "shards/fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0.zip", "sha256": "fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0", "bytes": 1383584, "skill_path": "medical-research-algorithm-matcher" }, "content_sha256": "bfabe14437f36901f9f87392f4ae297ea6608d39e0eea7708c8c90d44ed30695" }, { "id": "medical-research-gap-finder", "version": "1.0.0", "display_name": "medical-research-gap-finder", "summary": "Identifies real, evidence-audited, topic-specific research gaps in medical research by first retrieving and verifying literature from trusted sources, then mapping the current evidence landscape, rejecting pseudo-gaps, and converting only medium/high-confidence gaps into study-ready research opportunities. Always require real literature retrieval before formal gap claims. Never fabricate references, metadata, or findings.", "category": "Evidence Insight", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "awesome-med-research-skills/Evidence Insight/medical-research-gap-finder" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 86, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/awesome-med-research-skills/Evidence%20Insight/medical-research-gap-finder/eval_report_medical-research-gap-finder_result.json", "evaluated_on": "2026-04-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 89, "max_score": 100 }, "dynamic_score": { "score": 84, "max_score": 100 } }, "release": { "path": "releases/medical-research-gap-finder/1.0.0.json", "sha256": "a9a8d8366b93290db58664ca8df5056618ca29e942d928d3009da0eabf56e9bd" }, "artifact": { "path": "shards/363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb.zip", "sha256": "363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb", "bytes": 1471244, "skill_path": "medical-research-gap-finder" }, "content_sha256": "2699272c2346fa756c4df236a841dbbbcf44ef3c70906a4611d0ec7ac4d78c2e" }, { "id": "medical-research-gap-to-study-planner", "version": "1.0.0", "display_name": "medical-research-gap-to-study-planner", "summary": "Converts an audited medical research gap into a complete, structured, gap-traceable study design. Always use this skill whenever a user already has one or more candidate research gaps and wants to transform them into an executable biomedical research plan rather than re-run broad topic ideation. Covers six gap-to-design patterns (evidence-completion, mechanism-resolution, cell-state/context-mapping, translation-bridge, causality-upgrade, population/stage-specific) and always outputs one recommended primary protocol, a gap-to-design dependency map, step-by-step workflow, figure plan, validation strategy, minimal executable version, publication upgrade path, and verified design-support literature rules. Never fabricate references. Preserve claim-evidence discipline and do not replace a topic-specific gap with a generic workflow.", "category": "Protocol Design", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "awesome-med-research-skills/Protocol Design/medical-research-gap-to-study-planner" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 90, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/awesome-med-research-skills/Protocol%20Design/medical-research-gap-to-study-planner/eval_report_medical-research-gap-to-study-planner_result.json", "evaluated_on": "2026-04-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 92, "max_score": 100 }, "dynamic_score": { "score": 88.4, "max_score": 100 } }, "release": { "path": "releases/medical-research-gap-to-study-planner/1.0.0.json", "sha256": "b7ddfee6a352c7ffa6d8131d00a54f524d9c2979d717fcac1bcefecf79cd3655" }, "artifact": { "path": "shards/fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0.zip", "sha256": "fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0", "bytes": 1383584, "skill_path": "medical-research-gap-to-study-planner" }, "content_sha256": "710247b5fdf1bfeadd1df1d247153bbcaed5cc74071c68e4e297974faa141edf" }, { "id": "medical-research-literature-reader-pro", "version": "1.0.0", "display_name": "medical-research-literature-reader-pro", "summary": "A medical-research-native literature reading skill for users with clinical, bioinformatics, translational, and basic experimental backgrounds. Use this skill whenever a user wants to read, analyze, critique, or interpret a medical or scientific paper — whether they provide a PDF, abstract, DOI, PMID, or just a title. Triggers include requests like \\\"analyze this paper\\\", \\\"critique this study\\\", \\\"is this a strong paper?\\\", \\\"give me similar studies\\\", \\\"prepare me for journal club\\\", \\\"help me understand this bioinformatics paper\\\", \\\"what are the weaknesses here?\\\", or \\\"turn this into a mind map\\\". Also activate for any downstream deliverables such as journal club kits, comparison tables, PI decision briefs, replication starters, or follow-up experiment designs. Do NOT treat as a generic summarizer — this skill performs structured evidence-type classification, track-specific critical appraisal, interpretation-boundary judgment, and research-grade follow-up generation.", "category": "Evidence Insight", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "awesome-med-research-skills/Evidence Insight/medical-research-literature-reader-pro" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 93, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/awesome-med-research-skills/Evidence%20Insight/medical-research-literature-reader-pro/eval_report_medical-research-literature-reader-pro_result.json", "evaluated_on": "2026-04-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 96, "max_score": 100 }, "dynamic_score": { "score": 91, "max_score": 100 } }, "release": { "path": "releases/medical-research-literature-reader-pro/1.0.0.json", "sha256": "02ce70906d9a1e2d6cb01092acda8eb0a5405bbcaaabb41c59190db7430e747a" }, "artifact": { "path": "shards/363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb.zip", "sha256": "363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb", "bytes": 1471244, "skill_path": "medical-research-literature-reader-pro" }, "content_sha256": "be9d714fb977bacba2c6c6b541f54b52a5c3f66b2ce4830de50f4bf31e9375cc" }, { "id": "medical-scribe-dictation", "version": "1.0.1", "display_name": "medical-scribe-dictation", "summary": "Convert physician verbal dictation into structured SOAP notes. Trigger.", "category": "Academic Writing", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Academic Writing/medical-scribe-dictation" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "Unknown", "evaluation": { "kind": "upstream-self-assessment", "score": 85, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Academic%20Writing/medical-scribe-dictation/medical-scribe-dictation_audit_result_v2.json", "evaluated_on": "2026-03-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 88, "max_score": 100 }, "dynamic_score": { "score": 83.6, "max_score": 100 } }, "release": { "path": "releases/medical-scribe-dictation/1.0.1.json", "sha256": "97783a5269b0b9c0fd000cf42d080f43986bf1dd6a09acd117d78c348edf937e" }, "artifact": { "path": "shards/96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b.zip", "sha256": "96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b", "bytes": 66876850, "skill_path": "medical-scribe-dictation" }, "content_sha256": "13cd15e9dc72d84122c913388ca9bc0dc48712edb1ad5bf3647b0aa373090b17" }, { "id": "medical-topic-saturation-and-whitespace-checker", "version": "1.0.0", "display_name": "medical-topic-saturation-and-whitespace-checker", "summary": "Maps whether a biomedical research topic, subtopic, or study angle is truly saturated, superficially crowded, strategically occupied, or still open for differentiated entry. Use this skill when a user wants to know whether a hot medical research direction is already overworked, whether meaningful whitespace remains, whether major groups have already occupied the obvious claims, and whether the timing window is still open. Always distinguish popularity from true saturation, and distinguish cosmetic novelty from meaningful differentiating entry.", "category": "Evidence Insight", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "awesome-med-research-skills/Evidence Insight/medical-topic-saturation-and-whitespace-checker" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 86, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/awesome-med-research-skills/Evidence%20Insight/medical-topic-saturation-and-whitespace-checker/eval_report_medical-topic-saturation-and-whitespace-checker_result.json", "evaluated_on": "2026-04-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 90, "max_score": 100 }, "dynamic_score": { "score": 84.1, "max_score": 100 } }, "release": { "path": "releases/medical-topic-saturation-and-whitespace-checker/1.0.0.json", "sha256": "12d4ab491e52f67edef9d566a5b5fe782eecf37e4a42342648d453b273fe820a" }, "artifact": { "path": "shards/363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb.zip", "sha256": "363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb", "bytes": 1471244, "skill_path": "medical-topic-saturation-and-whitespace-checker" }, "content_sha256": "1553527dcf8a3a38ebc2d0e5e690ce588ff7e9573d2ca4755e4ad0734f041553" }, { "id": "medical-translation", "version": "1.0.1", "display_name": "medical-translation", "summary": "Use medical translation for academic writing workflows that need structured execution, explicit assumptions, and clear output boundaries.", "category": "Academic Writing", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Academic Writing/medical-translation" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 85, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Academic%20Writing/medical-translation/medical-translation_audit_result_v2.json", "evaluated_on": "2026-03-23", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 88, "max_score": 100 }, "dynamic_score": { "score": 83.6, "max_score": 100 } }, "release": { "path": "releases/medical-translation/1.0.1.json", "sha256": "1c0b772b374aacc29c7489b87e4b9fc5291881148f7e47c130f02e2a6bee9b8e" }, "artifact": { "path": "shards/96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b.zip", "sha256": "96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b", "bytes": 66876850, "skill_path": "medical-translation" }, "content_sha256": "bcb9fbc4dccf7236d176b115b6699e59bf9973701ffd036aa2e945beef24fc48" }, { "id": "medical-unit-converter", "version": "1.0.0", "display_name": "medical-unit-converter", "summary": "Convert medical laboratory values between units (mg/dL to mmol/L, etc.) with formula transparency and clinical reference ranges. Supports glucose, cholesterol, creatinine, and hemoglobin conversions.", "category": "Other", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Other/medical-unit-converter" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 90, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Other/medical-unit-converter/medical-unit-converter_audit_result_v4.json", "evaluated_on": "2026-03-19", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 91, "max_score": 100 }, "dynamic_score": { "score": 90, "max_score": 100 } }, "release": { "path": "releases/medical-unit-converter/1.0.0.json", "sha256": "82d5dbaee8fa61f7d19b904f9558b39cf6c3bc5aa8b67260192fe8c91b23e359" }, "artifact": { "path": "shards/fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0.zip", "sha256": "fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0", "bytes": 1383584, "skill_path": "medical-unit-converter" }, "content_sha256": "42fb75e009a544a4b57b512b81ee2e48314342dcf0e2c7d94a65342abaab3d98" }, { "id": "medical-vector-search", "version": "1.0.0", "display_name": "medical-vector-search", "summary": "Vector database retrieval and evidence-based answering for medical research topics. Use when users need knowledge-base-backed answers about methodology, disease mechanisms, drug effects, clinical research, or research tools. Input is a medical research question; output is a st...", "category": "Evidence Insight", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Evidence Insight/medical-vector-search" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 90, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Evidence%20Insight/medical-vector-search/eval_report_medical-vector-search_result.json", "evaluated_on": "2026-06-15", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 95, "max_score": 100 }, "dynamic_score": { "score": 86.2, "max_score": 100 } }, "release": { "path": "releases/medical-vector-search/1.0.0.json", "sha256": "694ff6f725a07738624a2ce322190870110afd64c8b2123eede1678668f9843d" }, "artifact": { "path": "shards/363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb.zip", "sha256": "363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb", "bytes": 1471244, "skill_path": "medical-vector-search" }, "content_sha256": "bfb0163e343ec702f5cd3d762eb24cf0896eca5afbb9ec9c6ac2fe1a15e35f21" }, { "id": "medication-adherence-message-gen", "version": "1.0.1", "display_name": "medication-adherence-message-gen", "summary": "Use medication adherence message gen for academic writing workflows that need structured execution, explicit assumptions, and clear output boundaries.", "category": "Academic Writing", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Academic Writing/medication-adherence-message-gen" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 85, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Academic%20Writing/medication-adherence-message-gen/medication-adherence-message-gen_audit_result_v2.json", "evaluated_on": "2026-03-23", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 88, "max_score": 100 }, "dynamic_score": { "score": 83.6, "max_score": 100 } }, "release": { "path": "releases/medication-adherence-message-gen/1.0.1.json", "sha256": "86f6690ec43b75bcf54d0af5011048952338df4eb87405b852fe991de987d38c" }, "artifact": { "path": "shards/96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b.zip", "sha256": "96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b", "bytes": 66876850, "skill_path": "medication-adherence-message-gen" }, "content_sha256": "f7aab6d31b5716e8775f06f212376bfe8637894cee05ab856ee8cc3a2adee624" }, { "id": "medication-reconciliation", "version": "1.0.0", "display_name": "medication-reconciliation", "summary": "Compare patient pre-admission medication lists with inpatient orders to automatically identify omitted or duplicated medications and improve medication safety.", "category": "Other", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Other/medication-reconciliation" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 89, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Other/medication-reconciliation/medication-reconciliation_audit_result_v2.json", "evaluated_on": "2026-03-19", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 90, "max_score": 100 }, "dynamic_score": { "score": 88.4, "max_score": 100 } }, "release": { "path": "releases/medication-reconciliation/1.0.0.json", "sha256": "974bfe47f6bb2581837489936a653eb8e5d3aba93e45126f7c4a235f78acf781" }, "artifact": { "path": "shards/fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0.zip", "sha256": "fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0", "bytes": 1383584, "skill_path": "medication-reconciliation" }, "content_sha256": "aac5440a58f9cb6370cca2b7f06824a11fd729fb1f46f8fb9cdb2115f4381d14" }, { "id": "meeting-minutes", "version": "1.0.1", "display_name": "meeting-minutes", "summary": "Use meeting minutes for other workflows that need structured execution, explicit assumptions, and clear output boundaries.", "category": "Academic Writing", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Academic Writing/meeting-minutes" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 85, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Academic%20Writing/meeting-minutes/meeting-minutes_audit_result_v2.json", "evaluated_on": "2026-03-23", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 88, "max_score": 100 }, "dynamic_score": { "score": 83.6, "max_score": 100 } }, "release": { "path": "releases/meeting-minutes/1.0.1.json", "sha256": "44465659f6acfa1b418751b583a87be8a47d7105be9cb59aec99af1bf9618cf6" }, "artifact": { "path": "shards/96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b.zip", "sha256": "96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b", "bytes": 66876850, "skill_path": "meeting-minutes" }, "content_sha256": "e2a08cbc9e2ace668c46866e20cbff8c76221ad1270202ce1adf9d845f0b9ac1" }, { "id": "mendelian-randomization-protocol-designer", "version": "1.0.0", "display_name": "mendelian-randomization-protocol-designer", "summary": "Generates complete Mendelian randomization study designs from a user-provided exposure and outcome direction. Always use this skill whenever a user wants to design, plan, or build a Mendelian randomization study — even if phrased as \"help me write a paper on X\", \"design an MR study for Y\", or \"I want to test whether A causally affects B using GWAS\". Covers core two-sample MR design, optional bidirectional follow-up, optional multivariable MR, IV selection logic, ancestry alignment, harmonization, IVW as the default primary estimator, weighted median / MR-Egger / MR-PRESSO / leave-one-out sensitivity analyses, Steiger directionality, heterogeneity / pleiotropy checks, and explicit claim-boundary control. Always outputs four workload configs (Lite / Standard / Advanced / Publication+) with a recommended primary plan, stepwise workflow, method rationale, validation ladder, figure plan, minimal executable version, and strictly verified literature guidance with no fabricated references.", "category": "Protocol Design", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "awesome-med-research-skills/Protocol Design/mendelian-randomization-protocol-designer" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 91, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/awesome-med-research-skills/Protocol%20Design/mendelian-randomization-protocol-designer/eval_report_mendelian-randomization-protocol-designer_result.json", "evaluated_on": "2026-04-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 94, "max_score": 100 }, "dynamic_score": { "score": 88.7, "max_score": 100 } }, "release": { "path": "releases/mendelian-randomization-protocol-designer/1.0.0.json", "sha256": "7586bc9e1b22fcbaa2f078bc2367925b6fd8e9eb44113f583cded4a6b217075e" }, "artifact": { "path": "shards/630d1b4dab37083d632d924264ae93642b8327521aaa70fbf83de3cc7fd53d61.zip", "sha256": "630d1b4dab37083d632d924264ae93642b8327521aaa70fbf83de3cc7fd53d61", "bytes": 542522, "skill_path": "mendelian-randomization-protocol-designer" }, "content_sha256": "4a6dfe0a285760e74a0b2cd79a0893bede562c014403156d27cd1b8c5d399a97" }, { "id": "meta-abstract-screener", "version": "1.0.0", "display_name": "meta-abstract-screener", "summary": "Screens research papers based on title/abstract and inclusion criteria, providing a structured Yes/No/Maybe decision. Use when you need to filter literature for meta-analysis or systematic reviews.", "category": "Data Analysis", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d915031495e1c755d272c3de817ee3fc012e8b7b", "path": "scientific-skills/Data Analysis/meta-abstract-screener" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 86, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d915031495e1c755d272c3de817ee3fc012e8b7b/scientific-skills/Data%20Analysis/meta-abstract-screener/meta-abstract-screener_audit_result_v2.json", "evaluated_on": "2026-03-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 87, "max_score": 100 }, "dynamic_score": { "score": 86, "max_score": 100 } }, "release": { "path": "releases/meta-abstract-screener/1.0.0.json", "sha256": "4ebaeb3cd4c06923301608c47225257be7d9cbce5315d42d26d018b78c1a5c32" }, "artifact": { "path": "shards/de33edee35bb8ab7aef49234e84a13f7363966440d4d5f381ae005f9e13edd20.zip", "sha256": "de33edee35bb8ab7aef49234e84a13f7363966440d4d5f381ae005f9e13edd20", "bytes": 166464, "skill_path": "meta-abstract-screener" }, "content_sha256": "19efd0a63d165924e83a925a6dcea7a008d83489fa514ebf4e0bb7ea18e3d4b2" }, { "id": "meta-analysis-methods-generator", "version": "1.0.1", "display_name": "meta-analysis-methods-generator", "summary": "Generates the Methods section for a meta-analysis paper, including search strategy, screening, quality assessment, data extraction, and statistical analysis.", "category": "Academic Writing", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Academic Writing/meta-analysis-methods-generator" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 90, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Academic%20Writing/meta-analysis-methods-generator/eval_report_meta-analysis-methods-generator_result.json", "evaluated_on": "2026-06-15", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 96, "max_score": 100 }, "dynamic_score": { "score": 86.6, "max_score": 100 } }, "release": { "path": "releases/meta-analysis-methods-generator/1.0.1.json", "sha256": "f9b2a5fc41abb1ef8ecf0239ccb283661c1a69b4e5cca4701b13f2f3d4c4c364" }, "artifact": { "path": "shards/96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b.zip", "sha256": "96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b", "bytes": 66876850, "skill_path": "meta-analysis-methods-generator" }, "content_sha256": "1a721e801217685681885d9dbad51af9551fcf8b3b142e139eb3eafe8602b45d" }, { "id": "meta-baseline-generator", "version": "1.0.1", "display_name": "meta-baseline-generator", "summary": "Generates a meta-analysis baseline characteristics section (text + table) from raw data. Supports Chinese and English. Use when the user provides baseline data and wants a formatted results section.", "category": "Academic Writing", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Academic Writing/meta-baseline-generator" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 90, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Academic%20Writing/meta-baseline-generator/eval_report_meta-baseline-generator_result.json", "evaluated_on": "2026-06-15", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 96, "max_score": 100 }, "dynamic_score": { "score": 86.2, "max_score": 100 } }, "release": { "path": "releases/meta-baseline-generator/1.0.1.json", "sha256": "0a349850c93a056b4a8f124d90969cd766cded8cf6e654985e01c8afbc5a6e5a" }, "artifact": { "path": "shards/96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b.zip", "sha256": "96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b", "bytes": 66876850, "skill_path": "meta-baseline-generator" }, "content_sha256": "30397cc18dce760c7879bf55301d81e85d507e7d4b4c34e55e5b56e58da589c3" }, { "id": "meta-baujat-plot", "version": "1.0.1", "display_name": "meta-baujat-plot", "summary": "Generate Baujat plots for heterogeneity analysis. Identify studies that contribute most to the overall meta-analysis results and heterogeneity, helping discover potential outlier studies. Input meta-analysis data CSV, output Baujat plot PNG and contribution data CSV.", "category": "Data Analysis", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Data Analysis/meta-baujat-plot" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 85, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Data%20Analysis/meta-baujat-plot/meta-baujat-plot_audit_result_v2.json", "evaluated_on": "2026-03-23", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 83, "max_score": 100 }, "dynamic_score": { "score": 86.6, "max_score": 100 } }, "release": { "path": "releases/meta-baujat-plot/1.0.1.json", "sha256": "f679b94244cb5c439f8e72060bf201ebc35641dc0a368e43e3897c689ef7e53f" }, "artifact": { "path": "shards/96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b.zip", "sha256": "96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b", "bytes": 66876850, "skill_path": "meta-baujat-plot" }, "content_sha256": "559dd4aa033e1f7f88f46c2283eace683cdec3036865bc947259549d63d8b57f" }, { "id": "meta-criteria-generator", "version": "1.0.1", "display_name": "meta-criteria-generator", "summary": "Generates scientifically sound inclusion and exclusion criteria for Meta-Analysis based on a given title or keywords. Use when user wants to design eligibility criteria for a systematic review or meta-analysis.", "category": "Data Analysis", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Data Analysis/meta-criteria-generator" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 91, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Data%20Analysis/meta-criteria-generator/meta-criteria-generator_audit_result_v2.json", "evaluated_on": "2026-03-23", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 83, "max_score": 100 }, "dynamic_score": { "score": 95.6, "max_score": 100 } }, "release": { "path": "releases/meta-criteria-generator/1.0.1.json", "sha256": "37f6d20df3db6245918c01ad1712e868a1b95fb6ff54af682645f7eeedf690b6" }, "artifact": { "path": "shards/96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b.zip", "sha256": "96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b", "bytes": 66876850, "skill_path": "meta-criteria-generator" }, "content_sha256": "40d82f41b70858528a2feb8b28bc90b4e557c8dea71f39efa6ecd6842ec082d8" }, { "id": "meta-feasibility-analyzer", "version": "1.0.1", "display_name": "meta-feasibility-analyzer", "summary": "Analyzes the feasibility of a proposed Meta-analysis topic by searching for existing Meta-analyses and Clinical Trials on PubMed/ClinicalTrials.gov. Use when you need to evaluate if a topic is viable for a new Meta-analysis.", "category": "Data Analysis", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Data Analysis/meta-feasibility-analyzer" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 86, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Data%20Analysis/meta-feasibility-analyzer/meta-feasibility-analyzer_audit_result_v1.json", "evaluated_on": "2026-03-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 78, "max_score": 100 }, "dynamic_score": { "score": 90.6, "max_score": 100 } }, "release": { "path": "releases/meta-feasibility-analyzer/1.0.1.json", "sha256": "d596d3a9149e09d3d07250a932960f53c222b9c7a97d47a1da1520d9d58bb4a0" }, "artifact": { "path": "shards/96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b.zip", "sha256": "96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b", "bytes": 66876850, "skill_path": "meta-feasibility-analyzer" }, "content_sha256": "88359dbbaa9fa233560bac56f0525b2598a741095cda916461a0984ba045ec7f" }, { "id": "meta-forest-binary-plot", "version": "1.0.1", "display_name": "meta-forest-binary-plot", "summary": "Generate meta-analysis forest plots for binary classification data. Input is a CSV file containing study names, event counts and sample sizes for experimental and control groups. Output includes forest plot PNG and data table CSV.", "category": "Data Analysis", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Data Analysis/meta-forest-binary-plot" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 91, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Data%20Analysis/meta-forest-binary-plot/meta-forest-binary-plot_audit_result_v2.json", "evaluated_on": "2026-03-23", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 83, "max_score": 100 }, "dynamic_score": { "score": 95.6, "max_score": 100 } }, "release": { "path": "releases/meta-forest-binary-plot/1.0.1.json", "sha256": "1a348d7c2c94ab42d7d94d11fc6f233dcabd06a9452e6973d48148bfaeafff03" }, "artifact": { "path": "shards/96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b.zip", "sha256": "96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b", "bytes": 66876850, "skill_path": "meta-forest-binary-plot" }, "content_sha256": "04c6a1ee230e43a61190c46199bd8edb04b1fce30aa4a84efa9fa6f734b39c98" }, { "id": "meta-forest-continuous-plot", "version": "1.0.1", "display_name": "meta-forest-continuous-plot", "summary": "Generate forest plots for meta-analysis of continuous data. Input a CSV file containing study names, means, standard deviations, and sample sizes for experimental and control groups. Output forest plot PNG and data table CSV.", "category": "Data Analysis", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Data Analysis/meta-forest-continuous-plot" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 85, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Data%20Analysis/meta-forest-continuous-plot/meta-forest-continuous-plot_audit_result_v2.json", "evaluated_on": "2026-03-23", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 83, "max_score": 100 }, "dynamic_score": { "score": 86.6, "max_score": 100 } }, "release": { "path": "releases/meta-forest-continuous-plot/1.0.1.json", "sha256": "9df256e97d3a1c6008be3ff082ecf7765df88a074a1bee48af67d37cb1dc2880" }, "artifact": { "path": "shards/96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b.zip", "sha256": "96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b", "bytes": 66876850, "skill_path": "meta-forest-continuous-plot" }, "content_sha256": "7436cf3c80e2ad11e6757fc103927aebb5042ebc2335015916e4d4826672d994" }, { "id": "meta-forest-model-plot", "version": "1.0.1", "display_name": "meta-forest-model-plot", "summary": "Generate forest plots for meta-analysis of survival data. Input is a CSV file containing study names, HR and 95% confidence intervals, output forest plot PNG and data table CSV. Supports both R and Python scripts.", "category": "Data Analysis", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Data Analysis/meta-forest-model-plot" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 89, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Data%20Analysis/meta-forest-model-plot/meta-forest-model-plot_audit_result_v2.json", "evaluated_on": "2026-03-23", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 83, "max_score": 100 }, "dynamic_score": { "score": 93.6, "max_score": 100 } }, "release": { "path": "releases/meta-forest-model-plot/1.0.1.json", "sha256": "5c0b4c573d52484b784090125d956a1ebf25e334deef3bd19f4cfe9e9dad8737" }, "artifact": { "path": "shards/96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b.zip", "sha256": "96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b", "bytes": 66876850, "skill_path": "meta-forest-model-plot" }, "content_sha256": "c7e552b6d583efe2ec560f53e84c7e87b8ae80ac764650e876685bb219b66db1" }, { "id": "meta-funnel-plot", "version": "1.0.1", "display_name": "meta-funnel-plot", "summary": "Generate Meta-analysis funnel plots and perform publication bias testing. Takes CSV file with Meta-analysis data as input, outputs funnel plot PNG, Egger test and Begg test results.", "category": "Data Analysis", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Data Analysis/meta-funnel-plot" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 85, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Data%20Analysis/meta-funnel-plot/meta-funnel-plot_audit_result_v2.json", "evaluated_on": "2026-03-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 83, "max_score": 100 }, "dynamic_score": { "score": 86.6, "max_score": 100 } }, "release": { "path": "releases/meta-funnel-plot/1.0.1.json", "sha256": "e3363b85bb151394bf2d7517362adbe4b1cf5c739b42e7424ada4717b8ee9afc" }, "artifact": { "path": "shards/96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b.zip", "sha256": "96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b", "bytes": 66876850, "skill_path": "meta-funnel-plot" }, "content_sha256": "a53e0bd462611a8b7201af5ab5f7152cf921a54db33e2888a587dca3ebe88f6a" }, { "id": "meta-picos-generator", "version": "1.0.1", "display_name": "meta-picos-generator", "summary": "Generates PI(E)COS structure (Population, Intervention, Comparator, Outcomes, Study Design) from Meta-analysis or study titles. Use when the user wants to extract these elements from a title.", "category": "Data Analysis", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Data Analysis/meta-picos-generator" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 91, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Data%20Analysis/meta-picos-generator/meta-picos-generator_audit_result_v2.json", "evaluated_on": "2026-03-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 83, "max_score": 100 }, "dynamic_score": { "score": 95.6, "max_score": 100 } }, "release": { "path": "releases/meta-picos-generator/1.0.1.json", "sha256": "c8c9d7bc775411ffcbd9e7027006c72df200b1597ddde22cabd54c82dfc1b6da" }, "artifact": { "path": "shards/96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b.zip", "sha256": "96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b", "bytes": 66876850, "skill_path": "meta-picos-generator" }, "content_sha256": "5c5091125fd5e158938dfc94582f17625bf9b5f96a7eacfefaa86dd591b04c5f" }, { "id": "meta-protocol-writer", "version": "1.0.0", "display_name": "meta-protocol-writer", "summary": "Generates a PROSPERO-compliant Meta-analysis protocol based on Title and PICOS. Use when the user wants to write a protocol for a systematic review or meta-analysis.", "category": "Protocol Design", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Protocol Design/meta-protocol-writer" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 90, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Protocol%20Design/meta-protocol-writer/eval_report_meta-protocol-writer_result.json", "evaluated_on": "2026-06-15", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 96, "max_score": 100 }, "dynamic_score": { "score": 86.2, "max_score": 100 } }, "release": { "path": "releases/meta-protocol-writer/1.0.0.json", "sha256": "6b58764824c2a7e826965b961bc25f76a8c91020f12402a94c155a63136f2057" }, "artifact": { "path": "shards/630d1b4dab37083d632d924264ae93642b8327521aaa70fbf83de3cc7fd53d61.zip", "sha256": "630d1b4dab37083d632d924264ae93642b8327521aaa70fbf83de3cc7fd53d61", "bytes": 542522, "skill_path": "meta-protocol-writer" }, "content_sha256": "b4218e5d78658cf9c6c6b2ad3be0704064bde07456e55cb78c25ac2cbdbd3af4" }, { "id": "meta-radial-plot", "version": "1.0.0", "display_name": "meta-radial-plot", "summary": "Generate radial plots (Radial Plot/Galbraith Plot) for heterogeneity analysis. Visually assess heterogeneity across studies by displaying the relationship between standardized effect sizes and precision. Input: Meta-analysis data in CSV format; Output: Radial plot PNG and data CSV.", "category": "Data Analysis", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Data Analysis/meta-radial-plot" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 85, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Data%20Analysis/meta-radial-plot/meta-radial-plot_audit_result_v2.json", "evaluated_on": "2026-03-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 83, "max_score": 100 }, "dynamic_score": { "score": 86.6, "max_score": 100 } }, "release": { "path": "releases/meta-radial-plot/1.0.0.json", "sha256": "ba0c81e38fcad2139acb6d2f6c25bb52634f10b2b15877d0a3e77144a480d37a" }, "artifact": { "path": "shards/363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb.zip", "sha256": "363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb", "bytes": 1471244, "skill_path": "meta-radial-plot" }, "content_sha256": "f572fe4365ae9b98ba6656610492f824178e748d0f482a5381ab366509dc8e3e" }, { "id": "meta-results-forest-plot-analyzer", "version": "1.0.1", "display_name": "meta-results-forest-plot-analyzer", "summary": "Analyzes forest plots for meta-analysis, generating detailed descriptions and formatting figure legends in Chinese or English. Use when the user wants to interpret a forest plot image, describe its statistical significance (heterogeneity, p-value), and format the output with s...", "category": "Academic Writing", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Academic Writing/meta-results-forest-plot-analyzer" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 90, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Academic%20Writing/meta-results-forest-plot-analyzer/eval_report_meta-results-forest-plot-analyzer_result.json", "evaluated_on": "2026-06-15", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 96, "max_score": 100 }, "dynamic_score": { "score": 86.6, "max_score": 100 } }, "release": { "path": "releases/meta-results-forest-plot-analyzer/1.0.1.json", "sha256": "cc3e2c8177b92337bf281b5f92b004dc566c68c449c28ac0dc5b9177e09503a1" }, "artifact": { "path": "shards/96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b.zip", "sha256": "96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b", "bytes": 66876850, "skill_path": "meta-results-forest-plot-analyzer" }, "content_sha256": "cb1190243f7c5b749c8ce244168495a212fcb3768272e9e7067d6d3683dcb359" }, { "id": "meta-results-funnel-plot-generator", "version": "1.0.1", "display_name": "meta-results-funnel-plot-generator", "summary": "Generates a Meta-analysis results section description for funnel plots, including statistical tables (Egger's, Begg's, Trim & Fill) and figure legends. Supports English and Chinese outputs. Use when user provides a funnel plot image and statistics and wants a formatted report.", "category": "Academic Writing", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Academic Writing/meta-results-funnel-plot-generator" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 90, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Academic%20Writing/meta-results-funnel-plot-generator/eval_report_meta-results-funnel-plot-generator_result.json", "evaluated_on": "2026-06-15", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 96, "max_score": 100 }, "dynamic_score": { "score": 86.2, "max_score": 100 } }, "release": { "path": "releases/meta-results-funnel-plot-generator/1.0.1.json", "sha256": "2b0604f6c7b80619e7f935940bcc09b732581d4833e43f35809d128b86e3f546" }, "artifact": { "path": "shards/96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b.zip", "sha256": "96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b", "bytes": 66876850, "skill_path": "meta-results-funnel-plot-generator" }, "content_sha256": "ed3a17dc49ab3245d9c530768791049a4ab0d5768e729401c75d6269816c5bcb" }, { "id": "meta-results-risk-of-bias", "version": "1.0.1", "display_name": "meta-results-risk-of-bias", "summary": "Generates the \"Risk of Bias\" results section for a meta-analysis based on assessment tables and statistics. Use when the user wants to draft the risk of bias analysis text from provided data tables.", "category": "Academic Writing", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Academic Writing/meta-results-risk-of-bias" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 90, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Academic%20Writing/meta-results-risk-of-bias/eval_report_meta-results-risk-of-bias_result.json", "evaluated_on": "2026-06-15", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 96, "max_score": 100 }, "dynamic_score": { "score": 86.6, "max_score": 100 } }, "release": { "path": "releases/meta-results-risk-of-bias/1.0.1.json", "sha256": "667d516770186ef5869d4c1a70de7c40e9e1f86aa7a89d7260a07d954c4ada03" }, "artifact": { "path": "shards/96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b.zip", "sha256": "96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b", "bytes": 66876850, "skill_path": "meta-results-risk-of-bias" }, "content_sha256": "7ddf2aa678168b4d4ad668bdeeaa4761559f7af4cbfcf9ebebdb104b0fd40f08" }, { "id": "meta-results-sensitivity-analysis", "version": "1.0.1", "display_name": "meta-results-sensitivity-analysis", "summary": "Generates the \"Results\" section for meta-analysis sensitivity analysis based on statistical tables and titles. Use when the user wants to describe sensitivity analysis results or format sensitivity tables for a meta-analysis paper.", "category": "Academic Writing", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Academic Writing/meta-results-sensitivity-analysis" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 90, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Academic%20Writing/meta-results-sensitivity-analysis/eval_report_meta-results-sensitivity-analysis_result.json", "evaluated_on": "2026-06-15", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 96, "max_score": 100 }, "dynamic_score": { "score": 86.6, "max_score": 100 } }, "release": { "path": "releases/meta-results-sensitivity-analysis/1.0.1.json", "sha256": "5b446c880f3a9019e7009c30e4fdfa8df21bfbc84a8582a426771da8357c3468" }, "artifact": { "path": "shards/96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b.zip", "sha256": "96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b", "bytes": 66876850, "skill_path": "meta-results-sensitivity-analysis" }, "content_sha256": "6922fdb4448b68a6bbefa0de02487141ca6957b9cef8ff11645a2ed036b3af6b" }, { "id": "meta-rob-plot", "version": "1.0.0", "display_name": "meta-rob-plot", "summary": "Draw ROB2 risk-of-bias plots, including a Traffic Light Plot and a Summary Bar Plot. Input is a CSV file with ROB2 assessments for each study; output are two PNG plot files.", "category": "Data Analysis", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Data Analysis/meta-rob2-plot" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "release": { "path": "releases/meta-rob-plot/1.0.0.json", "sha256": "276844e809c858ba84a26d4f19d52183757338d0f698334b800998e532131400" }, "artifact": { "path": "shards/363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb.zip", "sha256": "363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb", "bytes": 1471244, "skill_path": "meta-rob-plot" }, "content_sha256": "8f3a6194cdae70c4f1d945381091b57867cad234f6d0ee82c730758680a7ff75" }, { "id": "meta-screening-fulltext", "version": "1.0.0", "display_name": "meta-screening-fulltext", "summary": "Screen full-text papers against inclusion/exclusion criteria, with optional PubMed metadata check using PMID. Use when the user needs to evaluate a paper for a meta-analysis.", "category": "Data Analysis", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d915031495e1c755d272c3de817ee3fc012e8b7b", "path": "scientific-skills/Data Analysis/meta-screening-fulltext" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 85, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d915031495e1c755d272c3de817ee3fc012e8b7b/scientific-skills/Data%20Analysis/meta-screening-fulltext/meta-screening-fulltext_audit_result_v1.json", "evaluated_on": "2026-03-23", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 77, "max_score": 100 }, "dynamic_score": { "score": 90.6, "max_score": 100 } }, "release": { "path": "releases/meta-screening-fulltext/1.0.0.json", "sha256": "c13eb75493efe8a66d066173a1bc9c1a1732eb65ec84e096bb67cd09ad2c94cc" }, "artifact": { "path": "shards/de33edee35bb8ab7aef49234e84a13f7363966440d4d5f381ae005f9e13edd20.zip", "sha256": "de33edee35bb8ab7aef49234e84a13f7363966440d4d5f381ae005f9e13edd20", "bytes": 166464, "skill_path": "meta-screening-fulltext" }, "content_sha256": "d909e9cb203709a2a61ba227042c8edc77b6cb554d9c340ef60ff9b3b6089639" }, { "id": "meta-search-builder", "version": "1.0.0", "display_name": "meta-search-builder", "summary": "Medical literature search strategy generator. Given a user's natural-language description (e.g., meta-analysis topic, PICOS elements, research question), automatically extract medical entities (disease, intervention, population, outcomes) and generate professional search queries for seven major databases (PubMed, Cochrane, Embase, Web of Science, CNKI, Wanfang, VIP). Useful for developing search strategies for systematic reviews and meta-analyses.", "category": "Other", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Other/meta-search-builder" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 86, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Other/meta-search-builder/meta-search-builder_audit_result_v2.json", "evaluated_on": "2026-03-23", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 85, "max_score": 100 }, "dynamic_score": { "score": 86.2, "max_score": 100 } }, "release": { "path": "releases/meta-search-builder/1.0.0.json", "sha256": "466a8d939674fd2d84280ef4521d9185abd15d7e19fda7af3d9ef45a9afcc742" }, "artifact": { "path": "shards/fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0.zip", "sha256": "fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0", "bytes": 1383584, "skill_path": "meta-search-builder" }, "content_sha256": "164017bc7f9b54ec1889ab255f7f3e7e1a64194d3ea26c8738a3037db035beeb" }, { "id": "meta-sensitivity-plot", "version": "1.0.0", "display_name": "meta-sensitivity-plot", "summary": "Generate leave-one-out sensitivity analysis plots for meta-analysis. Input is a CSV file containing meta-analysis data; outputs are a sensitivity forest plot (PNG) and a sensitivity data table (CSV) showing pooled effect estimates after excluding each study in turn.", "category": "Data Analysis", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Data Analysis/meta-sensitivity-plot" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 86, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Data%20Analysis/meta-sensitivity-plot/meta-sensitivity-plot_audit_result_v1.json", "evaluated_on": "2026-03-23", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 79, "max_score": 100 }, "dynamic_score": { "score": 90.6, "max_score": 100 } }, "release": { "path": "releases/meta-sensitivity-plot/1.0.0.json", "sha256": "3ae8c499944ab007e9c96be14ba705a7069e0dec4390e7580ad96620ebef7f01" }, "artifact": { "path": "shards/363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb.zip", "sha256": "363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb", "bytes": 1471244, "skill_path": "meta-sensitivity-plot" }, "content_sha256": "0b405b71683c3a58a57c8a0a5cb3acbc2d0ae475ab0b41f936059335448f0590" }, { "id": "meta-title-generator", "version": "1.0.0", "display_name": "meta-title-generator", "summary": "Generates Meta-Analysis research titles based on user keywords, utilizing PubMed search results if available, or creative generation otherwise. Use when the user wants to brainstorm or generate titles for a meta-analysis, specifically starting from keywords or a topic.", "category": "Data Analysis", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Data Analysis/meta-title-generator" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 85, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Data%20Analysis/meta-title-generator/meta-title-generator_audit_result_v1.json", "evaluated_on": "2026-03-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 77, "max_score": 100 }, "dynamic_score": { "score": 90.6, "max_score": 100 } }, "release": { "path": "releases/meta-title-generator/1.0.0.json", "sha256": "f1c49d27ac86cd5fb3a0526733f27e36416620e29ff8af1a3f37a63be4b1e835" }, "artifact": { "path": "shards/363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb.zip", "sha256": "363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb", "bytes": 1471244, "skill_path": "meta-title-generator" }, "content_sha256": "11eb21875b4ac7d9eb1117d40b861a7b8321608a7a17d9067bd208cdd88b98be" }, { "id": "metabolomics-workbench-database", "version": "1.0.0", "display_name": "metabolomics-workbench-database", "summary": "Access NIH Metabolomics Workbench via REST API (4,200+ studies). Query metabolites, RefMet nomenclature, MS/NMR data, m/z searches, study metadata, for metabolomics and biomarker discovery.", "category": "Data Analysis", "source": { "repository": "https://github.com/K-Dense-AI/scientific-agent-skills", "commit": "71add644263a56368f8680d68df504c6674dc1e5", "path": "scientific-skills/metabolomics-workbench-database" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "K-Dense Inc." } ], "license": "Unknown", "release": { "path": "releases/metabolomics-workbench-database/1.0.0.json", "sha256": "6833d90590332b42acdfcf1df29e3ce5ebd827197eddb95b759ab42f75779f0b" }, "artifact": { "path": "shards/fcd3eaa3439234f967f6835adacfe5bd6e8cacef5cbe5da7a06ffa4dec4f4973.zip", "sha256": "fcd3eaa3439234f967f6835adacfe5bd6e8cacef5cbe5da7a06ffa4dec4f4973", "bytes": 110833, "skill_path": "metabolomics-workbench-database" }, "content_sha256": "3774ecff09c14cf8cade2b8d253a357bcd0cbb4228c178b014db9c1c237c733f" }, { "id": "method-gap-detector", "version": "1.0.0", "display_name": "method-gap-detector", "summary": "Detects methodological gaps across study design, analysis, validation, bias control, reproducibility, and implementation readiness within a biomedical research area. Use this skill when a user wants to identify what current studies are still methodologically missing, which weaknesses are most consequential, and what upgrade path would produce a stronger next-step study. Always separate design gaps, analysis gaps, validation gaps, and reproducibility gaps. Never treat technical complexity as methodological rigor.", "category": "Evidence Insight", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "awesome-med-research-skills/Evidence Insight/method-gap-detector" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 87, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/awesome-med-research-skills/Evidence%20Insight/method-gap-detector/eval_report_method-gap-detector_result.json", "evaluated_on": "2026-04-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 90, "max_score": 100 }, "dynamic_score": { "score": 84.4, "max_score": 100 } }, "release": { "path": "releases/method-gap-detector/1.0.0.json", "sha256": "eb1abebc498a1a230f3e925c367390feb277350e76bd25f3c601fde70ccf62e8" }, "artifact": { "path": "shards/363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb.zip", "sha256": "363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb", "bytes": 1471244, "skill_path": "method-gap-detector" }, "content_sha256": "0cf3ed7805e8349efcd469e51374aecd0d6f75c7f3dcc84b74ea445074e86195" }, { "id": "method-writing", "version": "1.0.1", "display_name": "method-writing", "summary": "Write and revise the Methods section of research papers to ensure reproducibility; use when preparing an IMRAD manuscript or responding to journal/reporting-guideline requirements (e.g., CONSORT/STROBE/PRISMA).", "category": "Academic Writing", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Academic Writing/method-writing" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "Unknown", "evaluation": { "kind": "upstream-self-assessment", "score": 90, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Academic%20Writing/method-writing/eval_report_method-writing_result.json", "evaluated_on": "2026-06-15", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 94, "max_score": 100 }, "dynamic_score": { "score": 86.6, "max_score": 100 } }, "release": { "path": "releases/method-writing/1.0.1.json", "sha256": "be2a6b121613ddbb98e7fbe43aa05ab693545d171370c806a0e3b80309e83b17" }, "artifact": { "path": "shards/96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b.zip", "sha256": "96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b", "bytes": 66876850, "skill_path": "method-writing" }, "content_sha256": "bf8a3ac135130afff45e84abb8be9bf53831265e5dcb113f2402b6064631bf3d" }, { "id": "methodology-extractor", "version": "1.0.0", "display_name": "methodology-extractor", "summary": "Extract experimental methods from one or more papers into a structured protocol-oriented summary; use when comparing methods across studies or drafting reproducible procedures.", "category": "Evidence Insight", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Evidence Insight/methodology-extractor" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 91, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Evidence%20Insight/methodology-extractor/eval_report_methodology-extractor_result.json", "evaluated_on": "2026-06-15", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 98, "max_score": 100 }, "dynamic_score": { "score": 86.6, "max_score": 100 } }, "release": { "path": "releases/methodology-extractor/1.0.0.json", "sha256": "2fafbfe95420ae991ca5b00538e3eb3d8817181d622568c54dca64eb704e89a3" }, "artifact": { "path": "shards/363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb.zip", "sha256": "363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb", "bytes": 1471244, "skill_path": "methodology-extractor" }, "content_sha256": "8fa3d5d0b2accda48db42ca9f8f7792bb1a6d9c91bab122fd89e3f763df39892" }, { "id": "methods-reverse-engineer", "version": "1.0.0", "display_name": "methods-reverse-engineer", "summary": "Reverse-engineers the methods section of a biomedical paper into a structured, reproducible workflow. Use this skill when a user wants to understand how a study was actually executed, extract data sources, inclusion/exclusion logic, preprocessing, analytical sequence, software/tools, validation path, and critical parameters, or build a replication checklist from a paper, abstract, DOI, PMID, title, screenshot, or partial methods text. Do not treat this as generic summarization. Focus on reconstructing the operational method pipeline, surfacing missing reproducibility details, and distinguishing explicitly reported steps from inferred or unresolved ones. Never fabricate references, methods details, identifiers, software versions, parameters, datasets, or validation steps.", "category": "Evidence Insight", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "awesome-med-research-skills/Evidence Insight/methods-reverse-engineer" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 90, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/awesome-med-research-skills/Evidence%20Insight/methods-reverse-engineer/eval_report_methods-reverse-engineer_result.json", "evaluated_on": "2026-04-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 95, "max_score": 100 }, "dynamic_score": { "score": 86.4, "max_score": 100 } }, "release": { "path": "releases/methods-reverse-engineer/1.0.0.json", "sha256": "fc6865411364e44a30048cca566a899f05eb1c9d45e41372c0b3e8df2579c433" }, "artifact": { "path": "shards/1d2612a702320846b10cda57f9f37ce17f5e19186095f2fe96bfa6e44eb077e8.zip", "sha256": "1d2612a702320846b10cda57f9f37ce17f5e19186095f2fe96bfa6e44eb077e8", "bytes": 234009, "skill_path": "methods-reverse-engineer" }, "content_sha256": "ba7ecc56d5422ef73c9bc9d2a46d304baa5a43c0e030db6f3fdcfbdbb067ac63" }, { "id": "methods-section-writer", "version": "1.0.1", "display_name": "methods-section-writer", "summary": "Turns your protocol and analysis workflow into publication-ready Methods text. Use when writing or revising the Methods section of a biomedical manuscript, ensuring it complies with reporting guidelines (CONSORT, STROBE, PRISMA, TRIPOD), matches what is in the Results section, and satisfies journal-specific word limits and declarations. Also triggers on \"write my methods\", \"revise my methods section\", \"how to report my statistics\", \"what do I need to include in methods for [study type]\", or \"make my methods CONSORT-compliant\".", "category": "Academic Writing", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "awesome-med-research-skills/Academic Writing/methods-section-writer" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "Unknown", "evaluation": { "kind": "upstream-self-assessment", "score": 92, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/awesome-med-research-skills/Academic%20Writing/methods-section-writer/eval_report_methods-section-writer_result.json", "evaluated_on": "2026-04-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 95, "max_score": 100 }, "dynamic_score": { "score": 89.4, "max_score": 100 } }, "release": { "path": "releases/methods-section-writer/1.0.1.json", "sha256": "73539e1153d6ea1fdce32c0b091ec36907fbc965ed2de7e540de009e0907458d" }, "artifact": { "path": "shards/96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b.zip", "sha256": "96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b", "bytes": 66876850, "skill_path": "methods-section-writer" }, "content_sha256": "3cb58eedc1e741c574be450d76a0aff32c3247be6ae088f61b3e2bffa81ccdb0" }, { "id": "microbiome-diversity-reporter", "version": "1.0.1", "display_name": "microbiome-diversity-reporter", "summary": "Interpret Alpha and Beta diversity metrics from 16S rRNA sequencing results.", "category": "Academic Writing", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Academic Writing/microbiome-diversity-reporter" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 85, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Academic%20Writing/microbiome-diversity-reporter/microbiome-diversity-reporter_audit_result_v2.json", "evaluated_on": "2026-03-23", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 88, "max_score": 100 }, "dynamic_score": { "score": 83.6, "max_score": 100 } }, "release": { "path": "releases/microbiome-diversity-reporter/1.0.1.json", "sha256": "ddf8e75567889ac25d8028b76f00aae7773634305121f1152daf1821803f9b6d" }, "artifact": { "path": "shards/96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b.zip", "sha256": "96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b", "bytes": 66876850, "skill_path": "microbiome-diversity-reporter" }, "content_sha256": "78ed770083fddc12f923c3e13333de31ae4f5faa238b486437f63e9298e975ee" }, { "id": "microscopy-scale-bar-adder", "version": "1.0.0", "display_name": "microscopy-scale-bar-adder", "summary": "Add accurate, publication-ready scale bars to microscopy images given pixel-to-unit calibration data.", "category": "Other", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Other/microscopy-scale-bar-adder" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 89, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Other/microscopy-scale-bar-adder/microscopy-scale-bar-adder_audit_result_v4.json", "evaluated_on": "2026-03-19", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 90, "max_score": 100 }, "dynamic_score": { "score": 88.8, "max_score": 100 } }, "release": { "path": "releases/microscopy-scale-bar-adder/1.0.0.json", "sha256": "806e773a585fec5792faf658987d52d4173dbfcec29dbd4da83d10c6f9787c70" }, "artifact": { "path": "shards/fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0.zip", "sha256": "fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0", "bytes": 1383584, "skill_path": "microscopy-scale-bar-adder" }, "content_sha256": "44014772944d288f6c4fc390401445b01feea976580fc1bed3d85b2915706103" }, { "id": "mindmap", "version": "1.0.0", "display_name": "mindmap", "summary": "Generate mindmaps locally/offline with native HTML/CSS/JS (no external libraries); use when you need to parse Plain Text node structures or map LLM outputs into an on-device visualization.", "category": "Other", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Other/mindmap" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 86, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Other/mindmap/mindmap_audit_result_v2.json", "evaluated_on": "2026-03-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 85, "max_score": 100 }, "dynamic_score": { "score": 86.2, "max_score": 100 } }, "release": { "path": "releases/mindmap/1.0.0.json", "sha256": "5a131804db0555ffd20c1259c68d5c988aa856d654ad476171ac6c0c99e9087f" }, "artifact": { "path": "shards/fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0.zip", "sha256": "fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0", "bytes": 1383584, "skill_path": "mindmap" }, "content_sha256": "142167c53403cb2399124dccc45e579ef53fbc8da1ae38b073cce05debe5684b" }, { "id": "mindmap-helper", "version": "1.0.0", "display_name": "mindmap-helper", "summary": "Organize requirements into Markdown/JSON mind map structures; use when you need a hierarchical outline for mind map tools (e.g., XMind, ProcessOn, FreeMind) or for documentation planning.", "category": "Other", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Other/mindmap-helper" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 86, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Other/mindmap-helper/mindmap-helper_audit_result_v2.json", "evaluated_on": "2026-03-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 85, "max_score": 100 }, "dynamic_score": { "score": 86.2, "max_score": 100 } }, "release": { "path": "releases/mindmap-helper/1.0.0.json", "sha256": "47ccd94c4103dd3aba2d2adc0ee447c2d85c96e59da56f945cdad3887f0ced63" }, "artifact": { "path": "shards/fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0.zip", "sha256": "fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0", "bytes": 1383584, "skill_path": "mindmap-helper" }, "content_sha256": "e34d4a7e20d7285398e7c36ad7d4c3e10c502ba78678e1c8f902bf3c579c52e3" }, { "id": "moa-explainer", "version": "1.0.1", "display_name": "moa-explainer", "summary": "Generate 3D animation scripts and lay explanations for drug mechanisms.", "category": "Academic Writing", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Academic Writing/moa-explainer" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 85, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Academic%20Writing/moa-explainer/moa-explainer_audit_result_v2.json", "evaluated_on": "2026-03-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 88, "max_score": 100 }, "dynamic_score": { "score": 83.6, "max_score": 100 } }, "release": { "path": "releases/moa-explainer/1.0.1.json", "sha256": "a0bada7a16f507f6263a5a53a93b055feb285d2f2cd042016d4493581e4b43ef" }, "artifact": { "path": "shards/96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b.zip", "sha256": "96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b", "bytes": 66876850, "skill_path": "moa-explainer" }, "content_sha256": "9d5389f417ba43f6e260cb9a3af624176506def4da78cf6d606062cb7f30f09b" }, { "id": "model-calibration-curve", "version": "1.0.1", "display_name": "model-calibration-curve", "summary": "Use when assessing how well a survival model's predicted probabilities agree with observed outcomes by fitting a Cox model and generating bootstrap calibration curves at one or more prediction horizons from a clinical CSV file. NOT for: nomogram construction, univariate Cox screening, ROC analysis, or decision-curve analysis.", "category": "Data Analysis", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "awesome-med-research-skills/Data Analysis/model-calibration-curve" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "license": "Unknown", "evaluation": { "kind": "upstream-self-assessment", "score": 95, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/awesome-med-research-skills/Data%20Analysis/model-calibration-curve/eval_report_model-calibration-curve_result.json", "evaluated_on": "2026-04-27", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 96, "max_score": 100 }, "dynamic_score": { "score": 94.4, "max_score": 100 } }, "release": { "path": "releases/model-calibration-curve/1.0.1.json", "sha256": "070e14436e01e445cf555fd6ea1f9faac196cfb694b0739573c094ea55f2410e" }, "artifact": { "path": "shards/96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b.zip", "sha256": "96e0bee15398ff148a6f7b7621559d78a91f04db18abf812f38c34fc1cbc321b", "bytes": 66876850, "skill_path": "model-calibration-curve" }, "content_sha256": "de617aabe07f90c89a5e8818e668dd5b2db2834670c5670be5edb8ad8698c06c" }, { "id": "molmim-nim", "version": "1.0.0", "display_name": "molmim-nim", "summary": "Use this skill for MolMIM, NVIDIA's BioNeMo NIM microservice for small-molecule latent-space generation and optimization. Invoke for MolMIM, molecular embeddings, hidden states, latent decoding, sampling around a seed SMILES, CMA-ES guided molecule generation, QED or plogP optimization, hosted NVIDIA API calls, or local Docker deployment.\n", "category": "Data Analysis", "source": { "repository": "https://github.com/NVIDIA-BioNeMo/bionemo-agent-toolkit", "commit": "0e67a612e4045f007e38fa77adc8f3ebfc5616b6", "path": "skills/bionemo-agent-toolkit/skills/molmim-nim" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "license": "Apache-2.0 AND CC-BY-4.0", "release": { "path": "releases/molmim-nim/1.0.0.json", "sha256": "f4f7e7aacd0babe7f16b65f1700d73623c3fa931967bc59e9a096dec1dab6060" }, "artifact": { "path": "shards/d9eadedf27584c96257c396b3554a2f479383e134ebaf455716ea80dd4f8de94.zip", "sha256": "d9eadedf27584c96257c396b3554a2f479383e134ebaf455716ea80dd4f8de94", "bytes": 1161536, "skill_path": "molmim-nim" }, "content_sha256": "941c917f1b568eabc2ad0367c38b006010d218cbb2893eff4a68fbce17713409" }, { "id": "motif-logo-generator", "version": "1.0.0", "display_name": "motif-logo-generator", "summary": "Generate publication-quality sequence logos for DNA or protein motifs.", "category": "Data Analysis", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Data Analysis/motif-logo-generator" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 86, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Data%20Analysis/motif-logo-generator/motif-logo-generator_audit_result_v2.json", "evaluated_on": "2026-03-23", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 83, "max_score": 100 }, "dynamic_score": { "score": 87.2, "max_score": 100 } }, "release": { "path": "releases/motif-logo-generator/1.0.0.json", "sha256": "a6dd5d78f739c1a1a2c4bd0f863b63c500564f1c695f2a7fdb5b892337d329b1" }, "artifact": { "path": "shards/363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb.zip", "sha256": "363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb", "bytes": 1471244, "skill_path": "motif-logo-generator" }, "content_sha256": "7e17d9c47b17b34854dd4f4a645699ed8c6600107bbfb93d71fb583a0c027157" }, { "id": "mr-scrna-research-planner", "version": "1.0.0", "display_name": "mr-scrna-research-planner", "summary": "Generates complete Mendelian Randomization + single-cell transcriptomics (scRNA-seq) research designs from a user-provided direction. Always use this skill whenever a user wants to design, plan, or build a study combining MR and single-cell data — even if phrased as \"help me write a paper on X\", \"design a bioinformatics study for Y\", or \"I want to study Z using MR and scRNA\". Covers five study patterns (mechanism gene-set, key-cell, candidate-gene reverse validation, exposure-disease-cell triangulation, translational biomarker) and always outputs four workload configs (Lite / Standard / Advanced / Publication+) with recommended primary plan, step-by-step workflow, figure plan, validation strategy, minimal executable version, publication upgrade path, and a strictly verified reference literature retrieval layer with real references only.", "category": "Protocol Design", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "awesome-med-research-skills/Protocol Design/mr-scrna-research-planner" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 90, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/awesome-med-research-skills/Protocol%20Design/mr-scrna-research-planner/eval_report_mr-scrna-research-planner_result.json", "evaluated_on": "2026-04-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 93, "max_score": 100 }, "dynamic_score": { "score": 88.7, "max_score": 100 } }, "release": { "path": "releases/mr-scrna-research-planner/1.0.0.json", "sha256": "1e3e70a32a4e83ba7f4786298ae5821b1bef8f5edfcc4f22bbd62610b4c460e7" }, "artifact": { "path": "shards/630d1b4dab37083d632d924264ae93642b8327521aaa70fbf83de3cc7fd53d61.zip", "sha256": "630d1b4dab37083d632d924264ae93642b8327521aaa70fbf83de3cc7fd53d61", "bytes": 542522, "skill_path": "mr-scrna-research-planner" }, "content_sha256": "be94f527586263f912af110c2c0774d4af9e229d14d14b6b658003138d809cdb" }, { "id": "msa-search-nim", "version": "1.0.0", "display_name": "msa-search-nim", "summary": "Generate multiple sequence alignments (MSAs) for protein sequences using the ColabFold MSA-Search NIM. Use for homolog search, UniRef30/ColabFold env searches, A3M or FASTA alignments, paired MSA search for complexes, PDB70 structural templates, hosted NVIDIA API calls, or local Docker deployment. For local deployment, download the databases in parallel with aria2c and launch via NIM_MODEL_NAME (the recommended default fast path, ~14 min vs over 80 min for the built-in downloader); a plain docker run uses the slow built-in downloader.\n", "category": "Data Analysis", "source": { "repository": "https://github.com/NVIDIA-BioNeMo/bionemo-agent-toolkit", "commit": "0e67a612e4045f007e38fa77adc8f3ebfc5616b6", "path": "skills/bionemo-agent-toolkit/skills/msa-search-nim" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "license": "Apache-2.0 AND CC-BY-4.0", "release": { "path": "releases/msa-search-nim/1.0.0.json", "sha256": "b2c9e53c70d0a9a30a10c52f00fb2eb4d1d8391f5e8c0aff2c2a33365d24bfe9" }, "artifact": { "path": "shards/d9eadedf27584c96257c396b3554a2f479383e134ebaf455716ea80dd4f8de94.zip", "sha256": "d9eadedf27584c96257c396b3554a2f479383e134ebaf455716ea80dd4f8de94", "bytes": 1161536, "skill_path": "msa-search-nim" }, "content_sha256": "3b57d5c75d6fcbe2908f0adff1fe4e321ca8b11c57c89cdeb97c37e43d7d7f49" }, { "id": "msa-structure-prediction-pipeline", "version": "1.0.0", "display_name": "msa-structure-prediction-pipeline", "summary": "NOTE: your protein sequence and the retrieved MSA alignment are transmitted to external NVIDIA-hosted APIs (health.api.nvidia.com) on every call. Use local NIM containers for confidential or proprietary sequences. Run a complete protein structure prediction pipeline using NVIDIA BioNeMo NIMs: search for MSA alignments with MSA-Search (ColabFold), then predict the structure with OpenFold3 using the retrieved alignments. Use this skill whenever the user wants to predict a protein structure with maximum accuracy using MSA context, run the full AlphaFold3-style pipeline, generate MSA-informed structure predictions, or improve structure prediction accuracy by providing evolutionary information. Triggers on: MSA structure prediction pipeline, structure prediction pipeline, MSA-informed prediction, OpenFold3, ColabFold MSA, AlphaFold3 pipeline, protein structure, homology search, a3m alignment, UniRef30, NIM microservice. This pipeline chains MSA-Search and OpenFold3.\n", "category": "Protocol Design", "source": { "repository": "https://github.com/NVIDIA-BioNeMo/bionemo-agent-toolkit", "commit": "0e67a612e4045f007e38fa77adc8f3ebfc5616b6", "path": "skills/bionemo-agent-toolkit/skills/msa-structure-prediction-pipeline" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "license": "Apache-2.0 AND CC-BY-4.0", "release": { "path": "releases/msa-structure-prediction-pipeline/1.0.0.json", "sha256": "0bd8465d8c1f09ef71df08bd94c3cb07a2cf8cee8fc4658840032d3107884f31" }, "artifact": { "path": "shards/d9eadedf27584c96257c396b3554a2f479383e134ebaf455716ea80dd4f8de94.zip", "sha256": "d9eadedf27584c96257c396b3554a2f479383e134ebaf455716ea80dd4f8de94", "bytes": 1161536, "skill_path": "msa-structure-prediction-pipeline" }, "content_sha256": "c016b1c7351d0765f9cd926fc90cf79ee34cb0ae37efaf79a0cb536854c17e8b" }, { "id": "multi-database-literature-collector", "version": "1.0.0", "display_name": "multi-database-literature-collector", "summary": "Collects candidate biomedical literature across multiple databases, adapts search logic by database, preserves source metadata, and organizes results into a structured, screening-ready candidate pool. Always use this skill when a user wants cross-database literature collection, search strategy construction, candidate paper aggregation, or first-pass evidence organization before deduplication, screening, layered reading, or review planning. Requires real and verifiable literature records only. Every formal literature item must include a real link and DOI when available; never fabricate citations, titles, authors, years, journals, abstracts, PMIDs, or DOIs. If a DOI is unavailable or cannot be verified, state that explicitly rather than inventing one.", "category": "Evidence Insight", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "awesome-med-research-skills/Evidence Insight/multi-database-literature-collector" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 87, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/awesome-med-research-skills/Evidence%20Insight/multi-database-literature-collector/eval_report_multi-database-literature-collector_result.json", "evaluated_on": "2026-04-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 91, "max_score": 100 }, "dynamic_score": { "score": 84.9, "max_score": 100 } }, "release": { "path": "releases/multi-database-literature-collector/1.0.0.json", "sha256": "2a4088a1ff334384704b2a6b4fda21a9a0d5a204ea0e1d801165263cda1153c6" }, "artifact": { "path": "shards/363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb.zip", "sha256": "363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb", "bytes": 1471244, "skill_path": "multi-database-literature-collector" }, "content_sha256": "c78382b168e5b49ab89898fb52ee1f59f8fc0046fddf83cbcd2c99e3194c1fef" }, { "id": "multi-omics-clinical-integration-planner", "version": "1.0.0", "display_name": "multi-omics-clinical-integration-planner", "summary": "Designs complete research plans that integrate clinical variables with multi-omics data from a user-provided biomedical direction. Always use this skill whenever a user wants to design, scope, or structure a study that combines clinical variables with transcriptomics, proteomics, metabolomics, epigenomics, or related omics layers for mechanism interpretation, biomarker development, risk stratification, treatment-response analysis, or translational use. It should define the clinical use case, alignment across data layers, feature-reduction and fusion logic, modeling route, mechanism-interpretation layer, validation ladder, and four workload configurations (Lite / Standard / Advanced / Publication+). Never fabricate datasets, accession numbers, sample counts, metadata completeness, platform coverage, literature references, PMIDs, DOIs, or validation status. Always include the mandatory Dataset Disclaimer immediately before any workflow section that mentions datasets or public resources.", "category": "Protocol Design", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "awesome-med-research-skills/Protocol Design/multi-omics-clinical-integration-planner" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 90, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/awesome-med-research-skills/Protocol%20Design/multi-omics-clinical-integration-planner/eval_report_multi-omics-clinical-integration-planner_result.json", "evaluated_on": "2026-04-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 93, "max_score": 100 }, "dynamic_score": { "score": 87.7, "max_score": 100 } }, "release": { "path": "releases/multi-omics-clinical-integration-planner/1.0.0.json", "sha256": "b970d8ff26a357a121bf2b1dab918f51bed4ba2961844477f965f5c09d7c25b9" }, "artifact": { "path": "shards/630d1b4dab37083d632d924264ae93642b8327521aaa70fbf83de3cc7fd53d61.zip", "sha256": "630d1b4dab37083d632d924264ae93642b8327521aaa70fbf83de3cc7fd53d61", "bytes": 542522, "skill_path": "multi-omics-clinical-integration-planner" }, "content_sha256": "e5cf12fbef85b2109604d65253551ec2f1c44b3e1b181ba6cac010a39ec3c466" }, { "id": "multi-panel-figure-assembler", "version": "1.0.0", "display_name": "multi-panel-figure-assembler", "summary": "Assemble 6 sub-figures (A–F) into a high-resolution composite figure with consistent labels, padding, and publication-ready DPI.", "category": "Other", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Other/multi-panel-figure-assembler" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 82, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Other/multi-panel-figure-assembler/multi-panel-figure-assembler_audit_result_v4.json", "evaluated_on": "2026-03-19", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 85, "max_score": 100 }, "dynamic_score": { "score": 80.4, "max_score": 100 } }, "release": { "path": "releases/multi-panel-figure-assembler/1.0.0.json", "sha256": "9353bb3995122879e9692ad11aabbd9bc5735c332c43ab7b039f3e8790dbcb57" }, "artifact": { "path": "shards/fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0.zip", "sha256": "fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0", "bytes": 1383584, "skill_path": "multi-panel-figure-assembler" }, "content_sha256": "05b1182fcf0789110ca26889d05c76a1478bbbdb9644479831f95d320b7ffe83" }, { "id": "nature-academic-search", "version": "1.0.1", "display_name": "nature-academic-search", "summary": "Multi-source literature search, citation verification, strict independent other-citation audits, article-level citation metric tables, influential citer profiling with citation-context extraction, MeSH search strategy, citation file management (.nbib/.ris/.bib conversion), and reference management (BibTeX, related articles, ID conversion) via MCP tools (PubMed, CrossRef, arXiv, Scopus, ScienceDirect). Use for coordinated literature workflows beyond one MCP call, including 文献检索、 查文献、找文献、文献综述检索、查论文、引文核对、参考文献管理、文献去重、 严格他引、他引判定、排除自引、谁引用了我的文章、引用我的文章的人有没有大牛、 院士引用、校长引用、院长引用、杰青引用、长江学者引用、Fellow引用、文章引用表、 指定文章引用数、严格他引数、整理成表格.", "category": "Evidence Insight", "source": { "repository": "https://github.com/Yuan1z0825/nature-skills", "commit": "558eae555d64e09b152efc449ca274bbd7747947", "path": "skills/nature-academic-search" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "license": "Unknown", "release": { "path": "releases/nature-academic-search/1.0.1.json", "sha256": "caf361ea72162f9fa936112a31cade21adacaf450a4ed9035866dda86487ea2c" }, "artifact": { "path": "shards/2b2d134141e3d5698b9e174f154599abf13d7ddd3c9060e29ef536399fd82407.zip", "sha256": "2b2d134141e3d5698b9e174f154599abf13d7ddd3c9060e29ef536399fd82407", "bytes": 17034738, "skill_path": "nature-academic-search" }, "content_sha256": "b428355fd6362851ca1c6d15606dcb9929e05f015889011947e45ae7c22d6f69" }, { "id": "nature-citation", "version": "1.0.1", "display_name": "nature-citation", "summary": "Add strict Nature/CNS citations to manuscript text by splitting long passages into citable segments, searching only accepted flagship and subjournal titles from Nature Portfolio, the AAAS Science family, and Cell Press, filtering by publication time range, and exporting one reference-manager-ready output by default. Use this skill whenever the user asks to input text and automatically get references, add citations to a paragraph/manuscript, find Nature-series or CNS support for statements, create text-to-reference correspondence, \"分段引用\", \"自动给出引用\", \"Nature系列引用\", \"CNS及子刊\", \"支撑文献\", \"补引用\", \"找引用\", or export EndNote/RIS/ENW/Zotero RDF. Also trigger on general academic-writing citation needs even without the word \"Nature\", such as adding references while writing a paper, finding sources/literature for a claim, building a reference list, citation/referencing for academic writing, and Chinese phrasings like 学术写作引用、写论文加引用、写paper找文献、加参考文献、配文献、引用文献、文献支撑.", "category": "Academic Writing", "source": { "repository": "https://github.com/Yuan1z0825/nature-skills", "commit": "558eae555d64e09b152efc449ca274bbd7747947", "path": "skills/nature-citation" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "Yuan1z skill, refactored into static/dynamic layers" } ], "license": "Unknown", "release": { "path": "releases/nature-citation/1.0.1.json", "sha256": "268b7da50d97380e4fa6f822ced8273c73cdc177f5ee30c768e979ef291435ec" }, "artifact": { "path": "shards/2b2d134141e3d5698b9e174f154599abf13d7ddd3c9060e29ef536399fd82407.zip", "sha256": "2b2d134141e3d5698b9e174f154599abf13d7ddd3c9060e29ef536399fd82407", "bytes": 17034738, "skill_path": "nature-citation" }, "content_sha256": "91a4130d07ff842b43ef977f312e1498e99cc9b62fb5d5ecb52a23b0eca7bdeb" }, { "id": "nature-downloader", "version": "1.0.1", "display_name": "nature-downloader", "summary": "Use when a user needs lawful academic full text, CNKI institutional access, English OA retrieval, publisher API access, institutional browser fallback, or supporting information downloads.", "category": "Evidence Insight", "source": { "repository": "https://github.com/Yuan1z0825/nature-skills", "commit": "558eae555d64e09b152efc449ca274bbd7747947", "path": "skills/nature-downloader" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "license": "Unknown", "release": { "path": "releases/nature-downloader/1.0.1.json", "sha256": "5942f912b6b1284fbe1f9d5a6554b81a721d56f9ff64ec126fec079b708adf0b" }, "artifact": { "path": "shards/2b2d134141e3d5698b9e174f154599abf13d7ddd3c9060e29ef536399fd82407.zip", "sha256": "2b2d134141e3d5698b9e174f154599abf13d7ddd3c9060e29ef536399fd82407", "bytes": 17034738, "skill_path": "nature-downloader" }, "content_sha256": "00141de4244e9069cba8c034666fe23ca04f8c16b0b483f94a7694dcf432d230" }, { "id": "nature-experiment-log", "version": "1.0.0", "display_name": "nature-experiment-log", "summary": "标准化实验日志记录——直接上传或读取本地图片、语音和文字，产出带 YAML frontmatter 的 Markdown；可选集成飞书 CLI 与 Obsidian。", "category": "Other", "source": { "repository": "https://github.com/Yuan1z0825/nature-skills", "commit": "558eae555d64e09b152efc449ca274bbd7747947", "path": "skills/nature-experiment-log" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "Jiahao8595" } ], "license": "MIT", "release": { "path": "releases/nature-experiment-log/1.0.0.json", "sha256": "b94d157d2f3e3854b66a80e1f7b7801db5b3e6d6d28a7384c01d389e3fc728d7" }, "artifact": { "path": "shards/fbeb5f12afe3c0c6924b0974d44144de230c07662f05cd53d487f267a6a0eb70.zip", "sha256": "fbeb5f12afe3c0c6924b0974d44144de230c07662f05cd53d487f267a6a0eb70", "bytes": 244577, "skill_path": "nature-experiment-log" }, "content_sha256": "2b4e6f2e020895c6c1882c8ccc76d8f7063b0859e7faf52a63cb249e98ee8202" }, { "id": "nature-image2ppt", "version": "1.0.1", "display_name": "nature-image2ppt", "summary": "Convert slide images, screenshots, scanned PDFs, and image-only PPT/PPTX files into high-fidelity object-level editable PowerPoint, including semantic-region mixed reconstruction, measured flowcharts and knowledge graphs, native circle nodes and connectors, single-object thin and filled arrows, speaker-note preservation, and rendered QA. Use for 图片转可编辑PPT、截图还原PPT、扫描PDF恢复、图片型PPTX转换、流程图/知识图谱/复合图形/箭头重建; not for authoring a new deck from notes.", "category": "Academic Writing", "source": { "repository": "https://github.com/Yuan1z0825/nature-skills", "commit": "558eae555d64e09b152efc449ca274bbd7747947", "path": "skills/nature-image2ppt" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "license": "Unknown", "release": { "path": "releases/nature-image2ppt/1.0.1.json", "sha256": "07b2f1d71ddc914a28b9b01dbb56381a73c018be96927b0f2e8ed49a3372e934" }, "artifact": { "path": "shards/2b2d134141e3d5698b9e174f154599abf13d7ddd3c9060e29ef536399fd82407.zip", "sha256": "2b2d134141e3d5698b9e174f154599abf13d7ddd3c9060e29ef536399fd82407", "bytes": 17034738, "skill_path": "nature-image2ppt" }, "content_sha256": "7a1371c98cedfb5d9532389687fb769793206ac92070fa20c72a135816e17cf3" }, { "id": "nature-literature-pipeline", "version": "1.0.0", "display_name": "nature-literature-pipeline", "summary": "Complete automated literature discovery pipeline: multi-source search → six-dimension scoring → fine reading → formatted delivery → archival.\nCombines a configurable engine with daily cron-driven application layer. Works with Feishu, Telegram, or any messaging platform.\n", "category": "Evidence Insight", "source": { "repository": "https://github.com/Yuan1z0825/nature-skills", "commit": "558eae555d64e09b152efc449ca274bbd7747947", "path": "skills/nature-literature-pipeline" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "Jiahao8595" } ], "license": "MIT", "release": { "path": "releases/nature-literature-pipeline/1.0.0.json", "sha256": "8fb191a6dceb64c2d821892e2c9f9ae959fa5b5e77644a8ed53f7d97d9d6ba7d" }, "artifact": { "path": "shards/fbeb5f12afe3c0c6924b0974d44144de230c07662f05cd53d487f267a6a0eb70.zip", "sha256": "fbeb5f12afe3c0c6924b0974d44144de230c07662f05cd53d487f267a6a0eb70", "bytes": 244577, "skill_path": "nature-literature-pipeline" }, "content_sha256": "5cc59159299e144677783f716f981895d7655dc660ba2e223a17e5b73d4b1966" }, { "id": "nature-paper-to-patent", "version": "1.0.1", "display_name": "nature-paper-to-patent", "summary": "Convert scientific papers, theses, technical reports, source code, figures, inventor notes, or research manuscripts into evidence-grounded Chinese invention patent drafts and attorney-facing technical disclosure materials. Use when an AI agent must mine patent points, draft or revise a Chinese technical disclosure (技术交底书), run prior-art comparison, convert Office project materials, map every claimed feature to source evidence, preserve core formulas as editable Office Math, generate claim-aligned flowcharts and methodology figures, compare a paper with an existing patent, audit support and consistency, or deliver Chinese DOCX patent/disclosure files.", "category": "Academic Writing", "source": { "repository": "https://github.com/Yuan1z0825/nature-skills", "commit": "558eae555d64e09b152efc449ca274bbd7747947", "path": "skills/nature-paper-to-patent" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "license": "Unknown", "release": { "path": "releases/nature-paper-to-patent/1.0.1.json", "sha256": "15f4b3192f077f081a39e2c4d63b46608ebf3b84bbb560b9baf0f96a93b792f9" }, "artifact": { "path": "shards/2b2d134141e3d5698b9e174f154599abf13d7ddd3c9060e29ef536399fd82407.zip", "sha256": "2b2d134141e3d5698b9e174f154599abf13d7ddd3c9060e29ef536399fd82407", "bytes": 17034738, "skill_path": "nature-paper-to-patent" }, "content_sha256": "1437b3fc928dd42c6b109196f3a62b8e31a1bd28ca5ad39dcc601e6fab1fed8b" }, { "id": "nature-ref-verifier", "version": "1.0.1", "display_name": "nature-ref-verifier", "summary": "对学术文献逐条执行多源交叉验证，逐字段对比作者、标题、年份、卷期、页码， 标记卷年/DOI年冲突、作者顺序异常、页码偏差等问题，输出结构化验证报告。 可批量处理整篇论文/开题报告的参考文献列表，也可单条校验，支持与 Zotero 同步修正。", "category": "Evidence Insight", "source": { "repository": "https://github.com/Yuan1z0825/nature-skills", "commit": "558eae555d64e09b152efc449ca274bbd7747947", "path": "skills/nature-ref-verifier" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "license": "Unknown", "release": { "path": "releases/nature-ref-verifier/1.0.1.json", "sha256": "270376bf65197f734194542c24e1cc2f8e900a33a204d5f0ee3d8025a6211647" }, "artifact": { "path": "shards/2b2d134141e3d5698b9e174f154599abf13d7ddd3c9060e29ef536399fd82407.zip", "sha256": "2b2d134141e3d5698b9e174f154599abf13d7ddd3c9060e29ef536399fd82407", "bytes": 17034738, "skill_path": "nature-ref-verifier" }, "content_sha256": "dd390b4e7b0f2bfebf1a2817b05492066bb134be452815ca0fa884474d28a8ea" }, { "id": "network-tox-docking-research-planner", "version": "1.0.0", "display_name": "network-tox-docking-research-planner", "summary": "Generates complete network toxicology + molecular docking research designs from a user-provided toxicant and disease/phenotype. Always use this skill when users want to investigate how an environmental toxicant, endocrine disruptor, heavy metal, food contaminant, pharmaceutical residue, or consumer product chemical may contribute to a disease through shared molecular targets, hub genes, pathways, and docking evidence. Trigger for:\"network toxicology study\", \"toxicology mechanism paper\", \"target prediction + PPI + docking\", \"environmental pollutant and disease mechanism\", \"hub genes and docking for toxicant\", \"Lite/Standard/Advanced toxicology plan\", \"CTD + SwissTargetPrediction + GeneCards + STRING\", \"CB-Dock2 docking study\", \"triclosan/BPA/cadmium/PFAS + disease\". Also triggers for Chinese phrasings:\"网络毒理学研究设计\"、\"毒物机制论文\"、\"靶点预测+PPI+对接\"、\"环境污染物与疾病机制\". Trigger even for casual phrasings like \"I want to study how chemical X affects disease Y\" or \"help me design a toxicology paper\". Always output four workload configurations (Lite / Standard / Advanced / Publication+) with a recommended primary plan, step-by-step workflow, figure plan, validation strategy, minimal executable version, and publication upgrade path.", "category": "Protocol Design", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Protocol Design/network-tox-docking-research-planner" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "release": { "path": "releases/network-tox-docking-research-planner/1.0.0.json", "sha256": "651c2d94d381b2405f7aa8553386a773c95669cddb15bc1d9d93c1700f9ba99e" }, "artifact": { "path": "shards/630d1b4dab37083d632d924264ae93642b8327521aaa70fbf83de3cc7fd53d61.zip", "sha256": "630d1b4dab37083d632d924264ae93642b8327521aaa70fbf83de3cc7fd53d61", "bytes": 542522, "skill_path": "network-tox-docking-research-planner" }, "content_sha256": "6298451f1f2b8a82baf5080a77a82f545f373519984e6c56ac83e980c0e21d1f" }, { "id": "networking-email-drafter", "version": "1.0.1", "display_name": "networking-email-drafter", "summary": "Draft professional follow-up emails to contacts made at conferences - not too pushy, but memorable.", "category": "Academic Writing", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Academic Writing/networking-email-drafter" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 85, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Academic%20Writing/networking-email-drafter/networking-email-drafter_audit_result_v2.json", "evaluated_on": "2026-03-23", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 88, "max_score": 100 }, "dynamic_score": { "score": 83.6, "max_score": 100 } }, "release": { "path": "releases/networking-email-drafter/1.0.1.json", "sha256": "20db228543702c622bfe978b3f89f56973add3158ca284effe5390f7f3f25527" }, "artifact": { "path": "shards/2b2d134141e3d5698b9e174f154599abf13d7ddd3c9060e29ef536399fd82407.zip", "sha256": "2b2d134141e3d5698b9e174f154599abf13d7ddd3c9060e29ef536399fd82407", "bytes": 17034738, "skill_path": "networking-email-drafter" }, "content_sha256": "f7348f971859ab6892432fb3ed2acfa610dc58b3fff96d383e10f64c1d36fe95" }, { "id": "neurokit", "version": "1.0.0", "display_name": "neurokit", "summary": "Comprehensive biosignal processing for ECG/PPG/EEG/EDA/RSP/EMG/EOG; use when you need to clean, segment, and extract physiological features for HRV, event-related responses, complexity metrics, or multimodal psychophysiology pipelines.", "category": "Data Analysis", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Data Analysis/neurokit2" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "release": { "path": "releases/neurokit/1.0.0.json", "sha256": "d49c53cfa5f9d4d1a0f3732e931b498b73fb05495cdeeca1bf2db014e702addc" }, "artifact": { "path": "shards/363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb.zip", "sha256": "363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb", "bytes": 1471244, "skill_path": "neurokit" }, "content_sha256": "ca15dfb7f48c49519d3683fe0616ec8c696f38267338fdfd44e3726c4821f98a" }, { "id": "neuropixels-analysis", "version": "1.0.0", "display_name": "neuropixels-analysis", "summary": "End-to-end Neuropixels extracellular electrophysiology analysis (SpikeGLX/Open Ephys/NWB) including preprocessing, motion correction, Kilosort4 spike sorting, QC metrics, and Allen/IBL-style curation; use when processing Neuropixels recordings or when users mention Neuropixels, SpikeGLX, Open Ephys, Kilosort, quality metrics, drift/motion correction, or unit curation.", "category": "Data Analysis", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Data Analysis/neuropixels-analysis" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 86, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Data%20Analysis/neuropixels-analysis/neuropixels-analysis_audit_result_v1.json", "evaluated_on": "2026-03-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 86, "max_score": 100 }, "dynamic_score": { "score": 86.6, "max_score": 100 } }, "release": { "path": "releases/neuropixels-analysis/1.0.0.json", "sha256": "e7a69847023365cc3244bd42dd3a6c0bfb96118102d9fca0af5a827627af96a1" }, "artifact": { "path": "shards/363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb.zip", "sha256": "363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb", "bytes": 1471244, "skill_path": "neuropixels-analysis" }, "content_sha256": "99857e39ca01143ead2a1d90992eed83e3b71550fb6ef2aac674d850b3382141" }, { "id": "nhanes-clinical-retrospective-biomarker-research-planner", "version": "1.0.0", "display_name": "nhanes-clinical-retrospective-biomarker-research-planner", "summary": "Generates complete NHANES-style cross-sectional epidemiology + retrospective clinical validation research designs from a user-provided disease and biomarker direction. Always use this skill whenever a user wants to design, plan, or build a population-level biomarker association study using NHANES or similar survey datasets, especially when the article logic includes disease definition, biomarker formula derivation, multivariable logistic regression, restricted cubic spline analysis, subgroup stability testing, and a secondary hospital-based retrospective validation cohort. Covers five study patterns (cross-sectional association, dose-response / RCS, subgroup-stability, NHANES + retrospective validation, preliminary screening-performance) and always outputs four workload configs (Lite / Standard / Advanced / Publication+) with recommended primary plan, step-by-step workflow, figure plan, validation strategy, minimal executable version, publication upgrade path...", "category": "Protocol Design", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "awesome-med-research-skills/Protocol Design/nhanes-clinical-retrospective-biomarker-research-planner" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 89, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/awesome-med-research-skills/Protocol%20Design/nhanes-clinical-retrospective-biomarker-research-planner/eval_report_nhanes-clinical-retrospective-biomarker-research-planner_result.json", "evaluated_on": "2026-04-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 92, "max_score": 100 }, "dynamic_score": { "score": 87.7, "max_score": 100 } }, "release": { "path": "releases/nhanes-clinical-retrospective-biomarker-research-planner/1.0.0.json", "sha256": "e77ce5228281fa101025159ec1cd300f569d83f7bcdcfadb0e5689d3532bda98" }, "artifact": { "path": "shards/630d1b4dab37083d632d924264ae93642b8327521aaa70fbf83de3cc7fd53d61.zip", "sha256": "630d1b4dab37083d632d924264ae93642b8327521aaa70fbf83de3cc7fd53d61", "bytes": 542522, "skill_path": "nhanes-clinical-retrospective-biomarker-research-planner" }, "content_sha256": "605016bc2046bef4bdc0ca2ff7066ff322a0f6b7329a283f43c45f8dfdb9f6a3" }, { "id": "nih-biosketch-builder", "version": "1.0.1", "display_name": "nih-biosketch-builder", "summary": "Generate NIH Biosketch documents compliant with the 2022 OMB-approved.", "category": "Academic Writing", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Academic Writing/nih-biosketch-builder" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 85, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Academic%20Writing/nih-biosketch-builder/nih-biosketch-builder_audit_result_v2.json", "evaluated_on": "2026-03-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 88, "max_score": 100 }, "dynamic_score": { "score": 83.6, "max_score": 100 } }, "release": { "path": "releases/nih-biosketch-builder/1.0.1.json", "sha256": "d65693d9acf82095624b4eef53f8437e4476a2225b0141d2658648e38af092f2" }, "artifact": { "path": "shards/2b2d134141e3d5698b9e174f154599abf13d7ddd3c9060e29ef536399fd82407.zip", "sha256": "2b2d134141e3d5698b9e174f154599abf13d7ddd3c9060e29ef536399fd82407", "bytes": 17034738, "skill_path": "nih-biosketch-builder" }, "content_sha256": "bd9b1b60554b5f93653184710f5595bd438b02b28b10c89d0b1323ac4bc18bb3" }, { "id": "nomogram-construction", "version": "1.0.1", "display_name": "nomogram-construction", "summary": "Use when constructing a prognosis nomogram from survival-related clinical predictors, exporting the nomogram bundle and C-index table, and optionally rendering the final nomogram PDF. NOT for: univariate/multivariable Cox feature screening, calibration curves, ROC analysis, decision-curve analysis, or non-survival outcomes.", "category": "Data Analysis", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "awesome-med-research-skills/Data Analysis/nomogram-construction" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "license": "Unknown", "evaluation": { "kind": "upstream-self-assessment", "score": 96, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/awesome-med-research-skills/Data%20Analysis/nomogram-construction/eval_report_nomogram-construction_result.json", "evaluated_on": "2026-04-27", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 98, "max_score": 100 }, "dynamic_score": { "score": 94.8, "max_score": 100 } }, "release": { "path": "releases/nomogram-construction/1.0.1.json", "sha256": "816814a279283fc1a96a4b8cebcbd27d8f6b65b3c596d9cddf82d8fad257ce5f" }, "artifact": { "path": "shards/2b2d134141e3d5698b9e174f154599abf13d7ddd3c9060e29ef536399fd82407.zip", "sha256": "2b2d134141e3d5698b9e174f154599abf13d7ddd3c9060e29ef536399fd82407", "bytes": 17034738, "skill_path": "nomogram-construction" }, "content_sha256": "3a5537d5b87b573386018a9e8a700d99ca59d9941acebd00a76fa2108cac1660" }, { "id": "non-tumor-mechanism-guided-diagnostic-ml-research-planner", "version": "1.0.0", "display_name": "non-tumor-mechanism-guided-diagnostic-ml-research-planner", "summary": "Generates complete conventional non-oncology diagnostic machine-learning research designs from a user-provided disease context, optional mechanism theme, and validation direction. Use when a study centers on disease-vs-control transcriptome comparison, optional mechanism-gene restriction, feature shrinkage, diagnostic model construction, ROC / calibration / DCA evaluation, interpretation layers, and orthogonal validation. Covers five study patterns and always outputs Lite / Standard / Advanced / Publication+ with a recommended primary plan, stepwise workflow, figure plan, validation hierarchy, minimal executable version, publication upgrade path, and strictly verified literature retrieval.", "category": "Protocol Design", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "awesome-med-research-skills/Protocol Design/non-tumor-mechanism-guided-diagnostic-ml-research-planner" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 89, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/awesome-med-research-skills/Protocol%20Design/non-tumor-mechanism-guided-diagnostic-ml-research-planner/eval_report_non-tumor-mechanism-guided-diagnostic-ml-research-planner_result.json", "evaluated_on": "2026-04-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 91, "max_score": 100 }, "dynamic_score": { "score": 87.7, "max_score": 100 } }, "release": { "path": "releases/non-tumor-mechanism-guided-diagnostic-ml-research-planner/1.0.0.json", "sha256": "e6af48f85961a0f8d3401893559dc63c3d693bfbc1e760e09912292e28623bcc" }, "artifact": { "path": "shards/630d1b4dab37083d632d924264ae93642b8327521aaa70fbf83de3cc7fd53d61.zip", "sha256": "630d1b4dab37083d632d924264ae93642b8327521aaa70fbf83de3cc7fd53d61", "bytes": 542522, "skill_path": "non-tumor-mechanism-guided-diagnostic-ml-research-planner" }, "content_sha256": "630e2a75ef8723e499999c9890b7bfcd81a0cdfcb6e5706d8d3665d5d3955060" }, { "id": "non-tumor-ml-research-planner", "version": "1.0.0", "display_name": "non-tumor-ml-research-planner", "summary": "Generates complete non-tumor biomedical machine learning research designs from a user-provided research direction. Always use this skill when users want to plan bioinformatics + ML papers for non-cancer diseases (metabolic, cardiovascular, kidney, inflammatory, autoimmune, infectious, neurological, endocrine, wound healing, chronic multifactor), design diagnostic biomarker studies, combine GEO datasets with feature selection and ML modeling, or generate Lite/Standard/Advanced/Publication+ workload plans. Trigger for:\"non-tumor ML study\", \"bioinformatics paper outside oncology\", \"key genes and diagnostic model for a disease\", \"pyroptosis/ferroptosis/senescence/autophagy + disease\", \"GEO datasets + machine learning\", \"RF + LASSO diagnostic model\", \"DEG + feature selection + validation\", \"immune infiltration + biomarker\", \"non-cancer biomarker paper\". Trigger even for casual phrasings like \"I want to study X using machine learning\", \"help me design a non-tumor bioinformatics paper\", or \"how do I build a diagnostic model for disease Y\".", "category": "Protocol Design", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Protocol Design/non-tumor-ml-research-planner" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "release": { "path": "releases/non-tumor-ml-research-planner/1.0.0.json", "sha256": "d57f7d6b9bb757fe63c2e11d9f902cfe0a73c46c9a53164142ba9444b854352f" }, "artifact": { "path": "shards/630d1b4dab37083d632d924264ae93642b8327521aaa70fbf83de3cc7fd53d61.zip", "sha256": "630d1b4dab37083d632d924264ae93642b8327521aaa70fbf83de3cc7fd53d61", "bytes": 542522, "skill_path": "non-tumor-ml-research-planner" }, "content_sha256": "c96c507b9bd41dcde0ee5a23caacb9e430efc99839db2b1543b9e805deef9ce6" }, { "id": "novelty-vs-feasibility-assessor", "version": "1.0.0", "display_name": "novelty-vs-feasibility-assessor", "summary": "Assesses whether a medical research topic is worth starting now by separating true novelty from pseudo-novelty, auditing real feasibility under stated resource constraints, and forcing a concrete start / narrow / redesign / stop decision. Always require explicit assumptions and never fabricate references, datasets, resource availability, precedent studies, or publication claims.", "category": "Evidence Insight", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "awesome-med-research-skills/Evidence Insight/novelty-vs-feasibility-assessor" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 87, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/awesome-med-research-skills/Evidence%20Insight/novelty-vs-feasibility-assessor/eval_report_novelty-vs-feasibility-assessor_result.json", "evaluated_on": "2026-04-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 90, "max_score": 100 }, "dynamic_score": { "score": 84.7, "max_score": 100 } }, "release": { "path": "releases/novelty-vs-feasibility-assessor/1.0.0.json", "sha256": "368302ca21b52b7068eb27d7bdb4ba181f74665bf95d962cafadfb0e99838747" }, "artifact": { "path": "shards/363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb.zip", "sha256": "363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb", "bytes": 1471244, "skill_path": "novelty-vs-feasibility-assessor" }, "content_sha256": "d299a0784cb96888314af4cb5232c9642555ac4bd64d4a16ccb315a2027053fe" }, { "id": "nsfc-grant-writer", "version": "1.0.1", "display_name": "nsfc-grant-writer", "summary": "Generate a complete NSFC (National Natural Science Foundation of China) General Program grant application in Chinese, following the 2026 official template. Use when the user provides a scientific hypothesis and wants to generate a full grant application document, including abs...", "category": "Academic Writing", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Academic Writing/nsfc-grant-writer" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 90, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Academic%20Writing/nsfc-grant-writer/eval_report_nsfc-grant-writer_result.json", "evaluated_on": "2026-06-15", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 95, "max_score": 100 }, "dynamic_score": { "score": 86.2, "max_score": 100 } }, "release": { "path": "releases/nsfc-grant-writer/1.0.1.json", "sha256": "7011e8cede6f715fda43ce1116a9e6d4aa5caa6a1b2df5fb8c8cb526102c96a7" }, "artifact": { "path": "shards/2b2d134141e3d5698b9e174f154599abf13d7ddd3c9060e29ef536399fd82407.zip", "sha256": "2b2d134141e3d5698b9e174f154599abf13d7ddd3c9060e29ef536399fd82407", "bytes": 17034738, "skill_path": "nsfc-grant-writer" }, "content_sha256": "35754b54413331ba3c6ba3c8257ef05f69f2e1db5fb60b6c0f97c80425061560" }, { "id": "nvmolkit-usage", "version": "1.0.0", "display_name": "nvmolkit-usage", "summary": "Write code that calls the installed nvMolKit Python API for GPU-accelerated, batched RDKit-style operations - Morgan fingerprints, Tanimoto/cosine similarity, ETKDG conformer embedding, MMFF/UFF optimization, TFD, conformer RMSD, Butina clustering, and substructure search. Use when the user is importing `nvmolkit.*`, debugging an `nvmolkit` call, choosing between nvMolKit and RDKit for a batched cheminformatics workflow, or wiring nvMolKit results into a torch/numpy pipeline. Out of scope: building nvMolKit from source.", "category": "Data Analysis", "source": { "repository": "https://github.com/NVIDIA-BioNeMo/bionemo-agent-toolkit", "commit": "0e67a612e4045f007e38fa77adc8f3ebfc5616b6", "path": "skills/bionemo-agent-toolkit/skills/nvmolkit-usage" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "license": "Apache-2.0", "release": { "path": "releases/nvmolkit-usage/1.0.0.json", "sha256": "d8ac76228fc592252d909c268ce070b8358ac110acd80be533ea6c09c7303fe8" }, "artifact": { "path": "shards/d9eadedf27584c96257c396b3554a2f479383e134ebaf455716ea80dd4f8de94.zip", "sha256": "d9eadedf27584c96257c396b3554a2f479383e134ebaf455716ea80dd4f8de94", "bytes": 1161536, "skill_path": "nvmolkit-usage" }, "content_sha256": "041ca7467a35cb750bceffe30cc83b9977cd2fb492abefb10ec7832fe03fde64" }, { "id": "open-targets-db", "version": "1.0.0", "display_name": "open-targets-db", "summary": "Query the Open Targets Platform to retrieve targets, diseases, or evidence records when you need target-disease association data and evidence-based scores for therapeutic discovery.", "category": "Evidence Insight", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Evidence Insight/open-targets-db" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 87, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Evidence%20Insight/open-targets-db/open-targets-db_audit_result_v2.json", "evaluated_on": "2026-03-23", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 88, "max_score": 100 }, "dynamic_score": { "score": 86.4, "max_score": 100 } }, "release": { "path": "releases/open-targets-db/1.0.0.json", "sha256": "3a269113ffa32a0cca760e6c8c45893d6ef19a86f9c2712997944f002bdabd65" }, "artifact": { "path": "shards/363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb.zip", "sha256": "363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb", "bytes": 1471244, "skill_path": "open-targets-db" }, "content_sha256": "565ba051c62f0174f7faf7a8603be298d2ee488bed6bfd579639a8b137aa3e96" }, { "id": "openalex-db", "version": "1.0.0", "display_name": "openalex-db", "summary": "Access the OpenAlex database (240M+ scholarly works) for bibliometric analysis, literature search, and citation tracking; use when you need to query works/authors/institutions/concepts without an API key.", "category": "Evidence Insight", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Evidence Insight/openalex-db" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 90, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Evidence%20Insight/openalex-db/openalex-db_audit_result_v1.json", "evaluated_on": "2026-03-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 85, "max_score": 100 }, "dynamic_score": { "score": 92.6, "max_score": 100 } }, "release": { "path": "releases/openalex-db/1.0.0.json", "sha256": "1fcb3a511ef5d5734ff74aad02eda0e9ffa4ff9a93c2cc752d7e9815a0fb5ae4" }, "artifact": { "path": "shards/363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb.zip", "sha256": "363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb", "bytes": 1471244, "skill_path": "openalex-db" }, "content_sha256": "e67695dae63cc0a6d3f2004f6c34dd798779ad1b7a494a1a389abc815820de83" }, { "id": "openfold2-nim", "version": "1.0.0", "display_name": "openfold2-nim", "summary": "Use this skill for OpenFold2, NVIDIA's BioNeMo NIM microservice for monomer protein structure prediction. Invoke whenever the user mentions OpenFold2, AlphaFold2-like monomer folding, protein sequence-to-structure prediction, A3M MSAs, mmCIF templates, hosted NVIDIA API calls, or local Docker deployment.\n", "category": "Data Analysis", "source": { "repository": "https://github.com/NVIDIA-BioNeMo/bionemo-agent-toolkit", "commit": "0e67a612e4045f007e38fa77adc8f3ebfc5616b6", "path": "skills/bionemo-agent-toolkit/skills/openfold2-nim" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "license": "Apache-2.0 AND CC-BY-4.0", "release": { "path": "releases/openfold2-nim/1.0.0.json", "sha256": "e260acbf355271d084d1fc6e5c40b3941319043d5135366f0443ac920c4fae95" }, "artifact": { "path": "shards/d9eadedf27584c96257c396b3554a2f479383e134ebaf455716ea80dd4f8de94.zip", "sha256": "d9eadedf27584c96257c396b3554a2f479383e134ebaf455716ea80dd4f8de94", "bytes": 1161536, "skill_path": "openfold2-nim" }, "content_sha256": "99876ab8d38a9a6f139b2bb825b5c427201630ddf597ad75f39ee609d54f5c37" }, { "id": "openfold3-nim", "version": "1.0.0", "display_name": "openfold3-nim", "summary": "Use this skill for OpenFold3, NVIDIA's BioNeMo NIM microservice for biomolecular structure prediction. Invoke whenever the user mentions OpenFold3 or needs protein, protein-ligand, protein-DNA/RNA, or multi-chain complex prediction with the hosted NVIDIA API or local Docker NIM. Covers endpoint choice, auth, request payloads, output artifacts, confidence scores, and local container setup.\n", "category": "Data Analysis", "source": { "repository": "https://github.com/NVIDIA-BioNeMo/bionemo-agent-toolkit", "commit": "0e67a612e4045f007e38fa77adc8f3ebfc5616b6", "path": "skills/bionemo-agent-toolkit/skills/openfold3-nim" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "license": "Apache-2.0 AND CC-BY-4.0", "release": { "path": "releases/openfold3-nim/1.0.0.json", "sha256": "03b0418e38f22722660bf674f9299b5d93e6507ff259c3806849510bd4eb476e" }, "artifact": { "path": "shards/d9eadedf27584c96257c396b3554a2f479383e134ebaf455716ea80dd4f8de94.zip", "sha256": "d9eadedf27584c96257c396b3554a2f479383e134ebaf455716ea80dd4f8de94", "bytes": 1161536, "skill_path": "openfold3-nim" }, "content_sha256": "7157a8092ab40712a3a688b430f6cb22444689f52947a04c9b1ed9c67f31362d" }, { "id": "outcome-extraction-for-clinical-trials", "version": "1.0.0", "display_name": "outcome-extraction-for-clinical-trials", "summary": "Clinical research outcome extraction for meta-analysis. Use when users need to extract outcome measures (binary, continuous, or survival data) from clinical research papers for systematic review and meta-analysis. Handles both database lookup by PMID and real-time LLM extraction.", "category": "Data Analysis", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Data Analysis/outcome-extraction-for-clinical-trials" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 87, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Data%20Analysis/outcome-extraction-for-clinical-trials/outcome-extraction-for-clinical-trials_audit_result_v1.json", "evaluated_on": "2026-03-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 82, "max_score": 100 }, "dynamic_score": { "score": 90.6, "max_score": 100 } }, "release": { "path": "releases/outcome-extraction-for-clinical-trials/1.0.0.json", "sha256": "5b08b5b124cf7f98698628d5ea1bbe3e4d8210cefb7dd7c626557270fee07b94" }, "artifact": { "path": "shards/363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb.zip", "sha256": "363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb", "bytes": 1471244, "skill_path": "outcome-extraction-for-clinical-trials" }, "content_sha256": "01bf2cfc67649848057022b5a5e42391f6f41ee836133ac0ca177f2bd6fc49e2" }, { "id": "outlier-detection-handler", "version": "1.0.0", "display_name": "outlier-detection-handler", "summary": "Use outlier detection handler for data analysis workflows that need structured execution, explicit assumptions, and clear output boundaries.", "category": "Data Analysis", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Data Analysis/outlier-detection-handler" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 90, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Data%20Analysis/outlier-detection-handler/eval_report_outlier-detection-handler_result.json", "evaluated_on": "2026-06-15", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 95, "max_score": 100 }, "dynamic_score": { "score": 86.6, "max_score": 100 } }, "release": { "path": "releases/outlier-detection-handler/1.0.0.json", "sha256": "a5e3f6f5b421c153ded710e601efc0d7018692f31bedc0cc447ef9e52839ca7e" }, "artifact": { "path": "shards/363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb.zip", "sha256": "363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb", "bytes": 1471244, "skill_path": "outlier-detection-handler" }, "content_sha256": "e24bcc82644ba49ad59819bb832f44b182477e28a4a93f8c729ad3b109a2bc98" }, { "id": "paper-lookup", "version": "1.0.0", "display_name": "paper-lookup", "summary": "Search 10 academic paper databases via REST APIs for research papers, preprints, and scholarly articles. Covers PubMed, PMC (full text), bioRxiv, medRxiv, arXiv, OpenAlex, Crossref, Semantic Scholar, CORE, Unpaywall. Use when searching for papers, citations, DOI/PMID lookups, abstracts, full text, open access, preprints, citation graphs, author search, or any scholarly literature query. Triggers on mentions of any supported database or requests like \"find papers on X\" or \"look up this DOI\".", "category": "Evidence Insight", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Evidence Insight/paper-lookup" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "K-Dense Inc." } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 86, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Evidence%20Insight/paper-lookup/eval_report_paper-lookup_result.json", "evaluated_on": "2026-06-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 90, "max_score": 100 }, "dynamic_score": { "score": 82.6, "max_score": 100 } }, "release": { "path": "releases/paper-lookup/1.0.0.json", "sha256": "40e0eb1402948c07d4c44c9ce509f7ead74243487db5863f414b450b8909555d" }, "artifact": { "path": "shards/db1762fd180f4ec84677183a5f8bd51207e11a31387d2b9cc977beff02afdcf9.zip", "sha256": "db1762fd180f4ec84677183a5f8bd51207e11a31387d2b9cc977beff02afdcf9", "bytes": 29837, "skill_path": "paper-lookup" }, "content_sha256": "e00822806820c884e0260d0e1b49df5ae8507f77274a74956f5dc75625256abf" }, { "id": "paper-sprint-review", "version": "1.0.0", "display_name": "paper-sprint-review", "summary": "Scrum-inspired paper review, revision, and R&R workflow. Handles docx/tex/md/PDF in English or Chinese. Auto-detects manuscript stage, estimates sprint count, runs multi-lens review (Contribution/Rigor/Writing/Editor), generates prioritized revision backlog, exports MD/DOCX/PDF/HTML reports. Use when asked to review a paper, revise based on reviewer comments, handle R&R, respond to peer review, plan paper revision sprints, or when user types /ps or /papersprint.", "category": "Academic Writing", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "awesome-med-research-skills/Academic Writing/paper-sprint-review" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 95, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/awesome-med-research-skills/Academic%20Writing/paper-sprint-review/eval_report_paper-sprint-review_result.json", "evaluated_on": "2026-04-20", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 93, "max_score": 100 }, "dynamic_score": { "score": 96.3, "max_score": 100 } }, "release": { "path": "releases/paper-sprint-review/1.0.0.json", "sha256": "2574826fff58d9380f13982b8d5fd26db244009be19ce0b8f74419a7a969b858" }, "artifact": { "path": "shards/1d2612a702320846b10cda57f9f37ce17f5e19186095f2fe96bfa6e44eb077e8.zip", "sha256": "1d2612a702320846b10cda57f9f37ce17f5e19186095f2fe96bfa6e44eb077e8", "bytes": 234009, "skill_path": "paper-sprint-review" }, "content_sha256": "0ab3d746130b53f391f2f77e436916bc6d897179211d260afd35105a6b184b8e" }, { "id": "paper-to-claim-verifier", "version": "1.0.0", "display_name": "paper-to-claim-verifier", "summary": "Verifies whether a scientific or biomedical claim is actually supported by the cited original papers rather than by citation drift, overstatement, selective citation, or correlation-to-causation inflation. Use this skill whenever a user wants to check whether a repeated statement, slide claim, manuscript sentence, review assertion, or “people often say” scientific conclusion is truly supported by the underlying primary literature. Always separate the claim itself, the cited paper(s), what the paper actually showed, what it did not show, and whether later retellings drifted beyond the original evidence. Never fabricate references, findings, study features, or citation chains.", "category": "Evidence Insight", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "awesome-med-research-skills/Evidence Insight/paper-to-claim-verifier" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 89, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/awesome-med-research-skills/Evidence%20Insight/paper-to-claim-verifier/eval_report_paper-to-claim-verifier_result.json", "evaluated_on": "2026-04-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 93, "max_score": 100 }, "dynamic_score": { "score": 85.7, "max_score": 100 } }, "release": { "path": "releases/paper-to-claim-verifier/1.0.0.json", "sha256": "5a1adaea4d686bcaaae17b70a71005973b266f1646e94c150583662c83359040" }, "artifact": { "path": "shards/363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb.zip", "sha256": "363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb", "bytes": 1471244, "skill_path": "paper-to-claim-verifier" }, "content_sha256": "c3307d58b75c45df2a6a97f997aeb9d4368ecedb689a303953cd2839a69ba1a9" }, { "id": "parabricks", "version": "1.0.0", "display_name": "parabricks", "summary": "Route NVIDIA Parabricks pbrun tools, assess GPU/runtime readiness, and provide version-aware command guidance for FASTQ/BAM processing, RNA-seq, variant calling, BAM QC, and GVCF workflows. Do NOT use for inspecting or accelerating whole pipelines — use genomics-workflow-acceleration.", "category": "Data Analysis", "source": { "repository": "https://github.com/NVIDIA-BioNeMo/bionemo-agent-toolkit", "commit": "0e67a612e4045f007e38fa77adc8f3ebfc5616b6", "path": "skills/bionemo-agent-toolkit/skills/parabricks" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "license": "CC-BY-4.0 AND Apache-2.0", "release": { "path": "releases/parabricks/1.0.0.json", "sha256": "39246cf916bd9a2c5d3b75ad32c816823ecbe0616a0432a2f83c52517193e1f7" }, "artifact": { "path": "shards/d9eadedf27584c96257c396b3554a2f479383e134ebaf455716ea80dd4f8de94.zip", "sha256": "d9eadedf27584c96257c396b3554a2f479383e134ebaf455716ea80dd4f8de94", "bytes": 1161536, "skill_path": "parabricks" }, "content_sha256": "386f17c3845776fca8b6d5acc739df13cdf6bdff6489454605a74c78cce1d1dd" }, { "id": "patent-assistant", "version": "1.0.1", "display_name": "patent-assistant", "summary": "Assists R&D teams with patent technical disclosure drafting and patent/novelty search analysis; use when users ask to write a patent disclosure, structure an invention description, search related patents, or assess novelty.", "category": "Academic Writing", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Academic Writing/patent-assistant" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 88, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Academic%20Writing/patent-assistant/patent-assistant_audit_result_v1.json", "evaluated_on": "2026-03-23", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 82, "max_score": 100 }, "dynamic_score": { "score": 91.6, "max_score": 100 } }, "release": { "path": "releases/patent-assistant/1.0.1.json", "sha256": "be75cf7e110161fd38f548a72ac1bdece2b328b6e5519048f1fab90be3aa6571" }, "artifact": { "path": "shards/2b2d134141e3d5698b9e174f154599abf13d7ddd3c9060e29ef536399fd82407.zip", "sha256": "2b2d134141e3d5698b9e174f154599abf13d7ddd3c9060e29ef536399fd82407", "bytes": 17034738, "skill_path": "patent-assistant" }, "content_sha256": "760a454ac631601c61e452b942801a1688a2d5d7e83aa525ced8b1b7230a881d" }, { "id": "pathml", "version": "1.0.0", "display_name": "pathml", "summary": "Use PathML for local, research-only computational pathology workflows: load and tile slides, build preprocessing and QC pipelines, manage h5path data, quantify multiplex images, construct spatial graphs, and plan bounded model inference.", "category": "Data Analysis", "source": { "repository": "https://github.com/K-Dense-AI/scientific-agent-skills", "commit": "36d8f13a1e754618794bf42f417884940077b4ae", "path": "skills/pathml" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "K-Dense Inc." } ], "license": "MIT", "release": { "path": "releases/pathml/1.0.0.json", "sha256": "286c0ce84b3774604e24f0d031dd7fef2928ec81b1495115e02e32c68acaca0d" }, "artifact": { "path": "shards/b55fb597588b663d1d9dfe3695492a0b1a9b88845adbce48fe564656e55759c6.zip", "sha256": "b55fb597588b663d1d9dfe3695492a0b1a9b88845adbce48fe564656e55759c6", "bytes": 624089, "skill_path": "pathml" }, "content_sha256": "7700c6a12921f9eddc3f949574d33b2ee14d8e02bf0cc2caf87c828b7fdaf73c" }, { "id": "pathway-introduction-expert", "version": "1.0.0", "display_name": "pathway-introduction-expert", "summary": "Generates comprehensive academic introductions for biological pathways, including signaling processes, markers, and inhibitors. Use when the user asks to introduce a pathway, molecule, or gene.", "category": "Evidence Insight", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Evidence Insight/pathway-introduction-expert" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 91, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Evidence%20Insight/pathway-introduction-expert/pathway-introduction-expert_audit_result_v2.json", "evaluated_on": "2026-03-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 83, "max_score": 100 }, "dynamic_score": { "score": 96, "max_score": 100 } }, "release": { "path": "releases/pathway-introduction-expert/1.0.0.json", "sha256": "31fdfa5f02c6a514d0ba8ca120beb5651052ddca6cde45f3f84f39abe1d8e258" }, "artifact": { "path": "shards/363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb.zip", "sha256": "363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb", "bytes": 1471244, "skill_path": "pathway-introduction-expert" }, "content_sha256": "a0e026ef56b8ed9c6b74eb8141413cf239cb4b5c982b47afb792ad654a323bb9" }, { "id": "patient-recruitment-ad-gen", "version": "1.0.1", "display_name": "patient-recruitment-ad-gen", "summary": "Generate ethical, compliant, and patient-friendly recruitment advertisements for clinical trials.", "category": "Academic Writing", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Academic Writing/patient-recruitment-ad-gen" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 85, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Academic%20Writing/patient-recruitment-ad-gen/patient-recruitment-ad-gen_audit_result_v2.json", "evaluated_on": "2026-03-23", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 88, "max_score": 100 }, "dynamic_score": { "score": 83.6, "max_score": 100 } }, "release": { "path": "releases/patient-recruitment-ad-gen/1.0.1.json", "sha256": "134d08dab703b219848ceb361cea2770654980f878addc49126a1459d6840cd0" }, "artifact": { "path": "shards/2b2d134141e3d5698b9e174f154599abf13d7ddd3c9060e29ef536399fd82407.zip", "sha256": "2b2d134141e3d5698b9e174f154599abf13d7ddd3c9060e29ef536399fd82407", "bytes": 17034738, "skill_path": "patient-recruitment-ad-gen" }, "content_sha256": "4c14c4325ef1c542d3820505f4b0a5217d30e66a9e6b4962377f4064983eb36f" }, { "id": "pca-dimensionality-reduction", "version": "1.0.1", "display_name": "pca-dimensionality-reduction", "summary": "Use when performing PCA principal component dimensionality reduction on tabular numeric data. Supports command-line parameter input, automatic numeric feature selection, parameter validation, result directory creation, and CSV or TXT format result export.", "category": "Data Analysis", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "awesome-med-research-skills/Data Analysis/pca-dimensionality-reduction" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "Unknown", "evaluation": { "kind": "upstream-self-assessment", "score": 93, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/awesome-med-research-skills/Data%20Analysis/pca-dimensionality-reduction/eval_report_pca-dimensionality-reduction_result.json", "evaluated_on": "2026-04-15", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 92, "max_score": 100 }, "dynamic_score": { "score": 93.2, "max_score": 100 } }, "release": { "path": "releases/pca-dimensionality-reduction/1.0.1.json", "sha256": "8c9b3a3d3d2070d0be3f335e9b83bdaad98ce68c446c349336cb0426e50d293d" }, "artifact": { "path": "shards/2b2d134141e3d5698b9e174f154599abf13d7ddd3c9060e29ef536399fd82407.zip", "sha256": "2b2d134141e3d5698b9e174f154599abf13d7ddd3c9060e29ef536399fd82407", "bytes": 17034738, "skill_path": "pca-dimensionality-reduction" }, "content_sha256": "8255ddc19148102be8c6a091a3000d3539b2996929d09096380aa86b7a8a7314" }, { "id": "pcd-immune-oncology-research-planner", "version": "1.0.0", "display_name": "pcd-immune-oncology-research-planner", "summary": "Generates complete programmed-cell-death (PCD) / regulated-cell-death (RCD) bulk-transcriptome oncology research designs from a user-provided disease and mechanism theme. Always use this skill whenever a user wants to design, plan, or structure a cancer bioinformatics study built around cell-death patterns, tumor microenvironment, prognostic modeling, immune landscape analysis, mutation profiling, and computational drug sensitivity. Covers five study patterns (mechanism-gene-set, subtype-discovery, prognostic-signature, immune-response stratification, translational drug-hypothesis) and always outputs four workload configs (Lite / Standard / Advanced / Publication+) with recommended primary plan, step-by-step workflow, figure plan, validation strategy, minimal executable version, publication upgrade path, and a strictly verified reference literature retrieval layer with real references only.", "category": "Protocol Design", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "awesome-med-research-skills/Protocol Design/pcd-immune-oncology-research-planner" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 89, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/awesome-med-research-skills/Protocol%20Design/pcd-immune-oncology-research-planner/eval_report_pcd-immune-oncology-research-planner_result.json", "evaluated_on": "2026-04-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 92, "max_score": 100 }, "dynamic_score": { "score": 87.7, "max_score": 100 } }, "release": { "path": "releases/pcd-immune-oncology-research-planner/1.0.0.json", "sha256": "8e4ec8855aec6fc550811ac926c46527d21a6c843cb28b12eb68030c5034eadc" }, "artifact": { "path": "shards/630d1b4dab37083d632d924264ae93642b8327521aaa70fbf83de3cc7fd53d61.zip", "sha256": "630d1b4dab37083d632d924264ae93642b8327521aaa70fbf83de3cc7fd53d61", "bytes": 542522, "skill_path": "pcd-immune-oncology-research-planner" }, "content_sha256": "dd1e5002684decae40aede11010ad0b8f829000d0f0ccdf2d8b38b6f70443935" }, { "id": "pdb-database", "version": "1.0.0", "display_name": "pdb-database", "summary": "Access the RCSB Protein Data Bank (PDB) to search, download, and programmatically retrieve 3D macromolecular structures and metadata; use when you need structure discovery (text/sequence/3D similarity) or automated structural data ingestion for structural biology and drug discovery workflows.", "category": "Evidence Insight", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Evidence Insight/pdb-database" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 86, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Evidence%20Insight/pdb-database/pdb-database_audit_result_v1.json", "evaluated_on": "2026-03-23", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 84, "max_score": 100 }, "dynamic_score": { "score": 87.6, "max_score": 100 } }, "release": { "path": "releases/pdb-database/1.0.0.json", "sha256": "1bca8a0980e46f7c2eecb497fd58776da631ae49a4afd1fa3135a7a599158577" }, "artifact": { "path": "shards/363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb.zip", "sha256": "363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb", "bytes": 1471244, "skill_path": "pdb-database" }, "content_sha256": "7a26fc93f054d655056f9cac21e2d112aa16d1defb0309a3ea766e33497633dc" }, { "id": "pdf-extract", "version": "1.0.0", "display_name": "pdf-extract", "summary": "Extract PDF selectable text and full-page or segmented page images (including tables) into Markdown with per-page headings and image links; use when you need both readable text and page visuals for PPT creation, review, or analysis.", "category": "Other", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Other/pdf-extract" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 90, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Other/pdf-extract/pdf-extract_audit_result_v2.json", "evaluated_on": "2026-03-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 83, "max_score": 100 }, "dynamic_score": { "score": 94.6, "max_score": 100 } }, "release": { "path": "releases/pdf-extract/1.0.0.json", "sha256": "a08a10b0df5af070f486a68edee4092f0f24f87bc8fab484925f955ff46bdaaa" }, "artifact": { "path": "shards/1ea735cead62d521282de0bf8a672a95515d73332ea4fa7d5d2ccf72eed45995.zip", "sha256": "1ea735cead62d521282de0bf8a672a95515d73332ea4fa7d5d2ccf72eed45995", "bytes": 342914, "skill_path": "pdf-extract" }, "content_sha256": "8c4d86c1585560f0d9d03e6291d012a28512a1aa5712b2e2c4dcc0fa79c9643f" }, { "id": "pdf-extract-experimental-materials", "version": "1.0.0", "display_name": "pdf-extract-experimental-materials", "summary": "Extract experimental materials and instrument information from PDFs (or PDF-derived text/Markdown) into three CSV tables; use when a paper/report contains sections like Materials and Methods, Key Resources Table, Reagents, Antibodies, Consumables, Software, Equipment, Instruments, or Reagent Preparation.", "category": "Evidence Insight", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Evidence Insight/pdf-extract-experimental-materials" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 91, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Evidence%20Insight/pdf-extract-experimental-materials/pdf-extract-experimental-materials_audit_result_v2.json", "evaluated_on": "2026-03-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 83, "max_score": 100 }, "dynamic_score": { "score": 96, "max_score": 100 } }, "release": { "path": "releases/pdf-extract-experimental-materials/1.0.0.json", "sha256": "4b885d9de1b4893fa0cf624b6a581664ba6230e9fdd3803522ca5a56902edbf0" }, "artifact": { "path": "shards/363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb.zip", "sha256": "363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb", "bytes": 1471244, "skill_path": "pdf-extract-experimental-materials" }, "content_sha256": "66dc62f8c29a30c94c02893ea4e5a4bc230e53ba83b02485631b518c089fee8a" }, { "id": "pdf-ppt", "version": "1.0.0", "display_name": "pdf-ppt", "summary": "Create literature-report PPTX decks from PDF papers. Use when you must extract a paper’s metadata, summarize the study, interpret Results/Figures/Tables, and generate slides with 1:1 figure-to-text alignment and layout rules (triggered by requests like “PDF to PPT”, “literature report slides”, or “turn this paper into a presentation”).", "category": "Other", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Other/pdf-ppt" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 85, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Other/pdf-ppt/pdf-ppt_audit_result_v1.json", "evaluated_on": "2026-03-23", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 84, "max_score": 100 }, "dynamic_score": { "score": 85.6, "max_score": 100 } }, "release": { "path": "releases/pdf-ppt/1.0.0.json", "sha256": "f0894a33b2d903c923aaaafc0667f3509a2dba200a844e3ffb92e0b6c7b6c655" }, "artifact": { "path": "shards/fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0.zip", "sha256": "fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0", "bytes": 1383584, "skill_path": "pdf-ppt" }, "content_sha256": "0df25047799840c76aebabe6508b467f1026aa842f6b9dc121825769e9b85f4f" }, { "id": "peer-review", "version": "1.0.1", "display_name": "peer-review", "summary": "Conduct professional peer reviews for papers or theses, providing structured evaluations and improvement suggestions; use when you need a pre-submission assessment, an internal review, or academic quality control.", "category": "Academic Writing", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Academic Writing/peer-review" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 86, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Academic%20Writing/peer-review/peer-review_audit_result_v1.json", "evaluated_on": "2026-03-23", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 84, "max_score": 100 }, "dynamic_score": { "score": 87.6, "max_score": 100 } }, "release": { "path": "releases/peer-review/1.0.1.json", "sha256": "e8f19b39d12f234951c6b3281ad0ecb6ec52a38407b359d0642c09a92f93b71f" }, "artifact": { "path": "shards/2b2d134141e3d5698b9e174f154599abf13d7ddd3c9060e29ef536399fd82407.zip", "sha256": "2b2d134141e3d5698b9e174f154599abf13d7ddd3c9060e29ef536399fd82407", "bytes": 17034738, "skill_path": "peer-review" }, "content_sha256": "a43b5c41897708dec1c207d10012ddb73ce014db54cb49f62c4eeffe024a1ff3" }, { "id": "peer-review-response-drafter", "version": "1.0.1", "display_name": "peer-review-response-drafter", "summary": "Assist in drafting professional peer review response letters. Trigger.", "category": "Academic Writing", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Academic Writing/peer-review-response-drafter" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 85, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Academic%20Writing/peer-review-response-drafter/peer-review-response-drafter_audit_result_v1.json", "evaluated_on": "2026-03-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 88, "max_score": 100 }, "dynamic_score": { "score": 83.6, "max_score": 100 } }, "release": { "path": "releases/peer-review-response-drafter/1.0.1.json", "sha256": "eb94a9f1ce5b1fa740dcc41d66cdfe28ff9819415f9aa53dfd089946e3b6b089" }, "artifact": { "path": "shards/2b2d134141e3d5698b9e174f154599abf13d7ddd3c9060e29ef536399fd82407.zip", "sha256": "2b2d134141e3d5698b9e174f154599abf13d7ddd3c9060e29ef536399fd82407", "bytes": 17034738, "skill_path": "peer-review-response-drafter" }, "content_sha256": "2b81452d81a25b7044b252a4f4ae2236ef438ac280f1a9f1f8029d8367ffbf8a" }, { "id": "phenotype-introduction", "version": "1.0.0", "display_name": "phenotype-introduction", "summary": "Expert system for generating comprehensive biomedical phenotype introductions with structured academic content. Use when users request detailed explanations of cellular phenotypes including concept, mechanism, regulation, and detection methods in Chinese academic writing.", "category": "Evidence Insight", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Evidence Insight/phenotype-introduction" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 89, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Evidence%20Insight/phenotype-introduction/phenotype-introduction_audit_result_v1.json", "evaluated_on": "2026-03-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 83, "max_score": 100 }, "dynamic_score": { "score": 92.6, "max_score": 100 } }, "release": { "path": "releases/phenotype-introduction/1.0.0.json", "sha256": "e068ea80f669f18a1ab5c020c0d56400c0e54d10af5df02eaacc01facd74d138" }, "artifact": { "path": "shards/363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb.zip", "sha256": "363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb", "bytes": 1471244, "skill_path": "phenotype-introduction" }, "content_sha256": "399e2682f80e2ba282036139ca027bfbf51bf98e03192b8b49f9cf61a98193c3" }, { "id": "phi-prompt-guard", "version": "1.0.0", "display_name": "phi-prompt-guard", "summary": "Runtime, prompt-time behavioral guardrail that helps reduce PHI exposure in LLM-assisted workflows by detecting PHI-bearing prompts, avoiding unsafe tool actions that would pull more PHI in, and redirecting users toward de-identified or synthetic inputs. Use when the user is about to paste, query, or read clinical/patient data, or when an action (DB query, file read, tool output) may pull PHI into the conversation. Honors a [PHI-OK] attestation for synthetic / test data.", "category": "Other", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Other/phi-prompt-guard" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 96, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Other/phi-prompt-guard/eval_report_phi-prompt-guard_result.json", "evaluated_on": "2026-05-14", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 98, "max_score": 100 }, "dynamic_score": { "score": 95.4, "max_score": 100 } }, "release": { "path": "releases/phi-prompt-guard/1.0.0.json", "sha256": "eee69f034fef2833d488e99f863e272d4d6ecfa8d04ab450bcec9a22d0be22e1" }, "artifact": { "path": "shards/1ea735cead62d521282de0bf8a672a95515d73332ea4fa7d5d2ccf72eed45995.zip", "sha256": "1ea735cead62d521282de0bf8a672a95515d73332ea4fa7d5d2ccf72eed45995", "bytes": 342914, "skill_path": "phi-prompt-guard" }, "content_sha256": "8105032059883455c035930e3638fbe6e428b2a006a3675db44d18a2e425d1a1" }, { "id": "phylogenetic-tree-styler", "version": "1.0.0", "display_name": "phylogenetic-tree-styler", "summary": "Analyze data with `phylogenetic-tree-styler` using a reproducible workflow, explicit validation, and structured outputs for review-ready interpretation.", "category": "Data Analysis", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Data Analysis/phylogenetic-tree-styler" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 89, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Data%20Analysis/phylogenetic-tree-styler/phylogenetic-tree-styler_audit_result_v2.json", "evaluated_on": "2026-03-23", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 88, "max_score": 100 }, "dynamic_score": { "score": 89.6, "max_score": 100 } }, "release": { "path": "releases/phylogenetic-tree-styler/1.0.0.json", "sha256": "20f388b6a09c17c4ad9e2e860218c81d3bb70af15501117ac31d817a20c210e8" }, "artifact": { "path": "shards/1ea735cead62d521282de0bf8a672a95515d73332ea4fa7d5d2ccf72eed45995.zip", "sha256": "1ea735cead62d521282de0bf8a672a95515d73332ea4fa7d5d2ccf72eed45995", "bytes": 342914, "skill_path": "phylogenetic-tree-styler" }, "content_sha256": "e0e1972f6f1902770bf51278994511075b51847fe00f876c73d0002c4dacdc4e" }, { "id": "plotly", "version": "1.0.0", "display_name": "plotly", "summary": "Interactive visualization library for Python. Use it when you need hover tooltips, zoom/pan, selection, animations, or charts embeddable in web pages (e.g., dashboards, exploratory analysis, presentations).", "category": "Other", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Other/plotly" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 86, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Other/plotly/plotly_audit_result_v2.json", "evaluated_on": "2026-03-23", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 85, "max_score": 100 }, "dynamic_score": { "score": 86.2, "max_score": 100 } }, "release": { "path": "releases/plotly/1.0.0.json", "sha256": "2ba072ca9f647f7cc67f437e7f6c845971d9088d1172506e6b94d33b20ef9b0f" }, "artifact": { "path": "shards/fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0.zip", "sha256": "fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0", "bytes": 1383584, "skill_path": "plotly" }, "content_sha256": "fc755618b66e49824097f197bd71f021d78f48558ecd876bce089bc3e74ce153" }, { "id": "pmc-official-download", "version": "1.0.0", "display_name": "pmc-official-download", "summary": "Download PubMed/PMC literature using officially permitted PMC methods. Whenever the user wants to download PMC full text, batch-retrieve PMC Open Access Subset articles, save full text corresponding to PMID/PMCID locally, or needs to determine whether a PubMed article can be l...", "category": "Evidence Insight", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Evidence Insight/pmc-official-download" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 90, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Evidence%20Insight/pmc-official-download/eval_report_pmc-official-download_result.json", "evaluated_on": "2026-06-15", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 95, "max_score": 100 }, "dynamic_score": { "score": 86.6, "max_score": 100 } }, "release": { "path": "releases/pmc-official-download/1.0.0.json", "sha256": "ee550d1a09b55732378bb1d6f0a17228eccb618a9cea8cd525203761318b4106" }, "artifact": { "path": "shards/363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb.zip", "sha256": "363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb", "bytes": 1471244, "skill_path": "pmc-official-download" }, "content_sha256": "cc2e2021e771a244ce1f566318dfa9003a454ef6a36dad6bad56c92e14aa4710" }, { "id": "population-gap-detector", "version": "1.0.0", "display_name": "population-gap-detector", "summary": "Detects overlooked, underrepresented, weakly resolved, or poorly validated populations and subgroups within a biomedical research area so users can identify more precise and meaningful study populations. Always use this skill when the real question is not just what is under-studied, but which populations, strata, or subgroups are missing, thinly represented, superficially analyzed, pooled without resolution, or insufficiently validated in the current evidence base. Focus on meaningful subgroup gaps rather than generic calls for diversity.", "category": "Evidence Insight", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "awesome-med-research-skills/Evidence Insight/population-gap-detector" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 87, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/awesome-med-research-skills/Evidence%20Insight/population-gap-detector/eval_report_population-gap-detector_result.json", "evaluated_on": "2026-04-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 90, "max_score": 100 }, "dynamic_score": { "score": 85, "max_score": 100 } }, "release": { "path": 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"https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "Unknown", "evaluation": { "kind": "upstream-self-assessment", "score": 85, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Other/postdoc-fellowship-matcher/postdoc-fellowship-matcher_audit_result_v2.json", "evaluated_on": "2026-03-19", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 85, "max_score": 100 }, "dynamic_score": { "score": 85.7, "max_score": 100 } }, "release": { "path": "releases/postdoc-fellowship-matcher/1.0.1.json", "sha256": "b749ed2235cb9e9b35057b97e5dddc183d2523e60fc5081620ffa22872e8b960" }, "artifact": { "path": "shards/2b2d134141e3d5698b9e174f154599abf13d7ddd3c9060e29ef536399fd82407.zip", "sha256": "2b2d134141e3d5698b9e174f154599abf13d7ddd3c9060e29ef536399fd82407", "bytes": 17034738, "skill_path": "postdoc-fellowship-matcher" }, "content_sha256": "30ce5b4e12e7d18e29d43858df36d5a4f909210dc50e6c0866352a80da8e08f3" }, { "id": "poster-designer", "version": "1.0.0", "display_name": "poster-designer", "summary": "Generate professional poster design concepts and optimized image-generation prompts, then automatically run a drawing script to produce the final poster image when a user needs a poster.", "category": "Other", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Other/poster-designer" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 87, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Other/poster-designer/poster-designer_audit_result_v1.json", "evaluated_on": "2026-03-23", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 87, "max_score": 100 }, "dynamic_score": { "score": 86.8, "max_score": 100 } }, "release": { "path": "releases/poster-designer/1.0.0.json", "sha256": "2688e6679b7b9489e25e60b62bdad8a9bce559bfd6e3245c75bb4288a9fdc4d3" }, "artifact": { "path": "shards/fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0.zip", "sha256": "fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0", "bytes": 1383584, "skill_path": "poster-designer" }, "content_sha256": "b812d1b53dff080d88aa55ca09ae9a16a9f47a0e86fc7921f6a997a8b283ac46" }, { "id": "poster-layout-planner", "version": "1.0.1", "display_name": "poster-layout-planner", "summary": "Use poster layout planner for other workflows that need structured execution, explicit assumptions, and clear output boundaries.", "category": "Academic Writing", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Academic Writing/poster-layout-planner" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 85, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Academic%20Writing/poster-layout-planner/poster-layout-planner_audit_result_v2.json", "evaluated_on": "2026-03-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 88, "max_score": 100 }, "dynamic_score": { "score": 83.6, "max_score": 100 } }, "release": { "path": "releases/poster-layout-planner/1.0.1.json", "sha256": "b6c3dd511222297ba9ba29c29c9c8192d1d643aceea95ad91663c78dfe26c2e2" }, "artifact": { "path": "shards/2b2d134141e3d5698b9e174f154599abf13d7ddd3c9060e29ef536399fd82407.zip", "sha256": "2b2d134141e3d5698b9e174f154599abf13d7ddd3c9060e29ef536399fd82407", "bytes": 17034738, "skill_path": "poster-layout-planner" }, "content_sha256": "1fa07d66d09e74e04ad0fe805c03c9d169508e39150d28b7bb35d114584dd5ba" }, { "id": "poster-storyline-builder", "version": "1.0.1", "display_name": "poster-storyline-builder", "summary": "Reorganizes a paper into a storyline suitable for scientific posters. Use when planning the section structure, title hierarchy, figure selection, and live-explanation flow for an academic conference poster. Also triggers on \"help me design a poster layout\", \"what sections should my poster have\", \"how do I arrange my poster\", \"poster structure for [conference]\", or \"which figures should I use for my poster\".", "category": "Academic Writing", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "awesome-med-research-skills/Academic Writing/poster-storyline-builder" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 87, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/awesome-med-research-skills/Academic%20Writing/poster-storyline-builder/eval_report_poster-storyline-builder_result.json", "evaluated_on": "2026-04-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 87, "max_score": 100 }, "dynamic_score": { "score": 87.3, "max_score": 100 } }, "release": { "path": "releases/poster-storyline-builder/1.0.1.json", "sha256": "b3daa12367f00306b9a7ff9e6ab611ed7e6d27aa9b5377f1e17ae365b90d87e1" }, "artifact": { "path": "shards/2b2d134141e3d5698b9e174f154599abf13d7ddd3c9060e29ef536399fd82407.zip", "sha256": "2b2d134141e3d5698b9e174f154599abf13d7ddd3c9060e29ef536399fd82407", "bytes": 17034738, "skill_path": "poster-storyline-builder" }, "content_sha256": "a7dd253e2195bd7a60a586f4322f5bd297af6c39d6ea68083a94488cb195da97" }, { "id": "pptx-posters", "version": "1.0.0", "display_name": "pptx-posters", "summary": "Generate PowerPoint presentations and academic posters from paper abstracts or full paper content, with automatic layout optimization and citation formatting.", "category": "Other", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Other/pptx-posters" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 85, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Other/pptx-posters/pptx-posters_audit_result_v2.json", "evaluated_on": "2026-03-19", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 84, "max_score": 100 }, "dynamic_score": { "score": 86.3, "max_score": 100 } }, "release": { "path": "releases/pptx-posters/1.0.0.json", "sha256": "e7c2b37fa49eac1479ca01c4ec7faec8f18a8ec4f85eb4e654d0aec90ad554ce" }, "artifact": { "path": "shards/fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0.zip", "sha256": "fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0", "bytes": 1383584, "skill_path": "pptx-posters" }, "content_sha256": "e5bf5b620a31d17fb6e481a7ace33c874671679e0e20ba7aeb52f6cb55190355" }, { "id": "preclinical-pkpd-analyst", "version": "1.0.0", "display_name": "preclinical-pkpd-analyst", "summary": "Use preclinical pkpd analyst for data analysis workflows that need structured execution, explicit assumptions, and clear output boundaries.", "category": "Data Analysis", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Data Analysis/preclinical-pkpd-analyst" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 86, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Data%20Analysis/preclinical-pkpd-analyst/preclinical-pkpd-analyst_audit_result_v2.json", "evaluated_on": "2026-03-23", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 83, "max_score": 100 }, "dynamic_score": { "score": 87.2, "max_score": 100 } }, "release": { "path": "releases/preclinical-pkpd-analyst/1.0.0.json", "sha256": "d004978757087cfb3a05439236d449db56fb59ea0b935a563034e24d0a45c277" }, "artifact": { "path": "shards/363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb.zip", "sha256": "363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb", "bytes": 1471244, "skill_path": "preclinical-pkpd-analyst" }, "content_sha256": "3b6943cc4fca633765f00183466f2a9752586dae21f68c4e1e3b4429c1c21b54" }, { "id": "preprint-surveillance-finder", "version": "1.0.1", "display_name": "preprint-surveillance-finder", "summary": "Tracks the latest preprints and emerging research topics related to your topic across bioRxiv, medRxiv, and arXiv. Use when a user wants to discover what is being published right now before it reaches journals, monitor competitor directions, spot new methodology trends, or get an early-warning scan of a research area. Triggers on phrases like \"what's new in X\", \"latest preprints on Y\", \"emerging topics in Z\", \"monitor bioRxiv for\", or \"what are people working on in this field\".", "category": "Evidence Insight", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "awesome-med-research-skills/Evidence Insight/preprint-surveillance-finder" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "Unknown", "evaluation": { "kind": "upstream-self-assessment", "score": 86, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/awesome-med-research-skills/Evidence%20Insight/preprint-surveillance-finder/eval_report_preprint-surveillance-finder_result.json", "evaluated_on": "2026-04-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 89, "max_score": 100 }, "dynamic_score": { "score": 83.6, "max_score": 100 } }, "release": { "path": "releases/preprint-surveillance-finder/1.0.1.json", "sha256": "1e873c291fb2038c672ab83b9517b1d8d63bab3e7bfd545bc8bca6adf23707be" }, "artifact": { "path": "shards/2b2d134141e3d5698b9e174f154599abf13d7ddd3c9060e29ef536399fd82407.zip", "sha256": "2b2d134141e3d5698b9e174f154599abf13d7ddd3c9060e29ef536399fd82407", "bytes": 17034738, "skill_path": "preprint-surveillance-finder" }, "content_sha256": "02b6b711ac1aac4f3bbf288e3ff39926a2bf9825a4f5f92433c4744dfc4ff7d4" }, { "id": "presentation-hook", "version": "1.0.1", "display_name": "presentation-hook", "summary": "Creates engaging opening statements and powerful closings for medical.", "category": "Academic Writing", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Academic Writing/presentation-hook" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 85, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Academic%20Writing/presentation-hook/presentation-hook_audit_result_v2.json", "evaluated_on": "2026-03-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 88, "max_score": 100 }, "dynamic_score": { "score": 83.6, "max_score": 100 } }, "release": { "path": "releases/presentation-hook/1.0.1.json", "sha256": "d81c005c3a48bf155997adbf8e74b95fa79567d115f540208d406841f2fe3643" }, "artifact": { "path": "shards/2b2d134141e3d5698b9e174f154599abf13d7ddd3c9060e29ef536399fd82407.zip", "sha256": "2b2d134141e3d5698b9e174f154599abf13d7ddd3c9060e29ef536399fd82407", "bytes": 17034738, "skill_path": "presentation-hook" }, "content_sha256": "6f54dcba67132081145d4d1c29052d2928456434e7e07addc91bc3d9b34e6760" }, { "id": "primary-plan-recommender", "version": "1.0.0", "display_name": "primary-plan-recommender", "summary": "Compares multiple study-route options for the same biomedical research question and recommends one primary plan, while explicitly explaining why alternative routes are secondary, premature, weaker, or dependency-heavy. Always use this skill when the user already has a reasonably defined question but is unsure which main study route should anchor the project. Focus on plan comparison, route selection, dependency awareness, and primary-plan justification rather than full protocol drafting.", "category": "Protocol Design", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "awesome-med-research-skills/Protocol Design/primary-plan-recommender" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 89, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/awesome-med-research-skills/Protocol%20Design/primary-plan-recommender/eval_report_primary-plan-recommender_result.json", "evaluated_on": "2026-04-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 91, "max_score": 100 }, "dynamic_score": { "score": 88.4, "max_score": 100 } }, "release": { "path": "releases/primary-plan-recommender/1.0.0.json", "sha256": "84991462a0d220ecee7c5887df306b04f1b441620a2d02c92f94a1d1209bc20e" }, "artifact": { "path": "shards/630d1b4dab37083d632d924264ae93642b8327521aaa70fbf83de3cc7fd53d61.zip", "sha256": "630d1b4dab37083d632d924264ae93642b8327521aaa70fbf83de3cc7fd53d61", "bytes": 542522, "skill_path": "primary-plan-recommender" }, "content_sha256": "d8c4e54f8b427d22de1b8ac695cf4e56dc1f06ff696439bffe0e32678f29dda4" }, { "id": "prior-auth-letter-drafter", "version": "1.0.1", "display_name": "prior-auth-letter-drafter", "summary": "Generate professional prior authorization request letters for insurance companies with proper clinical justification and formatting.", "category": "Academic Writing", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Academic Writing/prior-auth-letter-drafter" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "Unknown", "evaluation": { "kind": "upstream-self-assessment", "score": 85, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Academic%20Writing/prior-auth-letter-drafter/prior-auth-letter-drafter_audit_result_v1.json", "evaluated_on": "2026-03-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 88, "max_score": 100 }, "dynamic_score": { "score": 83.6, "max_score": 100 } }, "release": { "path": "releases/prior-auth-letter-drafter/1.0.1.json", "sha256": "e49dcfe5f30f79f8ee75222fded7e844a54a16ee8d713b07797e3212223bd104" }, "artifact": { "path": "shards/2b2d134141e3d5698b9e174f154599abf13d7ddd3c9060e29ef536399fd82407.zip", "sha256": "2b2d134141e3d5698b9e174f154599abf13d7ddd3c9060e29ef536399fd82407", "bytes": 17034738, "skill_path": "prior-auth-letter-drafter" }, "content_sha256": "10db4b467dd65928353824f20ac4fdab6aa395c53e50da43c4563bd5e9aed573" }, { "id": "probast-quality-assessment-for-prediction-model-studies", "version": "1.0.1", "display_name": "probast-quality-assessment-for-prediction-model-studies", "summary": "Assess bias in medical prediction model studies using PROBAST tool. Use when user wants to evaluate the quality or risk of bias of a medical paper (text or PDF).", "category": "Data Analysis", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Data Analysis/probast-quality-assessment-for-prediction-model-studies" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "Unknown", "evaluation": { "kind": "upstream-self-assessment", "score": 85, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Data%20Analysis/probast-quality-assessment-for-prediction-model-studies/probast-quality-assessment-for-prediction-model-studies_audit_result_v1.json", "evaluated_on": "2026-03-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 77, "max_score": 100 }, "dynamic_score": { "score": 90.6, "max_score": 100 } }, "release": { "path": "releases/probast-quality-assessment-for-prediction-model-studies/1.0.1.json", "sha256": "5acdfef9962822a09d87cadf7a1382932fd9f5455bd4f730cb955dcdf2b9d0a2" }, "artifact": { "path": "shards/2b2d134141e3d5698b9e174f154599abf13d7ddd3c9060e29ef536399fd82407.zip", "sha256": "2b2d134141e3d5698b9e174f154599abf13d7ddd3c9060e29ef536399fd82407", "bytes": 17034738, "skill_path": "probast-quality-assessment-for-prediction-model-studies" }, "content_sha256": "9086e905ecb0523fab3caf4f115894f2fa27d8cd35da8cb2969f7365fd9510ce" }, { "id": "process-related-diagnostic-biomarker-nomogram-research-planner", "version": "1.0.0", "display_name": "process-related-diagnostic-biomarker-nomogram-research-planner", "summary": "Generates complete process-related diagnostic biomarker bioinformatics research designs from a user-provided disease context, gene-family or pathway theme, and validation direction. Use when a study centers on process-related genes, DEG and WGCNA integration, machine-learning feature selection, nomogram-based diagnostic modeling, immune infiltration, regulatory-network analysis, and optional external or experimental validation. Covers five study patterns (process-DEG discovery, co-expression-module integration, machine-learning biomarker selection, diagnostic model/nomogram workflow, immune-regulatory interpretation and validation) and always outputs Lite / Standard / Advanced / Publication+ with a recommended primary plan, stepwise workflow, figure plan, validation hierarchy, minimal executable version, publication upgrade path, and strictly verified literature retrieval.", "category": "Protocol Design", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "awesome-med-research-skills/Protocol Design/process-related-diagnostic-biomarker-nomogram-research-planner" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 89, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/awesome-med-research-skills/Protocol%20Design/process-related-diagnostic-biomarker-nomogram-research-planner/eval_report_process-related-diagnostic-biomarker-nomogram-research-planner_result.json", "evaluated_on": "2026-04-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 91, "max_score": 100 }, "dynamic_score": { "score": 87.7, "max_score": 100 } }, "release": { "path": "releases/process-related-diagnostic-biomarker-nomogram-research-planner/1.0.0.json", "sha256": "ca610251eb194832522b86cb74e2b3afdea2357713c5b87304d3548c16cae67d" }, "artifact": { "path": "shards/630d1b4dab37083d632d924264ae93642b8327521aaa70fbf83de3cc7fd53d61.zip", "sha256": "630d1b4dab37083d632d924264ae93642b8327521aaa70fbf83de3cc7fd53d61", "bytes": 542522, "skill_path": "process-related-diagnostic-biomarker-nomogram-research-planner" }, "content_sha256": "05bf8b92a9055aee33a703266134f02a72c72d3490a17620302e0066aadff1b1" }, { "id": "prognostic-biomarker-protocol-designer", "version": "1.0.0", "display_name": "prognostic-biomarker-protocol-designer", "summary": "Designs discovery, modeling, and validation workflows for prognostic biomarkers in biomedical and clinical research. Always use this skill when the user needs a prognostic biomarker study blueprint rather than a diagnostic test protocol, predictive biomarker design, treatment recommendation, or a completed manuscript. Focus on endpoint family, follow-up horizon, time scale, candidate marker strategy, model-building logic, risk stratification framework, and internal/external validation requirements. Do not invent cohort size, event rate, assay readiness, literature support, or validation access.", "category": "Protocol Design", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "awesome-med-research-skills/Protocol Design/prognostic-biomarker-protocol-designer" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 90, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/awesome-med-research-skills/Protocol%20Design/prognostic-biomarker-protocol-designer/eval_report_prognostic-biomarker-protocol-designer_result.json", "evaluated_on": "2026-04-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 93, "max_score": 100 }, "dynamic_score": { "score": 87.7, "max_score": 100 } }, "release": { "path": "releases/prognostic-biomarker-protocol-designer/1.0.0.json", "sha256": "5c0e67a5b4ce19fc9a4535f63c190ae3cb497092d083e151517d8d4dcde39e7c" }, "artifact": { "path": "shards/630d1b4dab37083d632d924264ae93642b8327521aaa70fbf83de3cc7fd53d61.zip", "sha256": "630d1b4dab37083d632d924264ae93642b8327521aaa70fbf83de3cc7fd53d61", "bytes": 542522, "skill_path": "prognostic-biomarker-protocol-designer" }, "content_sha256": "7c964cd79d3124835f446c2d93f8c1cc4dd964b51827326bb5a0e62e476617a0" }, { "id": "prospero-registration-helper", "version": "1.0.0", "display_name": "prospero-registration-helper", "summary": "Assists researchers in generating PROSPERO registration content for meta-analyses from a title and optional protocol. Use when the user wants to draft a PROSPERO registration form.", "category": "Protocol Design", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Protocol Design/prospero-registration-helper" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 90, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Protocol%20Design/prospero-registration-helper/eval_report_prospero-registration-helper_result.json", "evaluated_on": "2026-06-15", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 96, "max_score": 100 }, "dynamic_score": { "score": 86.2, "max_score": 100 } }, "release": { "path": "releases/prospero-registration-helper/1.0.0.json", "sha256": "b98764dad705cb9beca6c4b3ca85f6862e241cb8416cc4c3f177c9544a99225b" }, "artifact": { "path": "shards/630d1b4dab37083d632d924264ae93642b8327521aaa70fbf83de3cc7fd53d61.zip", "sha256": "630d1b4dab37083d632d924264ae93642b8327521aaa70fbf83de3cc7fd53d61", "bytes": 542522, "skill_path": "prospero-registration-helper" }, "content_sha256": "beeabba6f613fb1fe30408ba7524ef2f02704b367572aed2fd562186a3739497" }, { "id": "protein-binder-design", "version": "1.0.0", "display_name": "protein-binder-design", "summary": "Orchestrate an end-to-end de novo protein binder design campaign against a protein target by composing BioNeMo NIM skills. Use for binder design, minibinder design, de novo binders, RFdiffusion + ProteinMPNN + Boltz2/OpenFold3 pipelines, epitope/hotspot-targeted design, in-silico binder validation, and ranking designs by interface confidence.\n", "category": "Protocol Design", "source": { "repository": "https://github.com/NVIDIA-BioNeMo/bionemo-agent-toolkit", "commit": "0e67a612e4045f007e38fa77adc8f3ebfc5616b6", "path": "skills/bionemo-agent-toolkit/skills/protein-binder-design" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "license": "Apache-2.0", "release": { "path": "releases/protein-binder-design/1.0.0.json", "sha256": "a8ffe7a6f3003cf2e8ba6962501649d83d281d2652c5182160697784826b0430" }, "artifact": { "path": "shards/d9eadedf27584c96257c396b3554a2f479383e134ebaf455716ea80dd4f8de94.zip", "sha256": "d9eadedf27584c96257c396b3554a2f479383e134ebaf455716ea80dd4f8de94", "bytes": 1161536, "skill_path": "protein-binder-design" }, "content_sha256": "78f292da59982f76c993712513f42efd8de2113622228656775d5819059f0a48" }, { "id": "proteinmpnn-nim", "version": "1.0.0", "display_name": "proteinmpnn-nim", "summary": "Run ProteinMPNN inverse folding via NVIDIA NIM to design protein sequences for a target backbone. Use for ProteinMPNN, inverse folding, sequence design, backbone redesign, fixed chains/residues, omit_AAs, sampling temperature, soluble model, hosted NVIDIA API, local Docker, PDB input, and multi-FASTA output.\n", "category": "Data Analysis", "source": { "repository": "https://github.com/NVIDIA-BioNeMo/bionemo-agent-toolkit", "commit": "0e67a612e4045f007e38fa77adc8f3ebfc5616b6", "path": "skills/bionemo-agent-toolkit/skills/proteinmpnn-nim" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "license": "Apache-2.0 AND CC-BY-4.0", "release": { "path": "releases/proteinmpnn-nim/1.0.0.json", "sha256": "53df6a68d4e85a175164e30830587791e0434a62600441132b2fb9370f6e5597" }, "artifact": { "path": "shards/d9eadedf27584c96257c396b3554a2f479383e134ebaf455716ea80dd4f8de94.zip", "sha256": "d9eadedf27584c96257c396b3554a2f479383e134ebaf455716ea80dd4f8de94", "bytes": 1161536, "skill_path": "proteinmpnn-nim" }, "content_sha256": "57f87b9ddd8c5b6a6f416a2884b986de75c9023134abe72164470a8e007e9994" }, { "id": "protocol-deviation-classifier", "version": "1.0.0", "display_name": "protocol-deviation-classifier", "summary": "Classify clinical trial protocol deviations as major or minor based on ICH E6/GCP guidelines. Three-impact-dimension assessment (safety, data integrity, scientific validity), confidence scoring, and regulatory compliance reporting with recommended actions.", "category": "Data Analysis", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Data Analysis/protocol-deviation-classifier" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 90, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Data%20Analysis/protocol-deviation-classifier/eval_report_protocol-deviation-classifier_result.json", "evaluated_on": "2026-06-15", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 95, "max_score": 100 }, "dynamic_score": { "score": 86.6, "max_score": 100 } }, "release": { "path": "releases/protocol-deviation-classifier/1.0.0.json", "sha256": "bf3d4f0f1a46ce8be4449c1f6ff45b5145b916b03c5f432922c1784c1cd8e05e" }, "artifact": { "path": "shards/363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb.zip", "sha256": "363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb", "bytes": 1471244, "skill_path": "protocol-deviation-classifier" }, "content_sha256": "7835280c07ab526c8de5522c3c324d9fab97c958672437862c1a80c866c6f92b" }, { "id": "protocol-standardization", "version": "1.0.0", "display_name": "protocol-standardization", "summary": "Standardize fragmented experimental steps into reproducible protocol documents when you need method organization, lab SOP drafting, or cross-operator reproducibility; missing parameters must be explicitly marked as \"To be supplemented/Not provided\".", "category": "Protocol Design", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Protocol Design/protocol-standardization" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 89, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Protocol%20Design/protocol-standardization/eval_report_protocol-standardization_result.json", "evaluated_on": "2026-06-15", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 92, "max_score": 100 }, "dynamic_score": { "score": 86.6, "max_score": 100 } }, "release": { "path": "releases/protocol-standardization/1.0.0.json", "sha256": "3756095cc0e7da32a15030236aee29158cca22c03eb5108c9ee5ca71211d5a0c" }, "artifact": { "path": "shards/630d1b4dab37083d632d924264ae93642b8327521aaa70fbf83de3cc7fd53d61.zip", "sha256": "630d1b4dab37083d632d924264ae93642b8327521aaa70fbf83de3cc7fd53d61", "bytes": 542522, "skill_path": "protocol-standardization" }, "content_sha256": "2cec427a938bd1ebcf8615368b76e99e09a1c80fbae837812193f0f9ba365e80" }, { "id": "pseudotime-trajectory-viz", "version": "1.0.1", "display_name": "pseudotime-trajectory-viz", "summary": "Analyze data with `pseudotime-trajectory-viz` using a reproducible workflow, explicit validation, and structured outputs for review-ready interpretation.", "category": "Data Analysis", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Data Analysis/pseudotime-trajectory-viz" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "Unknown", "evaluation": { "kind": "upstream-self-assessment", "score": 85, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Data%20Analysis/pseudotime-trajectory-viz/pseudotime-trajectory-viz_audit_result_v2.json", "evaluated_on": "2026-03-23", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 87, "max_score": 100 }, "dynamic_score": { "score": 84.2, "max_score": 100 } }, "release": { "path": "releases/pseudotime-trajectory-viz/1.0.1.json", "sha256": "2a171b6e5c56fc74bb0f1cb0680ba3032059d12dcb0f7fe9346e33ad1b42c4ac" }, "artifact": { "path": "shards/2b2d134141e3d5698b9e174f154599abf13d7ddd3c9060e29ef536399fd82407.zip", "sha256": "2b2d134141e3d5698b9e174f154599abf13d7ddd3c9060e29ef536399fd82407", "bytes": 17034738, "skill_path": "pseudotime-trajectory-viz" }, "content_sha256": "103f37c6037799d3dc768710e729de74feb43d7f82e5927fb057420b02b7b146" }, { "id": "pubchem-database-skill", "version": "1.0.0", "display_name": "pubchem-database-skill", "summary": "Programmatic access to the PubChem database (via PUG-REST API and PubChemPy) for searching chemical compounds, retrieving physicochemical properties, performing structure similarity/substructure searches, and obtaining bioactivity data.", "category": "Evidence Insight", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Evidence Insight/pubchem-database-skill" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 86, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Evidence%20Insight/pubchem-database-skill/pubchem-database-skill_audit_result_v1.json", "evaluated_on": "2026-03-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 87, "max_score": 100 }, "dynamic_score": { "score": 86, "max_score": 100 } }, "release": { "path": "releases/pubchem-database-skill/1.0.0.json", "sha256": "8e4757e5651fdba23a1e4b30d9e2ef33abe76d399eaa7090c68b5a930a160e77" }, "artifact": { "path": "shards/363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb.zip", "sha256": "363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb", "bytes": 1471244, "skill_path": "pubchem-database-skill" }, "content_sha256": "755e30d0facb06483edfae188fc5fa4104e67cdcc211c5573fd22b83380a3cd6" }, { "id": "pubmed-database", "version": "1.0.0", "display_name": "pubmed-database", "summary": "Direct REST API access to PubMed. Advanced Boolean/MeSH queries, E-utilities API, batch processing, citation management. For Python workflows, prefer biopython (Bio.Entrez). Use this for direct HTTP/REST work or custom API implementations.", "category": "Evidence Insight", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Evidence Insight/pubmed-database" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 88, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Evidence%20Insight/pubmed-database/eval_report_pubmed-database_result.json", "evaluated_on": "2026-06-15", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 89, "max_score": 100 }, "dynamic_score": { "score": 86.6, "max_score": 100 } }, "release": { "path": "releases/pubmed-database/1.0.0.json", "sha256": "c57b31077ad7a2018fac518c890e135da31946ec06a95b71e7e00fce4968298f" }, "artifact": { "path": "shards/363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb.zip", "sha256": "363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb", "bytes": 1471244, "skill_path": "pubmed-database" }, "content_sha256": "ff9f5e31ae1f37bc531b360d2e7ac4a02c33c2721e0a21d46201fdd2e9396139" }, { "id": "pubmed-search-specialist", "version": "1.0.0", "display_name": "pubmed-search-specialist", "summary": "Build complex Boolean query strings for precise PubMed/MEDLINE literature retrieval. Trigger when user needs MeSH term mapping, Boolean query construction, advanced PubMed filters, citation searching, systematic review search strategy, or clinical query optimization.", "category": "Evidence Insight", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Evidence Insight/pubmed-search-specialist" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 85, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Evidence%20Insight/pubmed-search-specialist/pubmed-search-specialist_audit_result_v1.json", "evaluated_on": "2026-03-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 88, "max_score": 100 }, "dynamic_score": { "score": 83.6, "max_score": 100 } }, "release": { "path": "releases/pubmed-search-specialist/1.0.0.json", "sha256": "e7d31b4a8d0c4c341c86171c4336128dba000af30f674b0c35cc5ff1419925fa" }, "artifact": { "path": "shards/363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb.zip", "sha256": "363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb", "bytes": 1471244, "skill_path": "pubmed-search-specialist" }, "content_sha256": "8945cad20f6e093d5f11976557079e0d92d1b4eb37d12aefd95eaf43c1044977" }, { "id": "pubmed-topic-recommend", "version": "1.0.0", "display_name": "pubmed-topic-recommend", "summary": "Generate ~5 actionable research topic recommendations by querying PubMed E-utilities; use when a user provides a research direction/constraints and needs evidence-backed topic ideas quickly.", "category": "Evidence Insight", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Evidence Insight/pubmed-topic-recommend" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 87, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Evidence%20Insight/pubmed-topic-recommend/pubmed-topic-recommend_audit_result_v2.json", "evaluated_on": "2026-03-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 88, "max_score": 100 }, "dynamic_score": { "score": 86, "max_score": 100 } }, "release": { "path": "releases/pubmed-topic-recommend/1.0.0.json", "sha256": "77fd05a5be7b58c7dbdf229a680877f5066e0c9ba588139b3314aff8ccb89f02" }, "artifact": { "path": "shards/363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb.zip", "sha256": "363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb", "bytes": 1471244, "skill_path": "pubmed-topic-recommend" }, "content_sha256": "9d6fde9cb7646eea7c8ec0c97c86213795c2eb4d2535cebfaa3c7274f8a92be7" }, { "id": "pydeseq", "version": "1.0.0", "display_name": "pydeseq", "summary": "Differential gene expression analysis for bulk RNA-seq count matrices using a DESeq2-like workflow in Python; use when you need Wald tests, FDR correction, and optional LFC shrinkage for condition/batch/covariate designs.", "category": "Data Analysis", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Data Analysis/pydeseq2" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "release": { "path": "releases/pydeseq/1.0.0.json", "sha256": "e6a7239b68adedad32c949043cb5a0b739553cac72ae9511460d63a6855ac9e9" }, "artifact": { "path": "shards/363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb.zip", "sha256": "363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb", "bytes": 1471244, "skill_path": "pydeseq" }, "content_sha256": "b9885518a6865dd9ae8f6538cfce4e90f8d1b0ee8208afd3903457840c0e9abb" }, { "id": "pydicom", "version": "1.0.0", "display_name": "pydicom", "summary": "Use pydicom to read, inspect, write, transform, and safely preflight local DICOM datasets and pixel data. Applies to DICOM metadata, transfer syntaxes, compression plugins, frames, private elements, JSON, and bounded de-identification review.", "category": "Data Analysis", "source": { "repository": "https://github.com/K-Dense-AI/scientific-agent-skills", "commit": "36d8f13a1e754618794bf42f417884940077b4ae", "path": "skills/pydicom" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "K-Dense Inc." } ], "license": "MIT", "release": { "path": "releases/pydicom/1.0.0.json", "sha256": "f280b73cc75b36c14c002a5c081b94f9ab37ce74d6276a1d9fabccb6f14eec1f" }, "artifact": { "path": "shards/b55fb597588b663d1d9dfe3695492a0b1a9b88845adbce48fe564656e55759c6.zip", "sha256": "b55fb597588b663d1d9dfe3695492a0b1a9b88845adbce48fe564656e55759c6", "bytes": 624089, "skill_path": "pydicom" }, "content_sha256": "620cba9d26c6a3cf6aa674c220eda56dca947e373887c2fbb9a986f1b56ff130" }, { "id": "pyhealth", "version": "1.0.1", "display_name": "pyhealth", "summary": "Build clinical/healthcare deep-learning pipelines with PyHealth — loading EHR/signal/imaging datasets (MIMIC-III/IV, eICU, OMOP, SleepEDF, ChestXray14, EHRShot), defining tasks (mortality, readmission, length-of-stay, drug recommendation, sleep staging, ICD coding, EEG events), instantiating models (Transformer, RETAIN, GAMENet, SafeDrug, MICRON, StageNet, AdaCare, CNN/RNN/MLP), training with the PyHealth Trainer, computing clinical metrics, and using medical code utilities (ICD/ATC/NDC/RxNorm lookup and cross-mapping). Use this skill whenever the user mentions PyHealth, MIMIC, eICU, OMOP, EHR modeling, clinical prediction, drug recommendation, sleep staging, medical code mapping, ICD/ATC codes, or any healthcare ML pipeline that fits the dataset → task → model → trainer → metrics pattern, even if \"PyHealth\" isn't named explicitly.", "category": "Data Analysis", "source": { "repository": "https://github.com/K-Dense-AI/scientific-agent-skills", "commit": "36d8f13a1e754618794bf42f417884940077b4ae", "path": "skills/pyhealth" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "K-Dense Inc." } ], "license": "Unknown", "release": { "path": "releases/pyhealth/1.0.1.json", "sha256": "9c736f6fed4acba1c5400019c86cac030bb543dd211b6df35d6595f8f927ce67" }, "artifact": { "path": "shards/2b2d134141e3d5698b9e174f154599abf13d7ddd3c9060e29ef536399fd82407.zip", "sha256": "2b2d134141e3d5698b9e174f154599abf13d7ddd3c9060e29ef536399fd82407", "bytes": 17034738, "skill_path": "pyhealth" }, "content_sha256": "d3d780da12acc22a934646bc8c2455bdf585091e9ff3af146e021142cfa4b707" }, { "id": "pyopenms-skill", "version": "1.0.0", "display_name": "pyopenms-skill", "summary": "Comprehensive tool for computational mass spectrometry using PyOpenMS; use when you need to read/write MS formats (mzML/mzXML/MGF), run signal processing (smoothing/peak picking), detect isotope features, or perform peptide identification in proteomics/metabolomics workflows.", "category": "Data Analysis", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Data Analysis/pyopenms-skill" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 86, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Data%20Analysis/pyopenms-skill/pyopenms-skill_audit_result_v1.json", "evaluated_on": "2026-03-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 85, "max_score": 100 }, "dynamic_score": { "score": 86.6, "max_score": 100 } }, "release": { "path": "releases/pyopenms-skill/1.0.0.json", "sha256": "11fdc989b988ff51b48ece3f12b4be2001a537db89b05dc50f57fb08c679d915" }, "artifact": { "path": "shards/363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb.zip", "sha256": "363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb", "bytes": 1471244, "skill_path": "pyopenms-skill" }, "content_sha256": "c520def8559d94fdaeee4befdae44e881dcea6d567e6cd573403cda73e13716e" }, { "id": "pysam", "version": "1.0.0", "display_name": "pysam", "summary": "Python/HTSlib workflows for genomic files. Use when reading, querying, filtering, or writing SAM/BAM/CRAM, VCF/BCF, FASTA/FASTQ, or tabix data with pysam, including pileup, coverage, indexing, and CRAM references.", "category": "Data Analysis", "source": { "repository": "https://github.com/K-Dense-AI/scientific-agent-skills", "commit": "36d8f13a1e754618794bf42f417884940077b4ae", "path": "skills/pysam" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "K-Dense Inc." } ], "license": "MIT", "release": { "path": "releases/pysam/1.0.0.json", "sha256": "34cd1ddcd2594864762d95c896f1c16488fe3029e92aad4870df1c8263cc8ee9" }, "artifact": { "path": "shards/b55fb597588b663d1d9dfe3695492a0b1a9b88845adbce48fe564656e55759c6.zip", "sha256": "b55fb597588b663d1d9dfe3695492a0b1a9b88845adbce48fe564656e55759c6", "bytes": 624089, "skill_path": "pysam" }, "content_sha256": "bd21289c79d62bfa5709853a2201836c440b2c8ab8dc86307c3d91e92e6d7f84" }, { "id": "pytdc", "version": "1.0.0", "display_name": "pytdc", "summary": "Therapeutics Data Commons (PyTDC) for AI-ready therapeutic ML datasets and benchmarks; use it when you need standardized dataset loading, meaningful splits (e.g., scaffold/cold-start), and consistent evaluation for ADME/Toxicity/DTI/DDI or molecular optimization.", "category": "Evidence Insight", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Evidence Insight/pytdc" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 87, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Evidence%20Insight/pytdc/pytdc_audit_result_v1.json", "evaluated_on": "2026-03-23", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 87, "max_score": 100 }, "dynamic_score": { "score": 87.6, "max_score": 100 } }, "release": { "path": "releases/pytdc/1.0.0.json", "sha256": "bcd909b38f20ae7c2cc2fa7a8eac65a6dec5f794bf8ed605056d126e4947d104" }, "artifact": { "path": "shards/363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb.zip", "sha256": "363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb", "bytes": 1471244, "skill_path": "pytdc" }, "content_sha256": "a90a40975fa689215972a7631349ff63eaa09fd4ce67ddbc507dd4e491b679d6" }, { "id": "q-and-a-prep-partner", "version": "1.0.1", "display_name": "q-and-a-prep-partner", "summary": "Predict challenging questions for presentations and prepare structured responses.", "category": "Academic Writing", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Academic Writing/q-and-a-prep-partner" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 85, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Academic%20Writing/q-and-a-prep-partner/q-and-a-prep-partner_audit_result_v2.json", "evaluated_on": "2026-03-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 88, "max_score": 100 }, "dynamic_score": { "score": 83.6, "max_score": 100 } }, "release": { "path": "releases/q-and-a-prep-partner/1.0.1.json", "sha256": "56847676df4f3d8f7c4c804a18abf7b71a1c662da4f02e18ca621237a5af3b49" }, "artifact": { "path": "shards/2b2d134141e3d5698b9e174f154599abf13d7ddd3c9060e29ef536399fd82407.zip", "sha256": "2b2d134141e3d5698b9e174f154599abf13d7ddd3c9060e29ef536399fd82407", "bytes": 17034738, "skill_path": "q-and-a-prep-partner" }, "content_sha256": "9c368f4f9546ee1c1c658898cc3dd8080f9268453e3aa4c974dd4dda1620d415" }, { "id": "qtl-colocalization-study-planner", "version": "1.0.0", "display_name": "qtl-colocalization-study-planner", "summary": "Designs QTL colocalization studies that connect eQTL, pQTL, sQTL, or related molecular QTL signals with GWAS loci. Always use this skill whenever a user wants to plan, scope, or structure a locus-level study asking whether a GWAS association and a molecular QTL association may reflect the same underlying causal signal. Covers locus definition, QTL/GWAS source architecture, ancestry and LD alignment, single-locus vs multi-locus strategy, candidate-gene prioritization, optional fine-mapping, linked MR/SMR follow-up, and functional annotation. Always output four workload configurations (Lite / Standard / Advanced / Publication+) with a recommended primary plan, stepwise workflow, method rationale, evidence hierarchy, figure plan, minimal executable version, and strictly verified literature guidance with no fabricated references. Never equate colocalization with causality proof, mediation proof, or automatic target validation. Always include the mandatory Dataset Disclaimer immediately before any workflow section that mentions datasets, repositories, consortia, or public resources.", "category": "Protocol Design", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "awesome-med-research-skills/Protocol Design/qtl-colocalization-study-planner" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 91, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/awesome-med-research-skills/Protocol%20Design/qtl-colocalization-study-planner/eval_report_qtl-colocalization-study-planner_result.json", "evaluated_on": "2026-04-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 94, "max_score": 100 }, "dynamic_score": { "score": 88.7, "max_score": 100 } }, "release": { "path": "releases/qtl-colocalization-study-planner/1.0.0.json", "sha256": "cbde41ab325b24ced810caff4e632fc08ce23ca02356be89b3ebea4ef4a87f5c" }, "artifact": { "path": "shards/630d1b4dab37083d632d924264ae93642b8327521aaa70fbf83de3cc7fd53d61.zip", "sha256": "630d1b4dab37083d632d924264ae93642b8327521aaa70fbf83de3cc7fd53d61", "bytes": 542522, "skill_path": "qtl-colocalization-study-planner" }, "content_sha256": "7e96344b580bdb1f6b40bb43e479313b8749226819b04aaa4c2ae275b59f7d32" }, { "id": "quadas-c-assessment-for-diagnostic-accuracy-studies", "version": "1.0.1", "display_name": "quadas-c-assessment-for-diagnostic-accuracy-studies", "summary": "Automated bias assessment for diagnostic accuracy studies using QUADAS-C criteria. Requires full text input.", "category": "Data Analysis", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Data Analysis/quadas-c-assessment-for-diagnostic-accuracy-studies" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "Unknown", "evaluation": { "kind": "upstream-self-assessment", "score": 85, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Data%20Analysis/quadas-c-assessment-for-diagnostic-accuracy-studies/quadas-c-assessment-for-diagnostic-accuracy-studies_audit_result_v1.json", "evaluated_on": "2026-03-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 85, "max_score": 100 }, "dynamic_score": { "score": 85.4, "max_score": 100 } }, "release": { "path": "releases/quadas-c-assessment-for-diagnostic-accuracy-studies/1.0.1.json", "sha256": "e02c39cae594d514ca33ac7c307040eea6a36080a7f14628aeac550935313a17" }, "artifact": { "path": "shards/2b2d134141e3d5698b9e174f154599abf13d7ddd3c9060e29ef536399fd82407.zip", "sha256": "2b2d134141e3d5698b9e174f154599abf13d7ddd3c9060e29ef536399fd82407", "bytes": 17034738, "skill_path": "quadas-c-assessment-for-diagnostic-accuracy-studies" }, "content_sha256": "6f4d11f721ba1e54cbffa9acd1e73e730664bb5f1086cad11251632dca90aa19" }, { "id": "quality-assessment", "version": "1.0.1", "display_name": "quality-assessment", "summary": "Automates critical appraisal and quality assessment for research papers by analyzing text against established methodological standards (such as risk of bias tools, quality checklists, or reporting guidelines) and synthesizing a structured evaluation report. Use when you need to assess the methodological quality, internal validity, or reporting completeness of any type of study—including RCTs, observational studies, systematic reviews, qualitative research, or diagnostic accuracy studies.", "category": "Data Analysis", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Data Analysis/quality-assessment" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "Unknown", "evaluation": { "kind": "upstream-self-assessment", "score": 87, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Data%20Analysis/quality-assessment/quality-assessment_audit_result_v2.json", "evaluated_on": "2026-03-23", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 88, "max_score": 100 }, "dynamic_score": { "score": 86.2, "max_score": 100 } }, "release": { "path": "releases/quality-assessment/1.0.1.json", "sha256": "99dae1df16d2c64f6a8bfe38e91d66031da48a01f9c6b38e9d6239c0c114e530" }, "artifact": { "path": "shards/2b2d134141e3d5698b9e174f154599abf13d7ddd3c9060e29ef536399fd82407.zip", "sha256": "2b2d134141e3d5698b9e174f154599abf13d7ddd3c9060e29ef536399fd82407", "bytes": 17034738, "skill_path": "quality-assessment" }, "content_sha256": "1758d25221ae66e3bc367aec1095eb7e117935bf97f4e7c4005ce712625976d8" }, { "id": "quapas-quality-assessment-for-prognosis-studies", "version": "1.0.1", "display_name": "quapas-quality-assessment-for-prognosis-studies", "summary": "Evaluates bias in medical literature (prognosis studies) using QUAPAS criteria. Use when the user wants to assess the quality or risk of bias of a medical paper text.", "category": "Data Analysis", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Data Analysis/quapas-quality-assessment-for-prognosis-studies" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "Unknown", "evaluation": { "kind": "upstream-self-assessment", "score": 87, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Data%20Analysis/quapas-quality-assessment-for-prognosis-studies/quapas-quality-assessment-for-prognosis-studies_audit_result_v1.json", "evaluated_on": "2026-03-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 81, "max_score": 100 }, "dynamic_score": { "score": 90.6, "max_score": 100 } }, "release": { "path": "releases/quapas-quality-assessment-for-prognosis-studies/1.0.1.json", "sha256": "8d159dea9735a9be9b11626094aef64474c17d1589787df80bebf4150ca15e12" }, "artifact": { "path": "shards/2b2d134141e3d5698b9e174f154599abf13d7ddd3c9060e29ef536399fd82407.zip", "sha256": "2b2d134141e3d5698b9e174f154599abf13d7ddd3c9060e29ef536399fd82407", "bytes": 17034738, "skill_path": "quapas-quality-assessment-for-prognosis-studies" }, "content_sha256": "7afbd94eaf2d594159c49409fa49b0801a0d3fce82bd1a34e1c6e54e4c66f81e" }, { "id": "rare-disease-hpo-mapper", "version": "1.0.1", "display_name": "rare-disease-hpo-mapper", "summary": "Map patient symptoms to Human Phenotype Ontology terms for gene diagnosis.", "category": "Evidence Insight", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Evidence Insight/rare-disease-hpo-mapper" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "Unknown", "evaluation": { "kind": "upstream-self-assessment", "score": 85, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Evidence%20Insight/rare-disease-hpo-mapper/rare-disease-hpo-mapper_audit_result_v2.json", "evaluated_on": "2026-03-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 88, "max_score": 100 }, "dynamic_score": { "score": 83.6, "max_score": 100 } }, "release": { "path": "releases/rare-disease-hpo-mapper/1.0.1.json", "sha256": "dc06fe8fe7cead4997c3c849765204d3a32461a12ba0e5dd51bb49e4c2c53a7d" }, "artifact": { "path": "shards/2b2d134141e3d5698b9e174f154599abf13d7ddd3c9060e29ef536399fd82407.zip", "sha256": "2b2d134141e3d5698b9e174f154599abf13d7ddd3c9060e29ef536399fd82407", "bytes": 17034738, "skill_path": "rare-disease-hpo-mapper" }, "content_sha256": "96e91c4e260cf71b649ac1da852f9b7b9dd3d2a637a903e444793d0d4f4ef56e" }, { "id": "rct-bias-assessment-rob", "version": "1.0.1", "display_name": "rct-bias-assessment-rob", "summary": "Automates Risk of Bias 2 (ROB2) assessment for RCT papers by analyzing text against specific domains and synthesizing a report. Use when you need to assess the quality of a clinical trial paper or evaluate risk of bias.", "category": "Data Analysis", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Data Analysis/rct-bias-assessment-rob2" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "Unknown", "release": { "path": "releases/rct-bias-assessment-rob/1.0.1.json", "sha256": "5cf45ae7219cea44eef1b298922f86053a77e30ef6c7acc4c5d63ee9d0b831a2" }, "artifact": { "path": "shards/2b2d134141e3d5698b9e174f154599abf13d7ddd3c9060e29ef536399fd82407.zip", "sha256": "2b2d134141e3d5698b9e174f154599abf13d7ddd3c9060e29ef536399fd82407", "bytes": 17034738, "skill_path": "rct-bias-assessment-rob" }, "content_sha256": "962106c883cf09b3805add08215c35cebe63032e96fd59d6a4e4e7a6c4dc83e5" }, { "id": "rct-bias-assessment-rob2", "version": "1.0.1", "display_name": "rct-bias-assessment-rob2", "summary": "Automates Risk of Bias 2 (ROB2) assessment for RCT papers by analyzing text against specific domains and synthesizing a report. Use when you need to assess the quality of a clinical trial paper or evaluate risk of bias.", "category": "Evidence Insight", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Evidence Insight/rct-bias-assessment-rob2" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "Unknown", "evaluation": { "kind": "upstream-self-assessment", "score": 91, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Evidence%20Insight/rct-bias-assessment-rob2/eval_report_rct-bias-assessment-rob2_result.json", "evaluated_on": "2026-06-15", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 98, "max_score": 100 }, "dynamic_score": { "score": 86.2, "max_score": 100 } }, "release": { "path": "releases/rct-bias-assessment-rob2/1.0.1.json", "sha256": "5aa494e4bb4eccfbc49ffc05ae515224f74d802fa65f4cdd52cb5a6c53154724" }, "artifact": { "path": "shards/2b2d134141e3d5698b9e174f154599abf13d7ddd3c9060e29ef536399fd82407.zip", "sha256": "2b2d134141e3d5698b9e174f154599abf13d7ddd3c9060e29ef536399fd82407", "bytes": 17034738, "skill_path": "rct-bias-assessment-rob2" }, "content_sha256": "b9c1bee2db04dbfb3ac9cb3360970ad81379877a2fe62fdc4fd3ce25c94880dc" }, { "id": "reagent-expiry-alert", "version": "1.0.0", "display_name": "reagent-expiry-alert", "summary": "Scan reagent barcodes or IDs, log expiration dates, and generate multi-level alerts before reagent expiry to support laboratory inventory management.", "category": "Other", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Other/reagent-expiry-alert" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 86, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Other/reagent-expiry-alert/reagent-expiry-alert_audit_result_v2.json", "evaluated_on": "2026-03-19", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 84, "max_score": 100 }, "dynamic_score": { "score": 87, "max_score": 100 } }, "release": { "path": "releases/reagent-expiry-alert/1.0.0.json", "sha256": "fa4ed1aa35b3750050e9a0c832a2419a49b333eb49c98c0f45a5c0c89192a564" }, "artifact": { "path": "shards/fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0.zip", "sha256": "fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0", "bytes": 1383584, "skill_path": "reagent-expiry-alert" }, "content_sha256": "9c3871701c62ef76b7a029de53cbf0aadf36b10a63a9ed5c80b3428feb154405" }, { "id": "real-world-evidence-study-designer", "version": "1.0.0", "display_name": "real-world-evidence-study-designer", "summary": "Designs a structured real-world evidence study using EHR, claims, or registry data, with explicit handling of time zero, eligibility windows, exposure definitions, outcome windows, censoring, confounding control, and target-trial-emulation logic. Use this skill when the user needs study-type design and protocol framing for an observational clinical study based on routine-care data. 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Generate module outlines (Modules 1-5), run ICH compliance checks (ALCOA+, E6 GCP, Q1-Q12), verify data integrity, and produce submission-ready dossiers with cover letters and checklists.", "category": "Academic Writing", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Academic Writing/regulatory-submission" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 87, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Academic%20Writing/regulatory-submission/eval_report_regulatory-submission_result.json", "evaluated_on": "2026-06-15", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 88, "max_score": 100 }, "dynamic_score": { "score": 86, "max_score": 100 } }, "release": { "path": "releases/regulatory-submission/1.0.1.json", "sha256": "73d3414ade216615f2aaf8ec46cf110a03d9a75269a40a3304c5278839560bab" }, "artifact": { "path": "shards/2b2d134141e3d5698b9e174f154599abf13d7ddd3c9060e29ef536399fd82407.zip", "sha256": "2b2d134141e3d5698b9e174f154599abf13d7ddd3c9060e29ef536399fd82407", "bytes": 17034738, "skill_path": "regulatory-submission" }, "content_sha256": "a4f50a1bc4c27de07a176cbadf4f5dbaf5bfef34dd6510b8fdb075a48c3e5775" }, { "id": "reporting-guideline-compliance-checker", "version": "1.0.1", "display_name": "reporting-guideline-compliance-checker", "summary": "Checks biomedical manuscripts against reporting guidelines such as CONSORT, STROBE, PRISMA, and TRIPOD to identify missing or weak reporting elements before submission or revision.", "category": "Academic Writing", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "awesome-med-research-skills/Academic Writing/reporting-guideline-compliance-checker" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 91, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/awesome-med-research-skills/Academic%20Writing/reporting-guideline-compliance-checker/eval_report_reporting-guideline-compliance-checker_result.json", "evaluated_on": "2026-04-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 93, "max_score": 100 }, "dynamic_score": { "score": 90.1, "max_score": 100 } }, "release": { "path": "releases/reporting-guideline-compliance-checker/1.0.1.json", "sha256": "a464ca270d831dad97040cc013f4f7014fc8fdf27cb124286cd438fea2c7c2ee" }, "artifact": { "path": "shards/2b2d134141e3d5698b9e174f154599abf13d7ddd3c9060e29ef536399fd82407.zip", "sha256": "2b2d134141e3d5698b9e174f154599abf13d7ddd3c9060e29ef536399fd82407", "bytes": 17034738, "skill_path": "reporting-guideline-compliance-checker" }, "content_sha256": "0f447597303c5b8dab8ae0c88cbfa60fcfc3e7193ccb239c357cc586aa103f23" }, { "id": "reproducibility-check", "version": "1.0.0", "display_name": "reproducibility-check", "summary": "Comprehensive reproducibility tool — audit Methods completeness for replication AND promote open science best practices (pre-registration, FAIR data, code sharing, replication design, reporting transparency); 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Use when users need to identify trending directions, topic clusters, or generate hotspot review reports. 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Use for composing, revising, or auditing research proposals, opening reports, research plans, and evidence-grounded scientific writing. Three modes (compose/revise/hybrid) with a four-layer QA pipeline. Enforces evidence-before-prose, argument-before-sections, and contracts-before-paragraphs.\n", "category": "Academic Writing", "source": { "repository": "https://github.com/Yuan1z0825/nature-skills", "commit": "558eae555d64e09b152efc449ca274bbd7747947", "path": "skills/nature-proposal-writer" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "license": "MIT", "release": { "path": "releases/researchwrite/1.0.0.json", "sha256": "143018d01537bfdda599ddd8fbc923753d79c195b883f92f6ec130e0ac2c10f1" }, "artifact": { "path": "shards/fbeb5f12afe3c0c6924b0974d44144de230c07662f05cd53d487f267a6a0eb70.zip", "sha256": "fbeb5f12afe3c0c6924b0974d44144de230c07662f05cd53d487f267a6a0eb70", "bytes": 244577, "skill_path": "researchwrite" }, "content_sha256": "d54969f0aadd551324ec628105a7916d050c184aba7445748a98d24a72d6f4f9" }, { "id": "residency-interview-prep", "version": "1.0.1", "display_name": "residency-interview-prep", "summary": "Mock interview preparation tool for residency Match interviews. Generates.", "category": "Academic Writing", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Academic Writing/residency-interview-prep" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 85, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Academic%20Writing/residency-interview-prep/residency-interview-prep_audit_result_v1.json", "evaluated_on": "2026-03-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 88, "max_score": 100 }, "dynamic_score": { "score": 83.6, "max_score": 100 } }, "release": { "path": "releases/residency-interview-prep/1.0.1.json", "sha256": "3bdbba46c65c271b69a57f54a143f996a0e671e1e1bec6a073805acf2b41bc26" }, "artifact": { "path": "shards/2b2d134141e3d5698b9e174f154599abf13d7ddd3c9060e29ef536399fd82407.zip", "sha256": "2b2d134141e3d5698b9e174f154599abf13d7ddd3c9060e29ef536399fd82407", "bytes": 17034738, "skill_path": "residency-interview-prep" }, "content_sha256": "8c1fcf2197e41bc638185413be6f82d5ec94329aaefaa4d7265c8a8cbc22185d" }, { "id": "resubmission-deadline-tracker", "version": "1.0.1", "display_name": "resubmission-deadline-tracker", "summary": "Track manuscript resubmission deadlines and automatically generate phase-appropriate task breakdowns for academic researchers based on remaining time.", "category": "Other", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "63c61d38c6c4bba5128f98f0b225aa44e3fe748d", "path": "scientific-skills/Other/resubmission-deadline-tracker" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "Unknown", "evaluation": { "kind": "upstream-self-assessment", "score": 87, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/63c61d38c6c4bba5128f98f0b225aa44e3fe748d/scientific-skills/Other/resubmission-deadline-tracker/resubmission-deadline-tracker_audit_result_v2.json", "evaluated_on": "2026-03-19", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 88, "max_score": 100 }, "dynamic_score": { "score": 86.4, "max_score": 100 } }, "release": { "path": "releases/resubmission-deadline-tracker/1.0.1.json", "sha256": "13c4da343ea065831e2bea41732fa132187574d5cef0bd57e9c79c882fc139f2" }, "artifact": { "path": "shards/2b2d134141e3d5698b9e174f154599abf13d7ddd3c9060e29ef536399fd82407.zip", "sha256": "2b2d134141e3d5698b9e174f154599abf13d7ddd3c9060e29ef536399fd82407", "bytes": 17034738, "skill_path": "resubmission-deadline-tracker" }, "content_sha256": "491fc927eb4af1cf8a789fb83df456d2f879a9c9541b42be2a917207d47ef015" }, { "id": "result-figure-consistencycheck", "version": "1.0.0", "display_name": "result-figure-consistencycheck", "summary": "Checks consistency between paper result descriptions and figure legends (text-only) when the input is a PDF-to-Markdown full text containing page breaks (e.g., `## Page XX`) and legend text; outputs a Markdown consistency report and a UTF-8 CSV issue list.", "category": "Other", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Other/result-figure-consistencycheck" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 89, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Other/result-figure-consistencycheck/eval_report_result-figure-consistencycheck_result.json", "evaluated_on": "2026-06-15", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 94, "max_score": 100 }, "dynamic_score": { "score": 86.2, "max_score": 100 } }, "release": { "path": "releases/result-figure-consistencycheck/1.0.0.json", "sha256": "a96fb0091f820dd362c4aa94bc5f30219447e3ade82528ac2a9d327041108711" }, "artifact": { "path": "shards/1ea735cead62d521282de0bf8a672a95515d73332ea4fa7d5d2ccf72eed45995.zip", "sha256": "1ea735cead62d521282de0bf8a672a95515d73332ea4fa7d5d2ccf72eed45995", "bytes": 342914, "skill_path": "result-figure-consistencycheck" }, "content_sha256": "465d6784fcb88af0790ffd629e64017ccf6a96595ce6d6a15b2d95c2fb66c94a" }, { "id": "result-reliability-checker", "version": "1.0.0", "display_name": "result-reliability-checker", "summary": "Assesses whether study results are trustworthy by auditing design integrity, sample structure, statistical handling, bias control, validation chain, and claim discipline. It identifies where results are robust, fragile, overfit, under-validated, or overclaimed. Always separate reported findings from reliability judgment. 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"release": { "path": "releases/result-reliability-checker/1.0.0.json", "sha256": "40cc3020a10cb8015db6f2c926ba23fb9ca4bea3c45def5bce263473b6844d87" }, "artifact": { "path": "shards/fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0.zip", "sha256": "fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0", "bytes": 1383584, "skill_path": "result-reliability-checker" }, "content_sha256": "db11714b3800e0bb4e8e579e8126e94699763d768665e06f884e5fe7c5be556f" }, { "id": "results-section-structurer", "version": "1.0.1", "display_name": "results-section-structurer", "summary": "Organizes biomedical figures, analyses, and result blocks into a clear Results section structure with disciplined narrative ordering and evidence-aware presentation.", "category": "Academic Writing", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "awesome-med-research-skills/Academic Writing/results-section-structurer" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 92, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/awesome-med-research-skills/Academic%20Writing/results-section-structurer/eval_report_results-section-structurer_result.json", "evaluated_on": "2026-04-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 94, "max_score": 100 }, "dynamic_score": { "score": 89.9, "max_score": 100 } }, "release": { "path": "releases/results-section-structurer/1.0.1.json", "sha256": "b4227531d1de6e1619df1a6a1d15e5be8018829866970d89dd356fcd5815269a" }, "artifact": { "path": "shards/2b2d134141e3d5698b9e174f154599abf13d7ddd3c9060e29ef536399fd82407.zip", "sha256": "2b2d134141e3d5698b9e174f154599abf13d7ddd3c9060e29ef536399fd82407", "bytes": 17034738, "skill_path": "results-section-structurer" }, "content_sha256": "e92649bb297c436b18b2110e38fd7caa82555cfaa0df49f8e755da5000c21f5a" }, { "id": "results-section-writer", "version": "1.0.1", "display_name": "results-section-writer", "summary": "Writes the full Results section of a biomedical manuscript from a sufficiently clear result structure, figure inventory, or analysis summary while preserving evidence boundaries and result hierarchy.", "category": "Academic Writing", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "awesome-med-research-skills/Academic Writing/results-section-writer" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 93, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/awesome-med-research-skills/Academic%20Writing/results-section-writer/eval_report_results-section-writer_result.json", "evaluated_on": "2026-04-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 97, "max_score": 100 }, "dynamic_score": { "score": 90.3, "max_score": 100 } }, "release": { "path": "releases/results-section-writer/1.0.1.json", "sha256": "b3e079b0a18ededeb79c87ab4b8b73e8a49053685362a0361e55eced108f473d" }, "artifact": { "path": "shards/2b2d134141e3d5698b9e174f154599abf13d7ddd3c9060e29ef536399fd82407.zip", "sha256": "2b2d134141e3d5698b9e174f154599abf13d7ddd3c9060e29ef536399fd82407", "bytes": 17034738, "skill_path": "results-section-writer" }, "content_sha256": "0081801f568b340adf6bb33549b29cb195a79ea8a9f2ea1f1a1fd19b60496259" }, { "id": "retraction-watcher", "version": "1.0.0", "display_name": "retraction-watcher", "summary": "Automatically scan reference lists and check whether cited papers have been retracted, corrected, or flagged; use before submission, review, or evidence synthesis to reduce citation risk.", "category": "Evidence Insight", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Evidence Insight/retraction-watcher" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 90, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Evidence%20Insight/retraction-watcher/eval_report_retraction-watcher_result.json", "evaluated_on": "2026-06-15", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 96, "max_score": 100 }, "dynamic_score": { "score": 86.6, "max_score": 100 } }, "release": { "path": "releases/retraction-watcher/1.0.0.json", "sha256": "4a087106d984c751b91c3d748b9aa0d7f8250be0bf674d947311108d9e6cfe5f" }, "artifact": { "path": "shards/fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0.zip", "sha256": "fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0", "bytes": 1383584, "skill_path": "retraction-watcher" }, "content_sha256": "4b8dcd82c7f543205bf1590164c5ba6fe8ea38086bcaee059b6a8d0b4474589d" }, { "id": "revision-strategy-planner", "version": "1.0.1", "display_name": "revision-strategy-planner", "summary": "Builds prioritized manuscript revision plans for major or minor revisions by separating comments that require experiments, analyses, clarification, restructuring, or wording changes.", "category": "Academic Writing", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "awesome-med-research-skills/Academic Writing/revision-strategy-planner" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 91, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/awesome-med-research-skills/Academic%20Writing/revision-strategy-planner/eval_report_revision-strategy-planner_result.json", "evaluated_on": "2026-04-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 92, "max_score": 100 }, "dynamic_score": { "score": 89.6, "max_score": 100 } }, "release": { "path": "releases/revision-strategy-planner/1.0.1.json", "sha256": "09ea969dcb029940e3e16725909288636505596d583824fc7c92747c0b646c4d" }, "artifact": { "path": "shards/2b2d134141e3d5698b9e174f154599abf13d7ddd3c9060e29ef536399fd82407.zip", "sha256": "2b2d134141e3d5698b9e174f154599abf13d7ddd3c9060e29ef536399fd82407", "bytes": 17034738, "skill_path": "revision-strategy-planner" }, "content_sha256": "dfcf92fb75196cf2a09b2851fa243f5341667c11a1e078976bb8a9d6aa08e4d3" }, { "id": "rf-model-importance-analysis", "version": "1.0.1", "display_name": "rf-model-importance-analysis", "summary": "Use when you need a standardized R CLI workflow to train a two-class random forest model from an expression-like feature matrix, rank variable importance, and generate reproducible error and importance plots. NOT for regression tasks, multi-class classification, missing-value imputation, preprocessing, or remote data fetching.", "category": "Data Analysis", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "awesome-med-research-skills/Data Analysis/rf-model-importance-analysis" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "Unknown", "evaluation": { "kind": "upstream-self-assessment", "score": 92, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/awesome-med-research-skills/Data%20Analysis/rf-model-importance-analysis/eval_report_rf-model-importance-analysis_result.json", "evaluated_on": "2026-04-20", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 97, "max_score": 100 }, "dynamic_score": { "score": 88.2, "max_score": 100 } }, "release": { "path": "releases/rf-model-importance-analysis/1.0.1.json", "sha256": "7154f2d48d8cc555fb8a61f133e70a377327ed0ff6d31ba8ac8b320ed02c4508" }, "artifact": { "path": "shards/2b2d134141e3d5698b9e174f154599abf13d7ddd3c9060e29ef536399fd82407.zip", "sha256": "2b2d134141e3d5698b9e174f154599abf13d7ddd3c9060e29ef536399fd82407", "bytes": 17034738, "skill_path": "rf-model-importance-analysis" }, "content_sha256": "f28400bd987708edd3ce4bf08f40f77050cf87ea8e470c6e779a82a0b25bd0bb" }, { "id": "rfdiffusion-nim", "version": "1.0.0", "display_name": "rfdiffusion-nim", "summary": "Run RFDiffusion protein backbone design via NVIDIA NIM. Use for de novo protein backbones, motif scaffolding, binder design, hotspot residues, contigs syntax, diffusion steps, hosted NVIDIA API calls, local Docker deployment, and PDB backbone outputs for ProteinMPNN sequence design.\n", "category": "Data Analysis", "source": { "repository": "https://github.com/NVIDIA-BioNeMo/bionemo-agent-toolkit", "commit": "0e67a612e4045f007e38fa77adc8f3ebfc5616b6", "path": "skills/bionemo-agent-toolkit/skills/rfdiffusion-nim" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "license": "Apache-2.0 AND CC-BY-4.0", "release": { "path": "releases/rfdiffusion-nim/1.0.0.json", "sha256": "0e9d803fdc004645f60a16ce2c13948ac36be88437eba3b56945a8835e90dc72" }, "artifact": { "path": "shards/d9eadedf27584c96257c396b3554a2f479383e134ebaf455716ea80dd4f8de94.zip", "sha256": "d9eadedf27584c96257c396b3554a2f479383e134ebaf455716ea80dd4f8de94", "bytes": 1161536, "skill_path": "rfdiffusion-nim" }, "content_sha256": "df5b3dc126c36630be16072b176894555a7328a9d75c99fc92911d9096b5a179" }, { "id": "sample-correlation-analysis", "version": "1.0.1", "display_name": "sample-correlation-analysis", "summary": "Use when performing correlation analysis between two variables including Pearson and Spearman correlation methods. Supports command-line parameter input, automatic data format detection, parameter validation, result directory creation, and CSV or TXT format result export.", "category": "Data Analysis", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "awesome-med-research-skills/Data Analysis/sample-correlation-analysis" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "Unknown", "evaluation": { "kind": "upstream-self-assessment", "score": 86, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/awesome-med-research-skills/Data%20Analysis/sample-correlation-analysis/eval_report_sample-correlation-analysis_result.json", "evaluated_on": "2026-04-15", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 89, "max_score": 100 }, "dynamic_score": { "score": 84.4, "max_score": 100 } }, "release": { "path": "releases/sample-correlation-analysis/1.0.1.json", "sha256": "9b9cc418d73d23a70bc34bd41f3992da835efe88e64fa61cd59058a7ee509f6d" }, "artifact": { "path": "shards/2b2d134141e3d5698b9e174f154599abf13d7ddd3c9060e29ef536399fd82407.zip", "sha256": "2b2d134141e3d5698b9e174f154599abf13d7ddd3c9060e29ef536399fd82407", "bytes": 17034738, "skill_path": "sample-correlation-analysis" }, "content_sha256": "ac7ee0cd0092fff94cc93f96a0546d2001308239d1fd2286765fee74fa40da57" }, { "id": "sample-group-sankey-plot", "version": "1.0.0", "display_name": "sample-group-sankey-plot", "summary": "Use when generating Sankey or alluvial plots from sample annotation tables where rows are samples and selected columns are categorical stages such as risk group, response status, subtype, or cohort labels. NOT for: gene network flow analysis, continuous-value trajectories, or graph-structured pathway visualization.", "category": "Data Analysis", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "awesome-med-research-skills/Data Analysis/sample-group-sankey-plot" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 96, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/awesome-med-research-skills/Data%20Analysis/sample-group-sankey-plot/eval_report_sample-group-sankey-plot_result.json", "evaluated_on": "2026-04-27", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 95, "max_score": 100 }, "dynamic_score": { "score": 97.2, "max_score": 100 } }, "release": { "path": "releases/sample-group-sankey-plot/1.0.0.json", "sha256": "f9ac61654ef89275d2c6a7b18d685089a78d113883c3b5fd7e9aed079da2fc70" }, "artifact": { "path": "shards/1ea735cead62d521282de0bf8a672a95515d73332ea4fa7d5d2ccf72eed45995.zip", "sha256": "1ea735cead62d521282de0bf8a672a95515d73332ea4fa7d5d2ccf72eed45995", "bytes": 342914, "skill_path": "sample-group-sankey-plot" }, "content_sha256": "331a89a6347e731620d10de99de1015a1c2966960296d998d5ea04f7e731e9c6" }, { "id": "sample-size-and-power-planning-assistant", "version": "1.0.0", "display_name": "sample-size-and-power-planning-assistant", "summary": "Plans sample size estimation logic, power assumptions, feasibility checks, and fallback enrollment strategies for clinical and translational study protocols.", "category": "Protocol Design", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "awesome-med-research-skills/Protocol Design/sample-size-and-power-planning-assistant" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 90, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/awesome-med-research-skills/Protocol%20Design/sample-size-and-power-planning-assistant/eval_report_sample-size-and-power-planning-assistant_result.json", "evaluated_on": "2026-04-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 92, "max_score": 100 }, "dynamic_score": { "score": 88.4, "max_score": 100 } }, "release": { "path": "releases/sample-size-and-power-planning-assistant/1.0.0.json", "sha256": "ea0911bc5c9ac215200071cb4d77698028041bea2b8de5423990f3d0604b30d6" }, "artifact": { "path": "shards/630d1b4dab37083d632d924264ae93642b8327521aaa70fbf83de3cc7fd53d61.zip", "sha256": "630d1b4dab37083d632d924264ae93642b8327521aaa70fbf83de3cc7fd53d61", "bytes": 542522, "skill_path": "sample-size-and-power-planning-assistant" }, "content_sha256": "5f705eb3e5e3cf87e21b22d4048c13b8c30b23405ab6645dfc51ff28bcc9b0bc" }, { "id": "sanger-chromatogram-qa", "version": "1.0.0", "display_name": "sanger-chromatogram-qa", "summary": "Use sanger chromatogram qa for data analysis workflows that need structured execution, explicit assumptions, and clear output boundaries.", "category": "Data Analysis", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Data Analysis/sanger-chromatogram-qa" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 86, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Data%20Analysis/sanger-chromatogram-qa/sanger-chromatogram-qa_audit_result_v2.json", "evaluated_on": "2026-03-23", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 83, "max_score": 100 }, "dynamic_score": { "score": 87.2, "max_score": 100 } }, "release": { "path": "releases/sanger-chromatogram-qa/1.0.0.json", "sha256": "bfd4b2e6c154ebc85c08b2376bcfd7dbe210313c725a322bc6c333f94390d9f4" }, "artifact": { "path": "shards/363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb.zip", "sha256": "363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb", "bytes": 1471244, "skill_path": "sanger-chromatogram-qa" }, "content_sha256": "f6a2778f695194e8b1983b6d82b6542d08ce97c89fbbd3f22446dfec29499468" }, { "id": "scanpy", "version": "1.0.0", "display_name": "scanpy", "summary": "Standard single-cell RNA-seq analysis pipeline. Use for QC, normalization, dimensionality reduction (PCA/UMAP/t-SNE), clustering, differential expression, visualization, and converting R-friendly single-cell formats such as Seurat or SingleCellExperiment RDS files into h5ad for Scanpy. Best for exploratory scRNA-seq analysis with established workflows. For deep learning models use scvi-tools; for data format questions use anndata.", "category": "Data Analysis", "source": { "repository": "https://github.com/K-Dense-AI/scientific-agent-skills", "commit": "36d8f13a1e754618794bf42f417884940077b4ae", "path": "skills/scanpy" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "K-Dense Inc." } ], "license": "BSD-3-Clause", "release": { "path": "releases/scanpy/1.0.0.json", "sha256": "6ab206ab6636afa76ef69c6d8c73b2d12ff12af996889904c7cf280a93195808" }, "artifact": { "path": "shards/fbeb5f12afe3c0c6924b0974d44144de230c07662f05cd53d487f267a6a0eb70.zip", "sha256": "fbeb5f12afe3c0c6924b0974d44144de230c07662f05cd53d487f267a6a0eb70", "bytes": 244577, "skill_path": "scanpy" }, "content_sha256": "bc9aee5c5b41bf0d58f861583a1097bdcddce952a61479ba6aa6b3fcb9f52736" }, { "id": "scholar-evaluation", "version": "1.0.0", "display_name": "scholar-evaluation", "summary": "Provide qualitative-first, evidence-traceable developmental review of scholarly works and audit low-stakes research-assessment rubrics with optional local quality controls. Never use for ranking people or consequential decisions.", "category": "Evidence Insight", "source": { "repository": "https://github.com/K-Dense-AI/scientific-agent-skills", "commit": "36d8f13a1e754618794bf42f417884940077b4ae", "path": "skills/scholar-evaluation" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "K-Dense Inc." } ], "license": "MIT", "release": { "path": "releases/scholar-evaluation/1.0.0.json", "sha256": "6acbd327724a1642d068bc171c8ea3ff96ebb828924275db19dad291282c60b9" }, "artifact": { "path": "shards/b55fb597588b663d1d9dfe3695492a0b1a9b88845adbce48fe564656e55759c6.zip", "sha256": "b55fb597588b663d1d9dfe3695492a0b1a9b88845adbce48fe564656e55759c6", "bytes": 624089, "skill_path": "scholar-evaluation" }, "content_sha256": "3219cb340ed6a65b1a111f865f34f477ff20d6d301966abfe14511538aef05c6" }, { "id": "sci-paper-reviewer", "version": "1.0.0", "display_name": "sci-paper-reviewer", "summary": "Simulates a strict SCI peer-review workflow; trigger when a user uploads or pastes a manuscript (PDF/DOC/DOCX/TXT) and requests an innovation score (1–12) plus experimental-logic vulnerability checks and revision suggestions.", "category": "Other", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Other/sci-paper-reviewer" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 87, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Other/sci-paper-reviewer/sci-paper-reviewer_audit_result_v1.json", "evaluated_on": "2026-03-23", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 88, "max_score": 100 }, "dynamic_score": { "score": 86, "max_score": 100 } }, "release": { "path": "releases/sci-paper-reviewer/1.0.0.json", "sha256": "ea7d6b9a74f4cbcb405c9320dd90f2581702825961be2ae7ba206b0423be6b8d" }, "artifact": { "path": "shards/fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0.zip", "sha256": "fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0", "bytes": 1383584, "skill_path": "sci-paper-reviewer" }, "content_sha256": "1ec0708249eabd9973a03c4ebb7614ecea57e46c0701f747b9933cf205d89ceb" }, { "id": "science-popularization-article", "version": "1.0.0", "display_name": "science-popularization-article", "summary": "Generates science popularization articles with titles and outlines based on medical topics and style preferences. Invoke when user needs to create medical/science popular content for public education.", "category": "Other", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Other/science-popularization-article" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 90, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Other/science-popularization-article/science-popularization-article_audit_result_v2.json", "evaluated_on": "2026-03-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 83, "max_score": 100 }, "dynamic_score": { "score": 94.6, "max_score": 100 } }, "release": { "path": "releases/science-popularization-article/1.0.0.json", "sha256": "c8a94173a527578e0881ae81e8268ddb8405d79e6a62d6d301394a4b3eea7a9b" }, "artifact": { "path": "shards/fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0.zip", "sha256": "fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0", "bytes": 1383584, "skill_path": "science-popularization-article" }, "content_sha256": "f6a7ed8a8fc0dcadc6631ae37c505f33cb3804dc44671dd5819953e75afc5061" }, { "id": "scientific-brainstorming", "version": "1.0.0", "display_name": "Scientific Brainstorming", "summary": "Creative research ideation and exploration. Use for open-ended brainstorming sessions, exploring interdisciplinary connections, challenging assumptions, or identifying research gaps. Best for early-stage research planning when you do not have specific observations yet. For formulating testable hypotheses from data use hypothesis-generation.", "category": "Protocol Design", "source": { "repository": "https://github.com/K-Dense-AI/scientific-agent-skills", "commit": "f4b05302fa4e2c440e3853cd094f4ddeb78ffc7f", "path": "scientific-skills/scientific-brainstorming" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "K-Dense Inc." } ], "license": "MIT license", "release": { "path": "releases/scientific-brainstorming/1.0.0.json", "sha256": "4734cc31fb794bba21bd0a4d4dab7bdf6d6f1152ae1122d36ab612f835aadd43" }, "artifact": { "path": "shards/99a60df6cc99b54f308f288ccebccde256dcc3925503b3cde49ceadd90857ec9.zip", "sha256": "99a60df6cc99b54f308f288ccebccde256dcc3925503b3cde49ceadd90857ec9", "bytes": 18851, "skill_path": "scientific-brainstorming" }, "content_sha256": "5613d5a1687a07c4632ef63c156ae4b81649a7a2ef77d365ec7d87b069cdec61" }, { "id": "scientific-critical-thinking", "version": "1.0.0", "display_name": "scientific-critical-thinking", "summary": "Evaluate research rigor. Assess methodology, experimental design, statistical validity, biases, confounding, evidence quality (GRADE, Cochrane ROB), for critical analysis of scientific claims.", "category": "Evidence Insight", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Evidence Insight/scientific-critical-thinking" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 87, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Evidence%20Insight/scientific-critical-thinking/eval_report_scientific-critical-thinking_result.json", "evaluated_on": "2026-06-15", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 87, "max_score": 100 }, "dynamic_score": { "score": 86.6, "max_score": 100 } }, "release": { "path": "releases/scientific-critical-thinking/1.0.0.json", "sha256": "27f8a29d4b09affc35f87addcce1820222bdcc0fc3f9e39ca6d10e9d7806f2df" }, "artifact": { "path": "shards/7c8f9f8124373f70427a2a59443dcbc72f77935f64151065ea3740842f3c00e3.zip", "sha256": "7c8f9f8124373f70427a2a59443dcbc72f77935f64151065ea3740842f3c00e3", "bytes": 53660, "skill_path": "scientific-critical-thinking" }, "content_sha256": "e25dc686183bf489beed85672dc0a90e12045d15a6e69d2684feec9f0eef7ecb" }, { "id": "scientific-podcast-summary", "version": "1.0.0", "display_name": "scientific-podcast-summary", "summary": "Automatically summarize scientific podcasts like Huberman Lab and Nature.", "category": "Evidence Insight", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Evidence Insight/scientific-podcast-summary" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 85, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Evidence%20Insight/scientific-podcast-summary/scientific-podcast-summary_audit_result_v2.json", "evaluated_on": "2026-03-23", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 88, "max_score": 100 }, "dynamic_score": { "score": 83.6, "max_score": 100 } }, "release": { "path": "releases/scientific-podcast-summary/1.0.0.json", "sha256": "2f9ee604ccd4ce9cc7ac355360d5ed83e8fef58ab39cd70db285671864eec955" }, "artifact": { "path": "shards/fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0.zip", "sha256": "fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0", "bytes": 1383584, "skill_path": "scientific-podcast-summary" }, "content_sha256": "b13f49de6d1a3b9af774d4b4af9925b792d7d8d5c405b33d99904c1ff7407e28" }, { "id": "scientific-schematics", "version": "1.0.0", "display_name": "scientific-schematics", "summary": "Automates publication-quality scientific diagrams (e.g., flowcharts, architectures, pathways) when you need journal/poster-ready visuals from a natural-language description.", "category": "Other", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Other/scientific-schematics" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 87, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Other/scientific-schematics/scientific-schematics_audit_result_v1.json", "evaluated_on": "2026-03-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 82, "max_score": 100 }, "dynamic_score": { "score": 89.6, "max_score": 100 } }, "release": { "path": "releases/scientific-schematics/1.0.0.json", "sha256": "d640fc169dd3e4c2a64edeff5443eb4589d10d7f2cc70731854ba6d7e7a08cb9" }, "artifact": { "path": "shards/fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0.zip", "sha256": "fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0", "bytes": 1383584, "skill_path": "scientific-schematics" }, "content_sha256": "545854965dd94ad1f02bc5094f410dd2b2102dacaac1c0223466189b90146ee5" }, { "id": "scikit-bio", "version": "1.0.0", "display_name": "scikit-bio", "summary": "A Python bioinformatics toolkit for sequence, phylogeny, and microbiome/community-ecology analysis; use it when you need to compute diversity/ordination/statistics from biological data and standard formats (FASTA/FASTQ/Newick/BIOM).", "category": "Data Analysis", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Data Analysis/scikit-bio" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 86, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Data%20Analysis/scikit-bio/scikit-bio_audit_result_v1.json", "evaluated_on": "2026-03-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 85, "max_score": 100 }, "dynamic_score": { "score": 86.6, "max_score": 100 } }, "release": { "path": "releases/scikit-bio/1.0.0.json", "sha256": "f9aca0da274527f0ea9994e359a34e0a475df98e4a5d454e581b80d671f1dc94" }, "artifact": { "path": "shards/363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb.zip", "sha256": "363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb", "bytes": 1471244, "skill_path": "scikit-bio" }, "content_sha256": "0a75531ac703bc559d7d2f1e9dc7299104502cc0779f3e6ff6a78f200044cd19" }, { "id": "scikit-survival", "version": "1.0.0", "display_name": "scikit-survival", "summary": "A comprehensive toolkit for survival analysis and time-to-event modeling in Python using scikit-survival; use it when you need to model censored time-to-event outcomes, fit Cox/RSF/GB models or Survival SVMs, evaluate with C-index/Brier score, or handle competing risks.", "category": "Data Analysis", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Data Analysis/scikit-survival" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 86, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Data%20Analysis/scikit-survival/scikit-survival_audit_result_v1.json", "evaluated_on": "2026-03-23", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 84, "max_score": 100 }, "dynamic_score": { "score": 86.6, "max_score": 100 } }, "release": { "path": "releases/scikit-survival/1.0.0.json", "sha256": "024f472615ce4ea2746e6ad44f12c4b9330799e08590f8d9198204bf03e1c80d" }, "artifact": { "path": "shards/363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb.zip", "sha256": "363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb", "bytes": 1471244, "skill_path": "scikit-survival" }, "content_sha256": "957e715f31f380fa0c0efdae971ec5811d989709854c7584716f80da76eac7a0" }, { "id": "scite-database", "version": "1.0.0", "display_name": "scite-database", "summary": "Access Scite.ai Smart Citations to classify how a paper is cited (supporting, contrasting, mentioning) and assess scientific claims; use it when you need to evaluate a paper’s reliability or its acceptance in the literature.", "category": "Evidence Insight", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Evidence Insight/scite-database" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 86, "max_score": 100, "report_url": 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correction (scVI), transfer learning, uncertainty-aware differential expression, or multimodal integration (totalVI/MultiVI).", "category": "Data Analysis", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Data Analysis/scvi-tools" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 86, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Data%20Analysis/scvi-tools/scvi-tools_audit_result_v1.json", "evaluated_on": "2026-03-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 84, "max_score": 100 }, "dynamic_score": { "score": 86.6, "max_score": 100 } }, "release": { "path": "releases/scvi-tools/1.0.0.json", "sha256": "369dd853125981948597e51aa39a3d3f5c9b0bfe57b235cf9f858bf2a2a295d2" }, "artifact": { "path": "shards/363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb.zip", "sha256": "363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb", "bytes": 1471244, "skill_path": "scvi-tools" }, "content_sha256": "ea35c3bac897c7718227787f29d732173ecd951021fc2996c06ab1c5f34dbcdf" }, { "id": "sds-msds-risk-scanner", "version": "1.0.0", "display_name": "sds-msds-risk-scanner", "summary": "Extract hazard codes and safety info from chemical safety datasheets.", "category": "Evidence Insight", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Evidence Insight/sds-msds-risk-scanner" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 85, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Evidence%20Insight/sds-msds-risk-scanner/sds-msds-risk-scanner_audit_result_v2.json", "evaluated_on": "2026-03-23", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 88, "max_score": 100 }, "dynamic_score": { "score": 83.6, "max_score": 100 } }, "release": { "path": "releases/sds-msds-risk-scanner/1.0.0.json", "sha256": "445c2c06713bd9cc1cace137f95d8bc8d2cdec0a0ad90713f81b468d10eec2a3" }, "artifact": { "path": "shards/fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0.zip", "sha256": "fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0", "bytes": 1383584, "skill_path": "sds-msds-risk-scanner" }, "content_sha256": "f03b0d0c421fb1f0e0ecd3017d55e69f6e7311556daa05b8263f39395d7505ec" }, { "id": "seaborn", "version": "1.0.0", "display_name": "seaborn", "summary": "Statistical visualization library integrated with pandas; use it when you need fast EDA of distributions, relationships, and categorical comparisons (e.g., box/violin/pair plots and heatmaps) with strong default aesthetics on top of matplotlib.", "category": "Data Analysis", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Data Analysis/seaborn" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 86, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Data%20Analysis/seaborn/seaborn_audit_result_v2.json", "evaluated_on": "2026-03-23", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 85, "max_score": 100 }, "dynamic_score": { "score": 86.6, "max_score": 100 } }, "release": { "path": "releases/seaborn/1.0.0.json", "sha256": "28a6e677aa21f1cf7e2240c07ab2fd10e414f73512c8f22421fdf4c87b7da720" }, "artifact": { "path": "shards/363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb.zip", "sha256": "363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb", "bytes": 1471244, "skill_path": "seaborn" }, "content_sha256": "9d7170b2db43d1cee9c254db6dd3875f1a34f81a3d749d8d910ad821a36eaef7" }, { "id": "search-pubmed", "version": "1.0.0", "display_name": "search-pubmed", "summary": "An intelligent tool for precision medical literature search using PubMed's E-utilities API.", "category": "Other", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Other/search-pubmed" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 89, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Other/search-pubmed/search-pubmed_audit_result_v2.json", "evaluated_on": "2026-03-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 81, "max_score": 100 }, "dynamic_score": { "score": 93.6, "max_score": 100 } }, "release": { "path": "releases/search-pubmed/1.0.0.json", "sha256": "06beb91fc6ce659e28d98293fbc4f0dfa52ce6a042f74b0472fb9352433957ed" }, "artifact": { "path": "shards/fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0.zip", "sha256": "fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0", "bytes": 1383584, "skill_path": "search-pubmed" }, "content_sha256": "8f147e4e5b2880d05c9c5d2cf90a86425e85ddf3e97189252eb87cd990e25496" }, { "id": "semantic-consistency-auditor", "version": "1.0.1", "display_name": "semantic-consistency-auditor", "summary": "Use semantic consistency auditor for academic writing workflows that need structured execution, explicit assumptions, and clear output boundaries.", "category": "Academic Writing", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Academic Writing/semantic-consistency-auditor" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 85, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Academic%20Writing/semantic-consistency-auditor/semantic-consistency-auditor_audit_result_v2.json", "evaluated_on": "2026-03-23", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 88, "max_score": 100 }, "dynamic_score": { "score": 83.6, "max_score": 100 } }, "release": { "path": "releases/semantic-consistency-auditor/1.0.1.json", "sha256": "a25dbbe9eb341a11f6fea2313912eaf5068009a887b0bf6dae6dd638caf4b39b" }, "artifact": { "path": "shards/2b2d134141e3d5698b9e174f154599abf13d7ddd3c9060e29ef536399fd82407.zip", "sha256": "2b2d134141e3d5698b9e174f154599abf13d7ddd3c9060e29ef536399fd82407", "bytes": 17034738, "skill_path": "semantic-consistency-auditor" }, "content_sha256": "13c54ff2ac13081fa8017995b8f20da4ba50a1b1184c7653502ae9e7404948a4" }, { "id": "semantic-scholar-database", "version": "1.0.0", "display_name": "semantic-scholar-database", "summary": "Access the Semantic Scholar Graph API to search papers and retrieve paper/author/citation data when you need literature discovery or citation graph exploration.", "category": "Evidence Insight", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Evidence Insight/semantic-scholar-database" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 90, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Evidence%20Insight/semantic-scholar-database/semantic-scholar-database_audit_result_v1.json", "evaluated_on": "2026-03-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 87, "max_score": 100 }, "dynamic_score": { "score": 92.6, "max_score": 100 } }, "release": { "path": "releases/semantic-scholar-database/1.0.0.json", "sha256": "80dc6a18e6a8a96031177b563e34194d7c07d39dc13cddd554a9475b2b6e3b03" }, "artifact": { "path": "shards/fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0.zip", "sha256": "fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0", "bytes": 1383584, "skill_path": "semantic-scholar-database" }, "content_sha256": "046cc90e033b39ca363007d54b462bff43c251fc7240af8e98aa505536b2297e" }, { "id": "sequence-alignment", "version": "1.0.0", "display_name": "sequence-alignment", "summary": "A skill for performing sequence alignment using NCBI BLAST API. Supports nucleotide and protein sequence comparison against major biological databases.", "category": "Data Analysis", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Data Analysis/sequence-alignment" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 89, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Data%20Analysis/sequence-alignment/sequence-alignment_audit_result_v2.json", "evaluated_on": "2026-03-23", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 88, "max_score": 100 }, "dynamic_score": { "score": 89.6, "max_score": 100 } }, "release": { "path": "releases/sequence-alignment/1.0.0.json", "sha256": "69b12c6cf6e8c16463c1954460bfe9ee83db5cb0d97cc60452681692c5344f21" }, "artifact": { "path": "shards/363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb.zip", "sha256": "363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb", "bytes": 1471244, "skill_path": "sequence-alignment" }, "content_sha256": "ee6f073e9d92fe64bd2ba087d0596454b21758fbaf5847da0be6b0b8f88f58f3" }, { "id": "shap", "version": "1.0.0", "display_name": "shap", "summary": "Explain and audit machine-learning predictions with SHAP. Use for selecting SHAP explainers and maskers, computing and validating feature attributions, handling multi-output explanations, and producing local or global SHAP visualizations.", "category": "Data Analysis", "source": { "repository": "https://github.com/K-Dense-AI/scientific-agent-skills", "commit": "36d8f13a1e754618794bf42f417884940077b4ae", "path": "skills/shap" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "K-Dense Inc." } ], "license": "MIT", "release": { "path": "releases/shap/1.0.0.json", "sha256": "9294d9680e7d6857bf23b8fe257578bbbe17f4415f64db30df7c659e914ec6a8" }, "artifact": { "path": "shards/b55fb597588b663d1d9dfe3695492a0b1a9b88845adbce48fe564656e55759c6.zip", "sha256": "b55fb597588b663d1d9dfe3695492a0b1a9b88845adbce48fe564656e55759c6", "bytes": 624089, "skill_path": "shap" }, "content_sha256": "7f484af991be3f16faf90195ed5a070e724190a669eff5c87ab07c9bf456071f" }, { "id": "shift-handover-summarizer", "version": "1.0.0", "display_name": "shift-handover-summarizer", "summary": "Generate structured shift handover summaries from EHR records, highlighting critical events, vital sign changes, and pending tasks for incoming clinical staff.", "category": "Other", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Other/shift-handover-summarizer" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 86, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Other/shift-handover-summarizer/shift-handover-summarizer_audit_result_v2.json", "evaluated_on": "2026-03-19", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 87, "max_score": 100 }, "dynamic_score": { "score": 86, "max_score": 100 } }, "release": { "path": "releases/shift-handover-summarizer/1.0.0.json", "sha256": "63224f882da47ebc8a3884955d4627a32aac79c1d0ce68267e9479f2b3516478" }, "artifact": { "path": "shards/fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0.zip", "sha256": "fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0", "bytes": 1383584, "skill_path": "shift-handover-summarizer" }, "content_sha256": "568b74b5bf38105c94e42f71649d1fdb1879da26076c77cba9ba669e402ce9a0" }, { "id": "single-cell-research-planner", "version": "1.0.0", "display_name": "single-cell-research-planner", "summary": "Designs complete single-cell research plans from a user-provided biomedical direction. Always use this skill whenever a user wants to design, scope, or structure a single-cell study — including disease-focused, mechanism-focused, biomarker-focused, translational, perturbation-inspired, or validation-aware projects. It should define the research question, choose the best-fit study pattern, recommend sample grouping logic, suggest reference datasets as examples only, specify the core analysis modules, propose a validation ladder, and output four workload configurations (Lite / Standard / Advanced / Publication+). Never fabricate datasets, sample metadata, accession numbers, cohort availability, cell-type labels, external validation resources, or literature references. Always include the mandatory Dataset Disclaimer immediately before any workflow section that mentions datasets or public resources.", "category": "Protocol Design", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "awesome-med-research-skills/Protocol Design/single-cell-research-planner" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 90, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/awesome-med-research-skills/Protocol%20Design/single-cell-research-planner/eval_report_single-cell-research-planner_result.json", "evaluated_on": "2026-04-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 93, "max_score": 100 }, "dynamic_score": { "score": 87.7, "max_score": 100 } }, "release": { "path": "releases/single-cell-research-planner/1.0.0.json", "sha256": "be168b23b253d924ea2bf8277e68c7e1e91f688c2975ab93def86f626d2d2786" }, "artifact": { "path": "shards/630d1b4dab37083d632d924264ae93642b8327521aaa70fbf83de3cc7fd53d61.zip", "sha256": "630d1b4dab37083d632d924264ae93642b8327521aaa70fbf83de3cc7fd53d61", "bytes": 542522, "skill_path": "single-cell-research-planner" }, "content_sha256": "f565ba0246342fe21dc601f2d22991459b67e1333e609debd45f565ee7155c4a" }, { "id": "single-compound-network-toxicology-disease-link-reference-grounded", "version": "1.0.0", "display_name": "single-compound-network-toxicology-disease-link-reference-grounded", "summary": "Generates complete single-compound network-toxicology research designs from one exposure, one disease or toxic phenotype, and a validation direction. Use when a study centers on one compound–one disease link and needs target collection, overlap construction, enrichment, PPI hub prioritization, docking, optional transcriptomic cross-check, and conservative mechanistic synthesis. Covers five study patterns and always outputs Lite / Standard / Advanced / Publication+ with a recommended primary plan, stepwise workflow, figure plan, validation hierarchy, minimal executable version, publication upgrade path, and strictly verified literature retrieval.", "category": "Protocol Design", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "awesome-med-research-skills/Protocol Design/single-compound-network-toxicology-disease-link-reference-grounded" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 89, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/awesome-med-research-skills/Protocol%20Design/single-compound-network-toxicology-disease-link-reference-grounded/eval_report_single-compound-network-toxicology-disease-link-reference-grounded_result.json", "evaluated_on": "2026-04-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 91, "max_score": 100 }, "dynamic_score": { "score": 87.7, "max_score": 100 } }, "release": { "path": "releases/single-compound-network-toxicology-disease-link-reference-grounded/1.0.0.json", "sha256": "a7c96158cb434419bc3d0f7c398225eae52d715971d43e501ca8dc6675cd2b96" }, "artifact": { "path": "shards/630d1b4dab37083d632d924264ae93642b8327521aaa70fbf83de3cc7fd53d61.zip", "sha256": "630d1b4dab37083d632d924264ae93642b8327521aaa70fbf83de3cc7fd53d61", "bytes": 542522, "skill_path": "single-compound-network-toxicology-disease-link-reference-grounded" }, "content_sha256": "89154a092689a16fb8125ddf91c8b2f661b0d5a9c86527bffc96f1dabb3e7744" }, { "id": "single-drug-adverse-effect-hub-first-network-pharmacology", "version": "1.0.0", "display_name": "single-drug-adverse-effect-hub-first-network-pharmacology", "summary": "Generates complete reference-grounded single-drug adverse-effect network-pharmacology research designs from a user-provided drug, adverse event, and desired evidence depth. Always use this skill when a user wants to design, plan, or upgrade a conventional network-pharmacology study centered on one fixed drug and one fixed adverse-effect endpoint, using drug-target prediction, adverse-event target collection, overlap analysis, PPI hub prioritization, enrichment interpretation, molecular docking, and optional orthogonal transcriptomic or literature validation. Covers five study patterns (canonical hub-first, cardiotoxicity or electrophysiology-oriented, immune-inflammatory adverse effect, organ-toxicity pathway context, translational validation) and always outputs four workload configs (Lite / Standard / Advanced / Publication+) with a recommended primary plan, dependency/evidence map, step-by-step workflow, figure plan, validation strategy, minimal executable version, publication upgrade path, verified-reference pack, and self-critical risk review.", "category": "Protocol Design", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "awesome-med-research-skills/Protocol Design/single-drug-adverse-effect-hub-first-network-pharmacology" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 89, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/awesome-med-research-skills/Protocol%20Design/single-drug-adverse-effect-hub-first-network-pharmacology/eval_report_single-drug-adverse-effect-hub-first-network-pharmacology_result.json", "evaluated_on": "2026-04-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 92, "max_score": 100 }, "dynamic_score": { "score": 87.7, "max_score": 100 } }, "release": { "path": "releases/single-drug-adverse-effect-hub-first-network-pharmacology/1.0.0.json", "sha256": "bae890fc24698591a951acf8cdf0a1c8d63eeeef042351457177aeeb35dab7c6" }, "artifact": { "path": "shards/630d1b4dab37083d632d924264ae93642b8327521aaa70fbf83de3cc7fd53d61.zip", "sha256": "630d1b4dab37083d632d924264ae93642b8327521aaa70fbf83de3cc7fd53d61", "bytes": 542522, "skill_path": "single-drug-adverse-effect-hub-first-network-pharmacology" }, "content_sha256": "e0ae52874ad023d135b25a853e79142c25975b91e56143a613d8d5808c846dde" }, { "id": "single-drug-adverse-effect-pathway-anchored-network-pharmacology", "version": "1.0.0", "display_name": "single-drug-adverse-effect-pathway-anchored-network-pharmacology", "summary": "Generates complete reference-grounded single-drug adverse-effect network-pharmacology research designs from a user-provided drug, adverse event, and desired evidence depth. Always use this skill when a user wants to design, plan, or upgrade a conventional network-pharmacology study centered on one fixed drug and one fixed adverse-effect endpoint, using drug-target prediction, adverse-event target collection, overlap analysis, PPI hub prioritization, enrichment interpretation, molecular docking, and optional orthogonal transcriptomic or literature validation. Covers five study patterns (canonical hub-first, cardiotoxicity or electrophysiology-oriented, immune-inflammatory adverse effect, organ-toxicity pathway context, translational validation) and always outputs four workload configs (Lite / Standard / Advanced / Publication+) with a recommended primary plan, dependency/evidence map, step-by-step workflow, figure plan, validation strategy, minimal executable version, publication upgrade path, verified-reference pack, and self-critical risk review.", "category": "Protocol Design", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "awesome-med-research-skills/Protocol Design/single-drug-adverse-effect-pathway-anchored-network-pharmacology" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 89, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/awesome-med-research-skills/Protocol%20Design/single-drug-adverse-effect-pathway-anchored-network-pharmacology/eval_report_single-drug-adverse-effect-pathway-anchored-network-pharmacology_result.json", "evaluated_on": "2026-04-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 92, "max_score": 100 }, "dynamic_score": { "score": 87.7, "max_score": 100 } }, "release": { "path": "releases/single-drug-adverse-effect-pathway-anchored-network-pharmacology/1.0.0.json", "sha256": "98bfd94356aa861f1a82530ce96e765bcce797de14ba255486c52070b8f53fa5" }, "artifact": { "path": "shards/630d1b4dab37083d632d924264ae93642b8327521aaa70fbf83de3cc7fd53d61.zip", "sha256": "630d1b4dab37083d632d924264ae93642b8327521aaa70fbf83de3cc7fd53d61", "bytes": 542522, "skill_path": "single-drug-adverse-effect-pathway-anchored-network-pharmacology" }, "content_sha256": "9233fcd0a5a9275642418b2e8740746ae0ada30bbcbce3eaf41226b3cba6ab33" }, { "id": "single-drug-faers-safety-profile-research-planner", "version": "1.0.0", "display_name": "single-drug-faers-safety-profile-research-planner", "summary": "Generates complete FAERS pharmacovigilance study designs for one-drug whole-profile safety mapping using signal detection, subgroup analysis, onset/seriousness characterization, and conservative label-gap interpretation.", "category": "Protocol Design", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "awesome-med-research-skills/Protocol Design/single-drug-faers-safety-profile-research-planner" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 89, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/awesome-med-research-skills/Protocol%20Design/single-drug-faers-safety-profile-research-planner/eval_report_single-drug-faers-safety-profile-research-planner_result.json", "evaluated_on": "2026-04-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 91, "max_score": 100 }, "dynamic_score": { "score": 87.7, "max_score": 100 } }, "release": { "path": "releases/single-drug-faers-safety-profile-research-planner/1.0.0.json", "sha256": "764dca58aa7b1f1a46e139db8c2d9be66719d0024f59cd98780d860d1e166a32" }, "artifact": { "path": "shards/630d1b4dab37083d632d924264ae93642b8327521aaa70fbf83de3cc7fd53d61.zip", "sha256": "630d1b4dab37083d632d924264ae93642b8327521aaa70fbf83de3cc7fd53d61", "bytes": 542522, "skill_path": "single-drug-faers-safety-profile-research-planner" }, "content_sha256": "d3b766e8d7085ffa8cd6df3b7ad25b8f6209a13c543ff8b1c2d5e3231aedc4bd" }, { "id": "single-gene-oncology-reference-grounded-research-planner", "version": "1.0.0", "display_name": "single-gene-oncology-reference-grounded-research-planner", "summary": "Generates complete conventional single-gene oncology research designs from a user-provided cancer context, target gene, and validation direction. Use when a study centers on a fixed candidate gene and needs expression, prognosis, clinicopathologic association, functional interpretation, immune context, genomic or epigenetic context, optional drug-response hypotheses, and orthogonal validation. Covers five study patterns and always outputs Lite / Standard / Advanced / Publication+ with a recommended primary plan, stepwise workflow, figure plan, validation hierarchy, minimal executable version, publication upgrade path, and strictly verified literature retrieval.", "category": "Protocol Design", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "awesome-med-research-skills/Protocol Design/single-gene-oncology-reference-grounded-research-planner" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 89, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/awesome-med-research-skills/Protocol%20Design/single-gene-oncology-reference-grounded-research-planner/eval_report_single-gene-oncology-reference-grounded-research-planner_result.json", "evaluated_on": "2026-04-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 92, "max_score": 100 }, "dynamic_score": { "score": 87.7, "max_score": 100 } }, "release": { "path": "releases/single-gene-oncology-reference-grounded-research-planner/1.0.0.json", "sha256": "c3183b3a534305d4f2f5a38b0ca3b5adf67d4d276e18cd64025bb1fe3ac129bc" }, "artifact": { "path": "shards/630d1b4dab37083d632d924264ae93642b8327521aaa70fbf83de3cc7fd53d61.zip", "sha256": "630d1b4dab37083d632d924264ae93642b8327521aaa70fbf83de3cc7fd53d61", "bytes": 542522, "skill_path": "single-gene-oncology-reference-grounded-research-planner" }, "content_sha256": "fa683c12fbb2c41fb5d8f2be30f681a2dc26c2c02aac3f21652f93fcbcf9c90e" }, { "id": "singlecell-portal", "version": "1.0.0", "display_name": "singlecell-portal", "summary": "Programmatically query public single-cell study metadata from the Broad Institute Single Cell Portal REST API when you need to search and filter datasets by organism, tissue, disease, or cell type without an API key.", "category": "Data Analysis", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Data Analysis/singlecell-portal" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 89, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Data%20Analysis/singlecell-portal/singlecell-portal_audit_result_v1.json", "evaluated_on": "2026-03-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 84, "max_score": 100 }, "dynamic_score": { "score": 91.6, "max_score": 100 } }, "release": { "path": "releases/singlecell-portal/1.0.0.json", "sha256": "75b35d5dab65121922e01da47a61cb5ee1acb34021162755720eb438e73c6371" }, "artifact": { "path": "shards/363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb.zip", "sha256": "363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb", "bytes": 1471244, "skill_path": "singlecell-portal" }, "content_sha256": "10f46afcba292924aff2ef418b2e6aa62f4ab861f81421ba325ff951d52be626" }, { "id": "slide-deck-for-lab-meeting", "version": "1.0.1", "display_name": "slide-deck-for-lab-meeting", "summary": "Structures research progress into focused and actionable slides for lab meetings or project reviews without inventing missing content.", "category": "Academic Writing", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "awesome-med-research-skills/Academic Writing/slide-deck-for-lab-meeting" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 91, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/awesome-med-research-skills/Academic%20Writing/slide-deck-for-lab-meeting/eval_report_slide-deck-for-lab-meeting_result.json", "evaluated_on": "2026-04-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 92, "max_score": 100 }, "dynamic_score": { "score": 89.9, "max_score": 100 } }, "release": { "path": "releases/slide-deck-for-lab-meeting/1.0.1.json", "sha256": "685cd21c8052550d57333212c798265bb5b3784e3ff13d5bb0a1e3ca85b6645f" }, "artifact": { "path": "shards/2b2d134141e3d5698b9e174f154599abf13d7ddd3c9060e29ef536399fd82407.zip", "sha256": "2b2d134141e3d5698b9e174f154599abf13d7ddd3c9060e29ef536399fd82407", "bytes": 17034738, "skill_path": "slide-deck-for-lab-meeting" }, "content_sha256": "b3702e6263f792526e95bed508bc75f52dddbda303ba35f67f159610e6607d48" }, { "id": "smart-journal-monitor", "version": "1.0.0", "display_name": "smart-journal-monitor", "summary": "Use smart journal monitor for evidence insight workflows that need structured execution, explicit assumptions, and clear output boundaries.", "category": "Evidence Insight", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Evidence Insight/smart-journal-monitor" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 85, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Evidence%20Insight/smart-journal-monitor/smart-journal-monitor_audit_result_v2.json", "evaluated_on": "2026-03-23", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 88, "max_score": 100 }, "dynamic_score": { "score": 83.6, "max_score": 100 } }, "release": { "path": "releases/smart-journal-monitor/1.0.0.json", "sha256": "ed8238bf28fdce69679569e4eb2df75103ace91073840a4188d70be54e6c24d7" }, "artifact": { "path": "shards/fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0.zip", "sha256": "fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0", "bytes": 1383584, "skill_path": "smart-journal-monitor" }, "content_sha256": "1a153c9fc826f45fcc580cebbafafbf70cec870d857863e5fabf20bb67d28240" }, { "id": "smiles-de-salter", "version": "1.0.1", "display_name": "smiles-de-salter", "summary": "Analyze data with `smiles-de-salter` using a reproducible workflow, explicit validation, and structured outputs for review-ready interpretation.", "category": "Data Analysis", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Data Analysis/smiles-de-salter" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "Unknown", "evaluation": { "kind": "upstream-self-assessment", "score": 86, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Data%20Analysis/smiles-de-salter/smiles-de-salter_audit_result_v2.json", "evaluated_on": "2026-03-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 88, "max_score": 100 }, "dynamic_score": { "score": 84.4, "max_score": 100 } }, "release": { "path": "releases/smiles-de-salter/1.0.1.json", "sha256": "2ba1a633a28433b02ef282d0467e72368a74e6130d608171cf6416f86dddf46c" }, "artifact": { "path": "shards/2b2d134141e3d5698b9e174f154599abf13d7ddd3c9060e29ef536399fd82407.zip", "sha256": "2b2d134141e3d5698b9e174f154599abf13d7ddd3c9060e29ef536399fd82407", "bytes": 17034738, "skill_path": "smiles-de-salter" }, "content_sha256": "47643ae43a6c50e7eba82eadd6b6fbf571f03a2ac29530e82a4c647fa9dc6118" }, { "id": "spreadsheet-ops", "version": "1.0.0", "display_name": "spreadsheet-ops", "summary": "Spreadsheet processing and analysis for CSV/Excel; trigger when users ask to merge/clean tabular data, run statistics, add/edit Excel formulas, apply formatting, generate charts, or force workbook recalculation.", "category": "Other", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Other/spreadsheet-ops" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 90, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Other/spreadsheet-ops/eval_report_spreadsheet-ops_result.json", "evaluated_on": "2026-06-15", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 96, "max_score": 100 }, "dynamic_score": { "score": 86.6, "max_score": 100 } }, "release": { "path": "releases/spreadsheet-ops/1.0.0.json", "sha256": "eb436e02eaf295a2f195bbeca04fb0efcbcc4f41722ce223dfa7e86c3ce26510" }, "artifact": { "path": "shards/fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0.zip", "sha256": "fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0", "bytes": 1383584, "skill_path": "spreadsheet-ops" }, "content_sha256": "9248f7236d03c2c9319282560f1909c266e9a6bc3ac27cf61e4fac1988ad6fff" }, { "id": "ssgsea-immune-infiltration-analysis", "version": "1.0.1", "display_name": "ssgsea-immune-infiltration-analysis", "summary": "Use when estimating immune infiltration from bulk RNA-seq expression matrices with ssGSEA/GSVA, comparing case versus control groups, and generating downstream immune-score visualizations. NOT for single-cell RNA-seq, absolute cell proportion estimation, or clinical decision making.", "category": "Data Analysis", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "awesome-med-research-skills/Data Analysis/ssgsea-immune-infiltration-analysis" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "Unknown", "evaluation": { "kind": "upstream-self-assessment", "score": 95.3, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/awesome-med-research-skills/Data%20Analysis/ssgsea-immune-infiltration-analysis/eval_report_ssgsea-immune-infiltration-analysis_result.json", "evaluated_on": "2026-04-15", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 94, "max_score": 100 }, "dynamic_score": { "score": 96.2, "max_score": 100 } }, "release": { "path": "releases/ssgsea-immune-infiltration-analysis/1.0.1.json", "sha256": "01c586ab89822224fdc7654875aec45f8b71cad76fd611cb8460baafc467b08b" }, "artifact": { "path": "shards/2b2d134141e3d5698b9e174f154599abf13d7ddd3c9060e29ef536399fd82407.zip", "sha256": "2b2d134141e3d5698b9e174f154599abf13d7ddd3c9060e29ef536399fd82407", "bytes": 17034738, "skill_path": "ssgsea-immune-infiltration-analysis" }, "content_sha256": "2a4732b505974cf9c25311efa848faf78bda7c97a92b31739bcdb643b4b19b1d" }, { "id": "statistical-analysis", "version": "1.0.0", "display_name": "statistical-analysis", "summary": "Guided statistical analysis for research data - test selection, assumption checking, effect sizes, power analysis, Bayesian alternatives, and APA-formatted reporting. Use whenever a user wants to compare groups, test a hypothesis, analyze experimental or survey data, check statistical assumptions, compute required sample sizes, or write up results - even if they never name a specific test. Covers t-tests, ANOVA, chi-square, correlation, regression, non-parametric and Bayesian methods. For low-level model APIs, see the statsmodels and pymc skills.", "category": "Data Analysis", "source": { "repository": "https://github.com/K-Dense-AI/scientific-agent-skills", "commit": "36d8f13a1e754618794bf42f417884940077b4ae", "path": "skills/statistical-analysis" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "K-Dense Inc." } ], "license": "MIT license", "release": { "path": "releases/statistical-analysis/1.0.0.json", "sha256": "e19c22af374891f6c5d7b5d2c86991562f5bf7d24c8a15b1b2c0cf04c51938ca" }, "artifact": { "path": "shards/b55fb597588b663d1d9dfe3695492a0b1a9b88845adbce48fe564656e55759c6.zip", "sha256": "b55fb597588b663d1d9dfe3695492a0b1a9b88845adbce48fe564656e55759c6", "bytes": 624089, "skill_path": "statistical-analysis" }, "content_sha256": "c521f0f71aec60a8a68e994bbf7441b6ffd30316df5ce54b2aa10c218ee85ea8" }, { "id": "statistical-analysis-advisor", "version": "1.0.0", "display_name": "statistical-analysis-advisor", "summary": "Recommends appropriate statistical methods (T-test vs ANOVA, etc.) based.", "category": "Data Analysis", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Data Analysis/statistical-analysis-advisor" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 89, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Data%20Analysis/statistical-analysis-advisor/statistical-analysis-advisor_audit_result_v2.json", "evaluated_on": "2026-03-23", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 88, "max_score": 100 }, "dynamic_score": { "score": 89.6, "max_score": 100 } }, "release": { "path": "releases/statistical-analysis-advisor/1.0.0.json", "sha256": "adcd78d71f556313dac22c6ce2c42efbc1951f776e374ca84abea1bef1ddafea" }, "artifact": { "path": "shards/363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb.zip", "sha256": "363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb", "bytes": 1471244, "skill_path": "statistical-analysis-advisor" }, "content_sha256": "79adf25c54b20a907c45bf1787bd48b0f67c070cecdfcae5da74ff0900604a64" }, { "id": "string-database", "version": "1.0.1", "display_name": "string-database", "summary": "Query the STRING database for protein-protein interactions (PPIs), functional enrichment, and homology. Use when the user asks about interactions between specific proteins, interaction evidence, confidence scores, protein interaction partners, or pathway enrichments.\n", "category": "Evidence Insight", "source": { "repository": "https://github.com/google-deepmind/science-skills", "commit": "0b42509800f49e6eb7809505d96e20a890ef99bd", "path": "skills/string_database" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "license": "Unknown", "release": { "path": "releases/string-database/1.0.1.json", "sha256": "23fe8395a4c21e3bff3d3887525826f49e9f49db763bb0afa6736bc181b0cae6" }, "artifact": { "path": "shards/2b2d134141e3d5698b9e174f154599abf13d7ddd3c9060e29ef536399fd82407.zip", "sha256": "2b2d134141e3d5698b9e174f154599abf13d7ddd3c9060e29ef536399fd82407", "bytes": 17034738, "skill_path": "string-database" }, "content_sha256": "631abc2889807bbdac4e3cb58937aae43f271c104b656717fe4dc6441dc38548" }, { "id": "study-design-identifier", "version": "1.0.0", "display_name": "study-design-identifier", "summary": "Identifies the real underlying study design used in a medical or biomedical paper, distinguishes primary and secondary design components when papers are hybrid, and converts the paper into an evidence-aware design label suitable for literature appraisal, evidence grading, and downstream review workflows. Always identify the actual design from what the study did, not from how the authors describe it. Never fabricate references, metadata, or study features.", "category": "Evidence Insight", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "awesome-med-research-skills/Evidence Insight/study-design-identifier" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 86, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/awesome-med-research-skills/Evidence%20Insight/study-design-identifier/eval_report_study-design-identifier_result.json", "evaluated_on": "2026-04-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 89, "max_score": 100 }, "dynamic_score": { "score": 83.3, "max_score": 100 } }, "release": { "path": "releases/study-design-identifier/1.0.0.json", "sha256": "8c6a13aca9f4715a1c2b9755364fb3415d37287f6429e068899796eac3274116" }, "artifact": { "path": "shards/fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0.zip", "sha256": "fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0", "bytes": 1383584, "skill_path": "study-design-identifier" }, "content_sha256": "125ba1292bf81835ab8878967032a347daf59c9769c3bb75fa02159320f9f68c" }, { "id": "study-design-scale-selector", "version": "1.0.0", "display_name": "study-design-scale-selector", "summary": "Determines the appropriate Risk of Bias assessment scale for a medical study based on its design (RCT, Cohort, etc.), using PubMed metadata lookup or text analysis. Use when the user wants to know which quality assessment tool to use for a specific paper (given PMID or abstract).", "category": "Data Analysis", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Data Analysis/study-design-scale-selector" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 87, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Data%20Analysis/study-design-scale-selector/study-design-scale-selector_audit_result_v2.json", "evaluated_on": "2026-03-23", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 88, "max_score": 100 }, "dynamic_score": { "score": 86.2, "max_score": 100 } }, "release": { "path": "releases/study-design-scale-selector/1.0.0.json", "sha256": "aaf7f3e5a37d0dd36dcf35bd22387bf2aec36115b9f3d61b588488d1ed51dffc" }, "artifact": { "path": "shards/363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb.zip", "sha256": "363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb", "bytes": 1471244, "skill_path": "study-design-scale-selector" }, "content_sha256": "85e7616c2abc8461f0491d867c8ba13e0d2a63d0086a7a97c0356aed0df52fd4" }, { "id": "study-limitations-drafter", "version": "1.0.1", "display_name": "study-limitations-drafter", "summary": "Use study limitations drafter for academic writing workflows that need structured execution, explicit assumptions, and clear output boundaries.", "category": "Academic Writing", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Academic Writing/study-limitations-drafter" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 91, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Academic%20Writing/study-limitations-drafter/eval_report_study-limitations-drafter_result.json", "evaluated_on": "2026-06-15", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 98, "max_score": 100 }, "dynamic_score": { "score": 86.6, "max_score": 100 } }, "release": { "path": "releases/study-limitations-drafter/1.0.1.json", "sha256": "59a29f03ae156d44ae3d6b0f3233cad343a68bd96b75e92cb7a256364430d3ed" }, "artifact": { "path": "shards/2b2d134141e3d5698b9e174f154599abf13d7ddd3c9060e29ef536399fd82407.zip", "sha256": "2b2d134141e3d5698b9e174f154599abf13d7ddd3c9060e29ef536399fd82407", "bytes": 17034738, "skill_path": "study-limitations-drafter" }, "content_sha256": "30726dde5c476315f89915d81b711c799b86cad8b6bd2155e29213a00c9b73c0" }, { "id": "study-objective-refiner", "version": "1.0.0", "display_name": "study-objective-refiner", "summary": "Refines broad, vague, or aspirational biomedical research objectives into clear, bounded, measurable, executable, and downstream-ready study objective statements. Always use this skill when a user has a general aim such as “explore a mechanism,” “study prognosis,” “investigate biomarkers,” or “look at treatment response,” but the objective is still too broad, non-operational, or too ambiguous to support protocol framing, design selection, analysis planning, or hypothesis design. Never assume that polished wording alone means the objective is actionable. Focus first on objective type, missing operational elements, scope discipline, and downstream-ready formulation.", "category": "Protocol Design", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "awesome-med-research-skills/Protocol Design/study-objective-refiner" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 90, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/awesome-med-research-skills/Protocol%20Design/study-objective-refiner/eval_report_study-objective-refiner_result.json", "evaluated_on": "2026-04-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 92, "max_score": 100 }, "dynamic_score": { "score": 88.4, "max_score": 100 } }, "release": { "path": "releases/study-objective-refiner/1.0.0.json", "sha256": "773a944de4a864851fedde382e162bf9d2fdb265feef3ddc9d00ce23cc18c13e" }, "artifact": { "path": "shards/630d1b4dab37083d632d924264ae93642b8327521aaa70fbf83de3cc7fd53d61.zip", "sha256": "630d1b4dab37083d632d924264ae93642b8327521aaa70fbf83de3cc7fd53d61", "bytes": 542522, "skill_path": "study-objective-refiner" }, "content_sha256": "e5ffeac341a55c48bc6a2d255b65b6556e0a0ae7c477176b0b92099cac2ac242" }, { "id": "style-journal-rewrite", "version": "1.0.1", "display_name": "style-journal-rewrite", "summary": "Rewrite drafts according to target writing style or target journal format. Triggers when user says \"rewrite in XX style\", \"match this tone\", \"format for XX journal\", \"rewrite following this format\". Input can be .docx / .md / .txt or conversation text.", "category": "Academic Writing", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Academic Writing/style-journal-rewrite" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 89, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Academic%20Writing/style-journal-rewrite/eval_report_style-journal-rewrite_result.json", "evaluated_on": "2026-06-15", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 92, "max_score": 100 }, "dynamic_score": { "score": 86.6, "max_score": 100 } }, "release": { "path": "releases/style-journal-rewrite/1.0.1.json", "sha256": "69a5762db1f4d587e8e90adb0b47142a23acf4c34d481e679c6a52168b99f3de" }, "artifact": { "path": "shards/2b2d134141e3d5698b9e174f154599abf13d7ddd3c9060e29ef536399fd82407.zip", "sha256": "2b2d134141e3d5698b9e174f154599abf13d7ddd3c9060e29ef536399fd82407", "bytes": 17034738, "skill_path": "style-journal-rewrite" }, "content_sha256": "01c5cb0753be17a72ec3685bfbf0b06a2ddff081b06e4ac4cb8e2fb430879c91" }, { "id": "survival-curve-risk-table", "version": "1.0.1", "display_name": "survival-curve-risk-table", "summary": "Analyze data with `survival-curve-risk-table` using a reproducible workflow, explicit validation, and structured outputs for review-ready interpretation.", "category": "Data Analysis", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Data Analysis/survival-curve-risk-table" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "Unknown", "evaluation": { "kind": "upstream-self-assessment", "score": 88, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Data%20Analysis/survival-curve-risk-table/survival-curve-risk-table_audit_result_v2.json", "evaluated_on": "2026-03-23", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 86, "max_score": 100 }, "dynamic_score": { "score": 89, "max_score": 100 } }, "release": { "path": "releases/survival-curve-risk-table/1.0.1.json", "sha256": "5289a193a31400b898436ab4bb16e04f82014cd75035de747522b49f49ccbbf4" }, "artifact": { "path": "shards/2b2d134141e3d5698b9e174f154599abf13d7ddd3c9060e29ef536399fd82407.zip", "sha256": "2b2d134141e3d5698b9e174f154599abf13d7ddd3c9060e29ef536399fd82407", "bytes": 17034738, "skill_path": "survival-curve-risk-table" }, "content_sha256": "27f62d364fe6091c1382b0764311668829203b09443d45c481edd75f4e7a71fe" }, { "id": "svm-model-importance-analysis", "version": "1.0.1", "display_name": "svm-model-importance-analysis", "summary": "Use when you need a standardized R CLI workflow to run two-class SVM-RFE feature ranking on an expression-like matrix, choose an informative feature count from cross-validated error, and generate reproducible ranking and error plots. NOT for regression, multi-class classification, missing-value imputation, or remote data fetching.", "category": "Data Analysis", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "awesome-med-research-skills/Data Analysis/svm-model-importance-analysis" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "Unknown", "evaluation": { "kind": "upstream-self-assessment", "score": 93, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/awesome-med-research-skills/Data%20Analysis/svm-model-importance-analysis/eval_report_svm-model-importance-analysis_result.json", "evaluated_on": "2026-04-20", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 94, "max_score": 100 }, "dynamic_score": { "score": 92.2, "max_score": 100 } }, "release": { "path": "releases/svm-model-importance-analysis/1.0.1.json", "sha256": "c882443d8dd1fc15827346b9adb45b7adcf3d5a4bf4effccf708b30a6b15c18d" }, "artifact": { "path": "shards/2b2d134141e3d5698b9e174f154599abf13d7ddd3c9060e29ef536399fd82407.zip", "sha256": "2b2d134141e3d5698b9e174f154599abf13d7ddd3c9060e29ef536399fd82407", "bytes": 17034738, "skill_path": "svm-model-importance-analysis" }, "content_sha256": "e2d41a9e122cecb9eb09d3533714e9807b71f8d67bab86fae10f5f8bf1eff2b9" }, { "id": "systematic-review", "version": "1.0.1", "display_name": "systematic-review", "summary": "Orchestrates a systematic review and meta-analysis workflow following PRISMA 2020 guidelines, from protocol development through multi-database search, screening, data extraction, and evidence synthesis. Use when conducting evidence-based reviews, meta-analyses, or scoping revi...", "category": "Academic Writing", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Academic Writing/systematic-review" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 86, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Academic%20Writing/systematic-review/eval_report_systematic-review_result.json", "evaluated_on": "2026-06-15", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 86, "max_score": 100 }, "dynamic_score": { "score": 86.2, "max_score": 100 } }, "release": { "path": "releases/systematic-review/1.0.1.json", "sha256": "765fab5282230c2fb7fc4e24f3c064a663dd43c948e88da6a4bd7bb902ba17cd" }, "artifact": { "path": "shards/2b2d134141e3d5698b9e174f154599abf13d7ddd3c9060e29ef536399fd82407.zip", "sha256": "2b2d134141e3d5698b9e174f154599abf13d7ddd3c9060e29ef536399fd82407", "bytes": 17034738, "skill_path": "systematic-review" }, "content_sha256": "32acb046e74dda1d763f812b45242d6d3022f74b02fd64a7fd015d6acbb8d8a8" }, { "id": "systematic-review-screener", "version": "1.0.0", "display_name": "systematic-review-screener", "summary": "Automated abstract screening tool for systematic literature reviews with PRISMA workflow support.", "category": "Evidence Insight", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Evidence Insight/systematic-review-screener" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 90, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Evidence%20Insight/systematic-review-screener/eval_report_systematic-review-screener_result.json", "evaluated_on": "2026-06-15", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 96, "max_score": 100 }, "dynamic_score": { "score": 86.6, "max_score": 100 } }, "release": { "path": "releases/systematic-review-screener/1.0.0.json", "sha256": "c4c2d6826cfa5123541c354f02735a028d941ebfc49b44e4420501b64c77b6be" }, "artifact": { "path": "shards/1ea735cead62d521282de0bf8a672a95515d73332ea4fa7d5d2ccf72eed45995.zip", "sha256": "1ea735cead62d521282de0bf8a672a95515d73332ea4fa7d5d2ccf72eed45995", "bytes": 342914, "skill_path": "systematic-review-screener" }, "content_sha256": "d71eb162a52ef08b89bfe427bf9160863dcb24e9c114f897ef1f3a81bfd154b8" }, { "id": "table-1-generator", "version": "1.0.0", "display_name": "table-1-generator", "summary": "Automated generation of baseline characteristics tables (Table 1) for clinical research papers.", "category": "Data Analysis", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Data Analysis/table-1-generator" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 90, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Data%20Analysis/table-1-generator/eval_report_table-1-generator_result.json", "evaluated_on": "2026-06-15", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 95, "max_score": 100 }, "dynamic_score": { "score": 86.6, "max_score": 100 } }, "release": { "path": "releases/table-1-generator/1.0.0.json", "sha256": "b1efe585cef84bbb7b9f5381fe6b7d06766f4e28fa0723a4beffc2a6213e1343" }, "artifact": { "path": "shards/363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb.zip", "sha256": "363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb", "bytes": 1471244, "skill_path": "table-1-generator" }, "content_sha256": "de34bfca9195375b4c3b4dac0d520ff3397cff774f10dd8fc34c252cb629cb63" }, { "id": "table-1-generator-advanced", "version": "1.0.0", "display_name": "table-1-generator-advanced", "summary": "Generate publication-ready baseline characteristics tables (Table 1) for clinical research papers with automatic variable type detection, appropriate statistics (mean±SD, median[IQR, n(%)), group comparisons (t-test, chi-square), and APA formatting.", "category": "Data Analysis", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Data Analysis/table-1-generator-advanced" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 90, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Data%20Analysis/table-1-generator-advanced/eval_report_table-1-generator-advanced_result.json", "evaluated_on": "2026-06-15", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 94, "max_score": 100 }, "dynamic_score": { "score": 86.6, "max_score": 100 } }, "release": { "path": "releases/table-1-generator-advanced/1.0.0.json", "sha256": "6f100420c88994eaaf39e879a927c89f54546d9e7ae50b0200ddfd730a985ea5" }, "artifact": { "path": "shards/363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb.zip", "sha256": "363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb", "bytes": 1471244, "skill_path": "table-1-generator-advanced" }, "content_sha256": "f72c81add409a4959771c9facf95b0c7c26e2ac8299876386db7b7f7e8dde581" }, { "id": "table-narrative-writer", "version": "1.0.1", "display_name": "table-narrative-writer", "summary": "Converts biomedical table content into clear manuscript or presentation narrative by prioritizing meaningful patterns, contrasts, and interpretation boundaries rather than restating every number.", "category": "Academic Writing", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "awesome-med-research-skills/Academic Writing/table-narrative-writer" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 91, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/awesome-med-research-skills/Academic%20Writing/table-narrative-writer/eval_report_table-narrative-writer_result.json", "evaluated_on": "2026-04-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 93, "max_score": 100 }, "dynamic_score": { "score": 90.3, "max_score": 100 } }, "release": { "path": "releases/table-narrative-writer/1.0.1.json", "sha256": "7f8216ea367ca9c2a711eab0a3b963215ff5140d743927db4e0a7d7e6189da3b" }, "artifact": { "path": "shards/2b2d134141e3d5698b9e174f154599abf13d7ddd3c9060e29ef536399fd82407.zip", "sha256": "2b2d134141e3d5698b9e174f154599abf13d7ddd3c9060e29ef536399fd82407", "bytes": 17034738, "skill_path": "table-narrative-writer" }, "content_sha256": "e887ce3f607b9bca794015392bb5244ecad0a528b09f0b7035ab160c290f97d6" }, { "id": "target-journal-matcher", "version": "1.0.1", "display_name": "target-journal-matcher", "summary": "Matches your study to appropriate journals based on topic, design, and evidence strength. Use when deciding where to submit a manuscript, comparing journal options by impact factor vs scope fit vs method tolerance, or finding a realistic submission target after a rejection. Also triggers on \"where should I submit this paper\", \"which journal is best for my study\", \"find journals for my manuscript\", \"is this a good fit for [journal]\", or \"I need a journal with IF around X\".", "category": "Academic Writing", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "awesome-med-research-skills/Academic Writing/target-journal-matcher" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "Unknown", "evaluation": { "kind": "upstream-self-assessment", "score": 86, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/awesome-med-research-skills/Academic%20Writing/target-journal-matcher/eval_report_target-journal-matcher_result.json", "evaluated_on": "2026-04-23", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 84, "max_score": 100 }, "dynamic_score": { "score": 88.2, "max_score": 100 } }, "release": { "path": "releases/target-journal-matcher/1.0.1.json", "sha256": "5d9ad1bac0879a12ab1ca3d2c29df77fbc6d2dfaee01ce4045c044c0dc24a3c3" }, "artifact": { "path": "shards/2b2d134141e3d5698b9e174f154599abf13d7ddd3c9060e29ef536399fd82407.zip", "sha256": "2b2d134141e3d5698b9e174f154599abf13d7ddd3c9060e29ef536399fd82407", "bytes": 17034738, "skill_path": "target-journal-matcher" }, "content_sha256": "e3ecf5a48e6e58b207f17bc46e6c4081d1227479ed33b2dacbed0b07c5b21736" }, { "id": "text-to-technical-roadmap", "version": "1.0.0", "display_name": "text-to-technical-roadmap", "summary": "Converts research text into a Mermaid technical roadmap flowchart. Use when the user provides research proposals, experiment designs, or scientific text and asks for a roadmap or flowchart.", "category": "Other", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Other/text-to-technical-roadmap" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 85, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Other/text-to-technical-roadmap/text-to-technical-roadmap_audit_result_v2.json", "evaluated_on": "2026-03-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 84, "max_score": 100 }, "dynamic_score": { "score": 85.6, "max_score": 100 } }, "release": { "path": "releases/text-to-technical-roadmap/1.0.0.json", "sha256": "e298db6c95fb00d84cf1f11cdf3cd4fd68aedfe01a2f493ae6b0c6f548bb0f67" }, "artifact": { "path": "shards/fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0.zip", "sha256": "fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0", "bytes": 1383584, "skill_path": "text-to-technical-roadmap" }, "content_sha256": "55b051402a686427db50e563c583c6e23c9f33df1478cd61ace1cc2d5beef3e0" }, { "id": "tf-target-gene-regulatory-network", "version": "1.0.1", "display_name": "tf-target-gene-regulatory-network", "summary": "Use when analyzing transcription factor (TF) regulatory networks using Dorothea database. Input gene list, identify regulating transcription factors, generate TF-Target network visualization. For: transcription factor enrichment analysis, gene regulatory network research.", "category": "Data Analysis", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "awesome-med-research-skills/Data Analysis/tf-target-gene-regulatory-network" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "license": "Unknown", "evaluation": { "kind": "upstream-self-assessment", "score": 94, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/awesome-med-research-skills/Data%20Analysis/tf-target-gene-regulatory-network/eval_report_tf-target-gene-regulatory-network_result.json", "evaluated_on": "2026-04-27", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 96, "max_score": 100 }, "dynamic_score": { "score": 92.4, "max_score": 100 } }, "release": { "path": "releases/tf-target-gene-regulatory-network/1.0.1.json", "sha256": "ce86ade2f6a4df8d6d45aa8010832c209700a4bddc7a60dad00c36a723eb290f" }, "artifact": { "path": "shards/2b2d134141e3d5698b9e174f154599abf13d7ddd3c9060e29ef536399fd82407.zip", "sha256": "2b2d134141e3d5698b9e174f154599abf13d7ddd3c9060e29ef536399fd82407", "bytes": 17034738, "skill_path": "tf-target-gene-regulatory-network" }, "content_sha256": "86833de5fc41439bd9cdf82e59c116e02ad0fcfc0c434db140f18aa11dadd3a2" }, { "id": "time-dependent-roc", "version": "1.0.1", "display_name": "time-dependent-roc", "summary": "Use when performing time-dependent ROC curve analysis for survival data with follow-up time, event status, and a numeric marker. Supports CSV/TXT/TSV/Excel input, `risk_score` as the default marker unless `--marker_col` is provided, parameter validation, standardized output directories, AUC table export, ROC point export, and PDF figure generation.", "category": "Data Analysis", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "awesome-med-research-skills/Data Analysis/time-dependent-roc" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "Unknown", "evaluation": { "kind": "upstream-self-assessment", "score": 87, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/awesome-med-research-skills/Data%20Analysis/time-dependent-roc/eval_report_time-dependent-roc_result.json", "evaluated_on": "2026-04-16", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 88, "max_score": 100 }, "dynamic_score": { "score": 86, "max_score": 100 } }, "release": { "path": "releases/time-dependent-roc/1.0.1.json", "sha256": "a5b0e21e5d258cf1b118a563d4cba8d58e765eee32a1e2b02f6e81a29bc319c7" }, "artifact": { "path": "shards/2b2d134141e3d5698b9e174f154599abf13d7ddd3c9060e29ef536399fd82407.zip", "sha256": "2b2d134141e3d5698b9e174f154599abf13d7ddd3c9060e29ef536399fd82407", "bytes": 17034738, "skill_path": "time-dependent-roc" }, "content_sha256": "23ec05a59a370dc00bb57f0cb56f2e1026aff4672811b7bb1f7e23657d0e81a7" }, { "id": "title-and-abstract-optimizer", "version": "1.0.1", "display_name": "title-and-abstract-optimizer", "summary": "Optimizes manuscript titles and abstracts for information density, factual accuracy, and submission fit in biomedical research writing.", "category": "Academic Writing", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "awesome-med-research-skills/Academic Writing/title-and-abstract-optimizer" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 85, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/awesome-med-research-skills/Academic%20Writing/title-and-abstract-optimizer/eval_report_title-and-abstract-optimizer_result.json", "evaluated_on": "2026-04-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 86, "max_score": 100 }, "dynamic_score": { "score": 83.7, "max_score": 100 } }, "release": { "path": "releases/title-and-abstract-optimizer/1.0.1.json", "sha256": "46666003c7ad792e22b61de8330da8fb61392fa8da0d3c368122337db71d66ca" }, "artifact": { "path": "shards/2b2d134141e3d5698b9e174f154599abf13d7ddd3c9060e29ef536399fd82407.zip", "sha256": "2b2d134141e3d5698b9e174f154599abf13d7ddd3c9060e29ef536399fd82407", "bytes": 17034738, "skill_path": "title-and-abstract-optimizer" }, "content_sha256": "51cc61543a6fc3035493e492c01dc99d0265b0623092ed9921bcd010034706f6" }, { "id": "tooluniverse-clinical-trial-design", "version": "1.0.1", "display_name": "tooluniverse-clinical-trial-design", "summary": "Strategic clinical trial design feasibility assessment using ToolUniverse. Evaluates patient population sizing, biomarker prevalence, endpoint selection, comparator analysis, safety monitoring, and regulatory pathways. Creates comprehensive feasibility reports with evidence gr...", "category": "Protocol Design", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Protocol Design/tooluniverse-clinical-trial-design" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "Unknown", "evaluation": { "kind": "upstream-self-assessment", "score": 89, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Protocol%20Design/tooluniverse-clinical-trial-design/eval_report_tooluniverse-clinical-trial-design_result.json", "evaluated_on": "2026-06-15", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 92, "max_score": 100 }, "dynamic_score": { "score": 86.6, "max_score": 100 } }, "release": { "path": "releases/tooluniverse-clinical-trial-design/1.0.1.json", "sha256": "17d73fb82bad4454e5c7b282c3f7aed6918eec8b03b93fdd9540cc6693e3579a" }, "artifact": { "path": "shards/2b2d134141e3d5698b9e174f154599abf13d7ddd3c9060e29ef536399fd82407.zip", "sha256": "2b2d134141e3d5698b9e174f154599abf13d7ddd3c9060e29ef536399fd82407", "bytes": 17034738, "skill_path": "tooluniverse-clinical-trial-design" }, "content_sha256": "ca491ed6bf960a200eebcda84e15f37dd0af113edd6f17712e6a9e7f52010e2b" }, { "id": "tooluniverse-clinical-trial-matching", "version": "1.0.0", "display_name": "tooluniverse-clinical-trial-matching", "summary": "AI-driven patient-to-trial matching for precision medicine and oncology. Given a patient profile (disease, molecular alterations, stage, prior treatments), discovers and ranks clinical trials from ClinicalTrials.gov using multi-dimensional matching across molecular eligibility...", "category": "Evidence Insight", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Evidence Insight/tooluniverse-clinical-trial-matching" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 88, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Evidence%20Insight/tooluniverse-clinical-trial-matching/eval_report_tooluniverse-clinical-trial-matching_result.json", "evaluated_on": "2026-06-15", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 90, "max_score": 100 }, "dynamic_score": { "score": 86.6, "max_score": 100 } }, "release": { "path": "releases/tooluniverse-clinical-trial-matching/1.0.0.json", "sha256": "2bbcfb72cc80f00760ab21d8aca0a0da5395bf2a5d0a65bb9dac174adce2fc52" }, "artifact": { "path": "shards/fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0.zip", "sha256": "fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0", "bytes": 1383584, "skill_path": "tooluniverse-clinical-trial-matching" }, "content_sha256": "ed85f63d926a2d289ad3504474a11e15504fcb9bc833db448a860dcae71ffb97" }, { "id": "tooluniverse-literature-deep-research", "version": "1.0.0", "display_name": "tooluniverse-literature-deep-research", "summary": "Conduct comprehensive literature research with target disambiguation, evidence grading, and structured theme extraction. Creates a detailed report with mandatory completeness checklist, biological model synthesis, and testable hypotheses. For biological targets, resolves offic...", "category": "Evidence Insight", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Evidence Insight/tooluniverse-literature-deep-research" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 87, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Evidence%20Insight/tooluniverse-literature-deep-research/eval_report_tooluniverse-literature-deep-research_result.json", "evaluated_on": "2026-06-15", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 88, "max_score": 100 }, "dynamic_score": { "score": 86.6, "max_score": 100 } }, "release": { "path": "releases/tooluniverse-literature-deep-research/1.0.0.json", "sha256": "c41f05968295d48149a3ece10534292bcb3a1500010a43528a037f3ab976071e" }, "artifact": { "path": "shards/fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0.zip", "sha256": "fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0", "bytes": 1383584, "skill_path": "tooluniverse-literature-deep-research" }, "content_sha256": "8e4c972ee239ec4bbcee8ca62c67afd65b0d82cd9b48e3aa627a51d471f39d93" }, { "id": "topic-evidence-mapper", "version": "1.0.0", "display_name": "topic-evidence-mapper", "summary": "Rapidly maps the evidence landscape around a medical topic by organizing major research streams, target populations, endpoints, methods, evidence density, and thin areas. Use this skill BEFORE medical-research-gap-finder — it provides the structured landscape that makes formal gap analysis more rigorous. Do not use for formal gap identification, study design, or protocol planning directly.", "category": "Evidence Insight", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "awesome-med-research-skills/Evidence Insight/topic-evidence-mapper" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 84, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/awesome-med-research-skills/Evidence%20Insight/topic-evidence-mapper/eval_report_topic-evidence-mapper_result.json", "evaluated_on": "2026-04-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 86, "max_score": 100 }, "dynamic_score": { "score": 82.4, "max_score": 100 } }, "release": { "path": "releases/topic-evidence-mapper/1.0.0.json", "sha256": "8f0612fbb52028b49e2db613a7bcb160b1a8ed126b07e79089ecaad0fa5f4be9" }, "artifact": { "path": "shards/fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0.zip", "sha256": "fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0", "bytes": 1383584, "skill_path": "topic-evidence-mapper" }, "content_sha256": "e95d658f39d7945adfbb27e279d76c6c73aeffc38cdfe988d8cba15d0cd3fe8c" }, { "id": "torchdrug-english", "version": "1.0.0", "display_name": "torchdrug-english", "summary": "PyTorch-native Graph Neural Network framework for molecules and proteins. Suitable for building custom GNN architectures for drug discovery, protein modeling, or knowledge graph reasoning. Best for custom model development, protein property prediction, and retrosynthesis. If you need pretrained models and diverse feature extractors, use deepchem; if you need benchmark datasets, use pytdc.", "category": "Data Analysis", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Data Analysis/TorchDrug-English" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "release": { "path": "releases/torchdrug-english/1.0.0.json", "sha256": "20c2b1e1582c9c29ad17348a1f56e0915c83b65dfa2336b3d09417d0695cafaa" }, "artifact": { "path": "shards/363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb.zip", "sha256": "363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb", "bytes": 1471244, "skill_path": "torchdrug-english" }, "content_sha256": "8c23158d65cd5367322373347b868841a437d6cce46277ef3d9a7b8fe370186f" }, { "id": "toxicity-structure-alert", "version": "1.0.0", "display_name": "toxicity-structure-alert", "summary": "Analyze data with `toxicity-structure-alert` using a reproducible workflow, explicit validation, and structured outputs for review-ready interpretation.", "category": "Data Analysis", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Data Analysis/toxicity-structure-alert" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 86, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Data%20Analysis/toxicity-structure-alert/toxicity-structure-alert_audit_result_v2.json", "evaluated_on": "2026-03-23", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 88, "max_score": 100 }, "dynamic_score": { "score": 84.4, "max_score": 100 } }, "release": { "path": "releases/toxicity-structure-alert/1.0.0.json", "sha256": "4308f55368413e8e95102c50364c96b5e702ffc30d6c8af0e8ececfb60f21d1a" }, "artifact": { "path": "shards/363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb.zip", "sha256": "363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb", "bytes": 1471244, "skill_path": "toxicity-structure-alert" }, "content_sha256": "901e7a46fce699882a76f7e7c2b808e8f4c6cb0af2e52594a615e72ef8884525" }, { "id": "translational-gap-analyzer", "version": "1.0.0", "display_name": "translational-gap-analyzer", "summary": "Assess translational gaps between preclinical models and human diseases.", "category": "Evidence Insight", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Evidence Insight/translational-gap-analyzer" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 85, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Evidence%20Insight/translational-gap-analyzer/translational-gap-analyzer_audit_result_v2.json", "evaluated_on": "2026-03-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 88, "max_score": 100 }, "dynamic_score": { "score": 83.6, "max_score": 100 } }, "release": { "path": "releases/translational-gap-analyzer/1.0.0.json", "sha256": "7828bec357f9e3f4577378fb595b5b7dde0826c23de14fed11048d643bbda852" }, "artifact": { "path": "shards/fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0.zip", "sha256": "fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0", "bytes": 1383584, "skill_path": "translational-gap-analyzer" }, "content_sha256": "1fe5c3ed0c902819fde98a9a296c64080afb7cd2f4d1a24d24b384ac1a5bfab4" }, { "id": "translational-study-blueprint", "version": "1.0.0", "display_name": "translational-study-blueprint", "summary": "Designs a translational blueprint for moving a biomedical finding toward diagnosis, prognosis, treatment response prediction, patient stratification, or therapeutic development, with explicit translational milestones, validation thresholds, and feasibility-sensitive route framing.", "category": "Protocol Design", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "awesome-med-research-skills/Protocol Design/translational-study-blueprint" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 89, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/awesome-med-research-skills/Protocol%20Design/translational-study-blueprint/eval_report_translational-study-blueprint_result.json", "evaluated_on": "2026-04-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 91, "max_score": 100 }, "dynamic_score": { "score": 87.4, "max_score": 100 } }, "release": { "path": "releases/translational-study-blueprint/1.0.0.json", "sha256": "b7d85dd81710afc8abd2a4e34ff31351378c23dad57aca9c5aecd2a9824fdf8a" }, "artifact": { "path": "shards/630d1b4dab37083d632d924264ae93642b8327521aaa70fbf83de3cc7fd53d61.zip", "sha256": "630d1b4dab37083d632d924264ae93642b8327521aaa70fbf83de3cc7fd53d61", "bytes": 542522, "skill_path": "translational-study-blueprint" }, "content_sha256": "c87987d938297c1d027a858deb5057338f718948aa85127a68f9826dd89d97bc" }, { "id": "treatment-plans", "version": "1.0.0", "display_name": "treatment-plans", "summary": "Format and structurally validate local treatment-plan documentation after clinical decisions have already been supplied and verified by authorized licensed professionals. Use for source traceability, clinician-authored intervention records, goals and checkpoints, shared-decision records, reconciliation handoffs, and release gates—not for clinical decision-making.", "category": "Other", "source": { "repository": "https://github.com/K-Dense-AI/scientific-agent-skills", "commit": "36d8f13a1e754618794bf42f417884940077b4ae", "path": "skills/treatment-plans" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "K-Dense Inc." } ], "license": "MIT", "release": { "path": "releases/treatment-plans/1.0.0.json", "sha256": "33969368d9b25ab37ac7831d30c0e369c3f68dbe1b6bb4ed7edae8322b74d549" }, "artifact": { "path": "shards/b55fb597588b663d1d9dfe3695492a0b1a9b88845adbce48fe564656e55759c6.zip", "sha256": "b55fb597588b663d1d9dfe3695492a0b1a9b88845adbce48fe564656e55759c6", "bytes": 624089, "skill_path": "treatment-plans" }, "content_sha256": "b33e9a36aa6d4a539af43f05f4702207731bf61da193a316dbe9b375d8529d52" }, { "id": "treatment-response-predictor-planner", "version": "1.0.0", "display_name": "treatment-response-predictor-planner", "summary": "Designs studies for predicting treatment response or resistance in biomedical and clinical research. Always use this skill when the user needs a treatment-response or resistance prediction study blueprint rather than a prognostic biomarker protocol, diagnostic test design, causal treatment-effect estimation, or a completed manuscript. Focus on responder definition, treatment context, baseline comparability, feature integration strategy, model development logic, validation architecture, and interpretation boundaries. Do not invent response rates, cohort size, assay readiness, regimen uniformity, literature support, or validation access.", "category": "Protocol Design", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "awesome-med-research-skills/Protocol Design/treatment-response-predictor-planner" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 90, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/awesome-med-research-skills/Protocol%20Design/treatment-response-predictor-planner/eval_report_treatment-response-predictor-planner_result.json", "evaluated_on": "2026-04-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 93, "max_score": 100 }, "dynamic_score": { "score": 87.7, "max_score": 100 } }, "release": { "path": "releases/treatment-response-predictor-planner/1.0.0.json", "sha256": "9d60497a0cbdf59a5bdc33eda62b43d6496d46184bea4f746de2f17a3dbf7cc6" }, "artifact": { "path": "shards/630d1b4dab37083d632d924264ae93642b8327521aaa70fbf83de3cc7fd53d61.zip", "sha256": "630d1b4dab37083d632d924264ae93642b8327521aaa70fbf83de3cc7fd53d61", "bytes": 542522, "skill_path": "treatment-response-predictor-planner" }, "content_sha256": "12b5885dbaa49ff119850d277e7ba452db5a1770f862520a2c38250cd5389d31" }, { "id": "tumor-immune-infiltration-diagnostic-ml-research-planner", "version": "1.0.0", "display_name": "tumor-immune-infiltration-diagnostic-ml-research-planner", "summary": "Generates complete tumor immune-infiltration-guided bulk-transcriptome diagnostic biomarker and machine-learning research designs from a user-provided cancer type and study direction. Always use this skill whenever a user wants to design, plan, or build a tumor bioinformatics study centered on differential expression, immune infiltration estimation, immune-linked module discovery, consensus feature selection, diagnostic modeling, nomogram construction, clinical association, and optional prognostic extension or validation. Covers five study patterns (immune-cell-first diagnostic workflow, immune-module-to-biomarker workflow, consensus-ML biomarker workflow, diagnostic-plus-prognostic hybrid workflow, translational validation workflow) and always outputs four workload configs (Lite / Standard / Advanced / Publication+) with recommended primary plan, step-by-step workflow, figure plan, validation strategy, minimal executable version, publication upgrade path, reference literature pack, and self-critical risk review.", "category": "Protocol Design", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "awesome-med-research-skills/Protocol Design/tumor-immune-infiltration-diagnostic-ml-research-planner" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 90, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/awesome-med-research-skills/Protocol%20Design/tumor-immune-infiltration-diagnostic-ml-research-planner/eval_report_tumor-immune-infiltration-diagnostic-ml-research-planner_result.json", "evaluated_on": "2026-04-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 93, "max_score": 100 }, "dynamic_score": { "score": 88.7, "max_score": 100 } }, "release": { "path": "releases/tumor-immune-infiltration-diagnostic-ml-research-planner/1.0.0.json", "sha256": "6de3d8ab0da6e4d1287143e8a9b1af077e3d949cddbcbf249e35979f4ff067be" }, "artifact": { "path": "shards/630d1b4dab37083d632d924264ae93642b8327521aaa70fbf83de3cc7fd53d61.zip", "sha256": "630d1b4dab37083d632d924264ae93642b8327521aaa70fbf83de3cc7fd53d61", "bytes": 542522, "skill_path": "tumor-immune-infiltration-diagnostic-ml-research-planner" }, "content_sha256": "85e9e77fad38957d24ff8dc4a6e4b1e6744e8468afbe3e8c2a8a7a70f8e028b9" }, { "id": "two-sample-mr-exposure-screening-reference-grounded", "version": "1.0.0", "display_name": "two-sample-mr-exposure-screening-reference-grounded", "summary": "Generates complete two-sample Mendelian randomization research designs from a user-provided outcome, exposure or exposure family, and robustness direction. Use when a study centers on summary-statistics causal inference with instrument selection, harmonization, IVW-primary estimation, complementary estimators, sensitivity analyses, optional multivariable upgrades, and conservative evidence interpretation. Covers five study patterns and always outputs Lite / Standard / Advanced / Publication+ with a recommended primary plan, stepwise workflow, figure plan, validation hierarchy, minimal executable version, publication upgrade path, and strictly verified literature retrieval.", "category": "Protocol Design", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "awesome-med-research-skills/Protocol Design/two-sample-mr-exposure-screening-reference-grounded" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 89, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/awesome-med-research-skills/Protocol%20Design/two-sample-mr-exposure-screening-reference-grounded/eval_report_two-sample-mr-exposure-screening-reference-grounded_result.json", "evaluated_on": "2026-04-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 92, "max_score": 100 }, "dynamic_score": { "score": 87.7, "max_score": 100 } }, "release": { "path": "releases/two-sample-mr-exposure-screening-reference-grounded/1.0.0.json", "sha256": "43d0348f05a71ba88ecf47b6c55ef326c2f156c11310d15751988abc09486416" }, "artifact": { "path": "shards/630d1b4dab37083d632d924264ae93642b8327521aaa70fbf83de3cc7fd53d61.zip", "sha256": "630d1b4dab37083d632d924264ae93642b8327521aaa70fbf83de3cc7fd53d61", "bytes": 542522, "skill_path": "two-sample-mr-exposure-screening-reference-grounded" }, "content_sha256": "20fbda8aca01b87d7fbe94c0227d8692a4744b54e70ea9117e24ad949aa9945f" }, { "id": "two-sample-mr-research-planner", "version": "1.0.0", "display_name": "two-sample-mr-research-planner", "summary": "Generates complete two-sample Mendelian randomization (MR) research designs from a user-provided research direction. Use when users want to design, plan, or build a study using two-sample MR to test causal relationships. Triggers:\"design a two-sample MR study\", \"build a publishable MR paper\", \"test whether this biomarker causally affects this disease\", \"generate Lite/Standard/Advanced MR plans\", \"screen multiple exposures with MR\", \"bidirectional MR design\", \"causal inference using GWAS summary statistics\", or \"I want to study X and Y using MR\". Always outputs four workload configurations (Lite / Standard / Advanced / Publication+) with a recommended primary plan, step-by-step workflow, figure plan, validation strategy, minimal executable version, and publication upgrade path.", "category": "Protocol Design", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Protocol Design/two-sample-mr-research-planner" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "release": { "path": "releases/two-sample-mr-research-planner/1.0.0.json", "sha256": "2144aa555360fa36cf1204ff428eea073b1b53d8224b2b8d88b4492f6b0762b1" }, "artifact": { "path": "shards/630d1b4dab37083d632d924264ae93642b8327521aaa70fbf83de3cc7fd53d61.zip", "sha256": "630d1b4dab37083d632d924264ae93642b8327521aaa70fbf83de3cc7fd53d61", "bytes": 542522, "skill_path": "two-sample-mr-research-planner" }, "content_sha256": "49884dfc5279c7bf2b0c576e49574a03a169d26d22b45a3c59dc16587d1d595c" }, { "id": "umap-tsne-analysis", "version": "1.0.1", "display_name": "umap-tsne-analysis", "summary": "Use when performing sample-level dimensionality reduction and visualization on abundance or OTU-style matrices with a companion group file, generating UMAP and/or t-SNE coordinates and plots for group separation assessment. NOT for: differential expression testing, single-cell workflows requiring dedicated embeddings pipelines, or analyses without a sample grouping file.", "category": "Data Analysis", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "awesome-med-research-skills/Data Analysis/umap-tsne-analysis" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "license": "Unknown", "evaluation": { "kind": "upstream-self-assessment", "score": 85, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/awesome-med-research-skills/Data%20Analysis/umap-tsne-analysis/eval_report_umap-tsne-analysis_result.json", "evaluated_on": "2026-04-27", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 85, "max_score": 100 }, "dynamic_score": { "score": 85.4, "max_score": 100 } }, "release": { "path": "releases/umap-tsne-analysis/1.0.1.json", "sha256": "053d3db4e41bc5b37a42ee8df995e66928e201a39c3a235af5711bdf8b51d12b" }, "artifact": { "path": "shards/2b2d134141e3d5698b9e174f154599abf13d7ddd3c9060e29ef536399fd82407.zip", "sha256": "2b2d134141e3d5698b9e174f154599abf13d7ddd3c9060e29ef536399fd82407", "bytes": 17034738, "skill_path": "umap-tsne-analysis" }, "content_sha256": "94e41233e50e4f66df5008027feba8bfb3f9b71c3545c592ae3c48400c4024b0" }, { "id": "uniprot-database", "version": "1.0.0", "display_name": "uniprot-database", "summary": "Direct REST API access to UniProt for protein search, entry retrieval, and identifier mapping; use when you need programmatic UniProtKB queries or cross-database ID conversion.", "category": "Evidence Insight", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Evidence Insight/uniprot-database" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 90, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Evidence%20Insight/uniprot-database/uniprot-database_audit_result_v1.json", "evaluated_on": "2026-03-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 87, "max_score": 100 }, "dynamic_score": { "score": 92.6, "max_score": 100 } }, "release": { "path": "releases/uniprot-database/1.0.0.json", "sha256": "72a046bf52d0113257d7613ee459c55b34f0607f806b098d07f023ea2867d7f1" }, "artifact": { "path": "shards/fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0.zip", "sha256": "fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0", "bytes": 1383584, "skill_path": "uniprot-database" }, "content_sha256": "bd8dfda4f8b89672edf453dbe61221e28bf85371f33d7bd61429b2cda4fd8691" }, { "id": "unmet-clinical-need-extractor", "version": "1.0.0", "display_name": "unmet-clinical-need-extractor", "summary": "Extracts concrete unmet clinical needs from guidelines, reviews, real-world studies, and clinical-practice evidence. Use this skill when a user wants to turn broad medical research value into specific clinical pain points such as weak early detection, poor risk stratification, treatment-response heterogeneity, monitoring gaps, diagnostic delay, undertreatment, overtreatment, or implementation failure. Always ground unmet-need claims in retrieved evidence and distinguish true care gaps from generic statements of importance.", "category": "Evidence Insight", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "awesome-med-research-skills/Evidence Insight/unmet-clinical-need-extractor" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 87, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/awesome-med-research-skills/Evidence%20Insight/unmet-clinical-need-extractor/eval_report_unmet-clinical-need-extractor_result.json", "evaluated_on": "2026-04-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 90, "max_score": 100 }, "dynamic_score": { "score": 84.6, "max_score": 100 } }, "release": { "path": "releases/unmet-clinical-need-extractor/1.0.0.json", "sha256": "158afc63030f58d45970fbcead9dd2eca2c156e18a25c0ac5639d3d9108c56d5" }, "artifact": { "path": "shards/fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0.zip", "sha256": "fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0", "bytes": 1383584, "skill_path": "unmet-clinical-need-extractor" }, "content_sha256": "9768a4915e687880e616131707ee84fa7d147df626d71f477d2cdbd51d7204af" }, { "id": "unstructured-medical-text-miner", "version": "1.0.1", "display_name": "unstructured-medical-text-miner", "summary": "Mine unstructured clinical text from MIMIC-IV to extract diagnostic logic.", "category": "Evidence Insight", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Evidence Insight/unstructured-medical-text-miner" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "Unknown", "evaluation": { "kind": "upstream-self-assessment", "score": 85, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Evidence%20Insight/unstructured-medical-text-miner/unstructured-medical-text-miner_audit_result_v2.json", "evaluated_on": "2026-03-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 88, "max_score": 100 }, "dynamic_score": { "score": 83.6, "max_score": 100 } }, "release": { "path": "releases/unstructured-medical-text-miner/1.0.1.json", "sha256": "81ade92450e52290905bdc1e3bb04a2a2294618824094d80adc71d099259aa1f" }, "artifact": { "path": "shards/2b2d134141e3d5698b9e174f154599abf13d7ddd3c9060e29ef536399fd82407.zip", "sha256": "2b2d134141e3d5698b9e174f154599abf13d7ddd3c9060e29ef536399fd82407", "bytes": 17034738, "skill_path": "unstructured-medical-text-miner" }, "content_sha256": "e06a5525724ed0769a805a903ba5745186fc46f296aeb3a2e691e4a095b93b42" }, { "id": "upset-plot-converter", "version": "1.0.0", "display_name": "upset-plot-converter", "summary": "Convert complex Venn diagrams with more than 4 sets to clearer Upset.", "category": "Data Analysis", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Data Analysis/upset-plot-converter" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 86, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Data%20Analysis/upset-plot-converter/upset-plot-converter_audit_result_v2.json", "evaluated_on": "2026-03-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 83, "max_score": 100 }, "dynamic_score": { "score": 87.2, "max_score": 100 } }, "release": { "path": "releases/upset-plot-converter/1.0.0.json", "sha256": "c9ccdd9f6696b52d6651ed2c29cf4cccda011d6ca64b52fb09a374c7581f7953" }, "artifact": { "path": "shards/363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb.zip", "sha256": "363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb", "bytes": 1471244, "skill_path": "upset-plot-converter" }, "content_sha256": "393ffbc45253c9f8242cf3bf8ac9375660c1cc8a77fcfb0eca16255dbeb70887" }, { "id": "uspto-database", "version": "1.0.0", "display_name": "uspto-database", "summary": "Access USPTO APIs for patent/trademark searches, examination history (PEDS), assignments, citations, office actions, TSDR, for IP analysis and prior art searches.", "category": "Other", "source": { "repository": "https://github.com/K-Dense-AI/scientific-agent-skills", "commit": "71add644263a56368f8680d68df504c6674dc1e5", "path": "scientific-skills/uspto-database" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "K-Dense Inc." } ], "license": "Unknown", "release": { "path": "releases/uspto-database/1.0.0.json", "sha256": "ee56a304c83512439bbfe3e99468648855ee65c2cc5b0eb8f1f2029c77a450db" }, "artifact": { "path": "shards/fcd3eaa3439234f967f6835adacfe5bd6e8cacef5cbe5da7a06ffa4dec4f4973.zip", "sha256": "fcd3eaa3439234f967f6835adacfe5bd6e8cacef5cbe5da7a06ffa4dec4f4973", "bytes": 110833, "skill_path": "uspto-database" }, "content_sha256": "f4a713dc7efef7fd2410184bfcb2a574c23068b47d2ef515f7a53c3fa2925f91" }, { "id": "validation-strategy-designer", "version": "1.0.0", "display_name": "validation-strategy-designer", "summary": "Designs internal, external, temporal, and functional validation strategies at the protocol stage for medical research studies.", "category": "Protocol Design", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "awesome-med-research-skills/Protocol Design/validation-strategy-designer" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 88, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/awesome-med-research-skills/Protocol%20Design/validation-strategy-designer/eval_report_validation-strategy-designer_result.json", "evaluated_on": "2026-04-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 90, "max_score": 100 }, "dynamic_score": { "score": 87.4, "max_score": 100 } }, "release": { "path": "releases/validation-strategy-designer/1.0.0.json", "sha256": "6fba33597f5c6f31175ec0a03793cd358c1797d3e42f05da24971a24c7f94d14" }, "artifact": { "path": "shards/630d1b4dab37083d632d924264ae93642b8327521aaa70fbf83de3cc7fd53d61.zip", "sha256": "630d1b4dab37083d632d924264ae93642b8327521aaa70fbf83de3cc7fd53d61", "bytes": 542522, "skill_path": "validation-strategy-designer" }, "content_sha256": "a9ca65ad5a198fd628db26c848a98b74b14114ca71521aa8b49a59028ebd2e0b" }, { "id": "variant-annotation", "version": "1.0.1", "display_name": "variant-annotation", "summary": "Query and annotate gene variants from ClinVar and dbSNP databases. \\n\\.", "category": "Evidence Insight", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Evidence Insight/variant-annotation" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "Unknown", "evaluation": { "kind": "upstream-self-assessment", "score": 85, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Evidence%20Insight/variant-annotation/variant-annotation_audit_result_v1.json", "evaluated_on": "2026-03-23", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 87, "max_score": 100 }, "dynamic_score": { "score": 83.6, "max_score": 100 } }, "release": { "path": "releases/variant-annotation/1.0.1.json", "sha256": "2d4233a3489563336f97f0a93986378e43436c2a26b851aa781a53e2733370b1" }, "artifact": { "path": "shards/2b2d134141e3d5698b9e174f154599abf13d7ddd3c9060e29ef536399fd82407.zip", "sha256": "2b2d134141e3d5698b9e174f154599abf13d7ddd3c9060e29ef536399fd82407", "bytes": 17034738, "skill_path": "variant-annotation" }, "content_sha256": "2e5c99e4662497880008e92da43a971b7e096c5d5f2f7b2f72d4ab01d3206657" }, { "id": "variant-pathogenicity-predictor", "version": "1.0.0", "display_name": "variant-pathogenicity-predictor", "summary": "Integrate REVEL, CADD, PolyPhen scores to predict variant pathogenicity.", "category": "Data Analysis", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Data Analysis/variant-pathogenicity-predictor" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 86, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Data%20Analysis/variant-pathogenicity-predictor/variant-pathogenicity-predictor_audit_result_v2.json", "evaluated_on": "2026-03-22", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 83, "max_score": 100 }, "dynamic_score": { "score": 87.2, "max_score": 100 } }, "release": { "path": "releases/variant-pathogenicity-predictor/1.0.0.json", "sha256": "339e58e6a5763c7c974395665c205b62f31b5d87a62e444e8db046119a486bcd" }, "artifact": { "path": "shards/363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb.zip", "sha256": "363034760dae6778b913abb9de8c2c721e250f5255b4c76346abd49f4a0de2cb", "bytes": 1471244, "skill_path": "variant-pathogenicity-predictor" }, "content_sha256": "b706c79eb30d8437d01f8973fcd067a08b725a65abbb0f085410390d5ab5a0fe" }, { "id": "vector-text-fixer", "version": "1.0.0", "display_name": "vector-text-fixer", "summary": "Fix garbled text in PDF/SVG vector graphics caused by font encoding issues, making files editable in AI tools. Supports batch processing and JSON export for manual correction.", "category": "Other", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Other/vector-text-fixer" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 88, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Other/vector-text-fixer/vector-text-fixer_audit_result_v2.json", "evaluated_on": "2026-03-19", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 89, "max_score": 100 }, "dynamic_score": { "score": 87.4, "max_score": 100 } }, "release": { "path": "releases/vector-text-fixer/1.0.0.json", "sha256": "a4cfe49ba7318599c8b30531e73493390fb32ac8ac72264c4e6b7b410b56a887" }, "artifact": { "path": "shards/fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0.zip", "sha256": "fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0", "bytes": 1383584, "skill_path": "vector-text-fixer" }, "content_sha256": "91b0e0facaef708655ce563f35aa636ad4fb5b56723f0331130fb4e1eecd2cf1" }, { "id": "venue-templates", "version": "1.0.1", "display_name": "venue-templates", "summary": "Venue-specific LaTeX templates, formatting requirements, and submission guidelines for journals, conferences, posters, and grants—use when a target venue imposes strict layout, page limits, anonymization, or agency compliance rules.", "category": "Evidence Insight", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Evidence Insight/venue-templates" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "Unknown", "evaluation": { "kind": "upstream-self-assessment", "score": 87, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Evidence%20Insight/venue-templates/venue-templates_audit_result_v1.json", "evaluated_on": "2026-03-23", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 87, "max_score": 100 }, "dynamic_score": { "score": 86.4, "max_score": 100 } }, "release": { "path": "releases/venue-templates/1.0.1.json", "sha256": "f499aef1562499c082313901d71c01bbc6ae22da1681faf12e2f256ffb39d842" }, "artifact": { "path": "shards/2b2d134141e3d5698b9e174f154599abf13d7ddd3c9060e29ef536399fd82407.zip", "sha256": "2b2d134141e3d5698b9e174f154599abf13d7ddd3c9060e29ef536399fd82407", "bytes": 17034738, "skill_path": "venue-templates" }, "content_sha256": "5317f38f29546798a04aa4ce7a8503060377ac3a53da6758510acc0e7ce5ce50" }, { "id": "virtual-patient-roleplay", "version": "1.0.0", "display_name": "virtual-patient-roleplay", "summary": "Simulate standardized patient encounters for medical training, supporting OSCE-style history-taking practice, communication skills rehearsal, and educational debriefing.", "category": "Other", "source": { "repository": "https://github.com/aipoch/medical-research-skills", "commit": "d92441066ea6259967469be8e0c8c7b6587928ab", "path": "scientific-skills/Other/virtual-patient-roleplay" }, "publisher": { "id": "aipoch", "name": "AIPOCH", "url": "https://aipoch.com/agent-skills" }, "authors": [ { "name": "AIPOCH" } ], "license": "MIT", "evaluation": { "kind": "upstream-self-assessment", "score": 85, "max_score": 100, "report_url": "https://github.com/aipoch/medical-research-skills/blob/d92441066ea6259967469be8e0c8c7b6587928ab/scientific-skills/Other/virtual-patient-roleplay/virtual-patient-roleplay_audit_result_v2.json", "evaluated_on": "2026-03-19", "evaluator_version": "skill-auditor@1.0", "static_score": { "score": 85, "max_score": 100 }, "dynamic_score": { "score": 85, "max_score": 100 } }, "release": { "path": "releases/virtual-patient-roleplay/1.0.0.json", "sha256": "5469ddf1fe844e7a56ddd289f9dc133b0725e6eabf7d2ca82220704ad6def6ad" }, "artifact": { "path": "shards/fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0.zip", "sha256": "fe539b040e93655def3e77f2488b69a153f319072065f701aef6f8076d8bbeb0", "bytes": 1383584, "skill_path": "virtual-patient-roleplay" }, "content_sha256": "0de367bdd57636f55fcda28dcc67cdb26b16a1ccd524fa6b72f8e991a4b274a9" }, { "id": "visual-content-desc", "version": "1.0.1", "display_name": "visual-content-desc", "summary": "Generates detailed text descriptions of medical 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