[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6DB2YTQ87AQ7HH2Y5A2ATRB":3,"skill-same:01M6DB2YTQ87AQ7HH2Y5A2ATRB":68},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":26,"publishedAt":43,"linkOnly":44,"markdownContent":45,"markdownBody":46,"frontMatter":47,"refName":48,"commitSha":49,"skillDirectory":50,"forksCount":14,"repositoryHtmlUrl":51,"repositoryHomepageUrl":6,"ownerHtmlUrl":52,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":53,"install":54,"resources":67},"01M6DB2YTQ87AQ7HH2Y5A2ATRB","biomedical-paper-triage",null,"Use when turning a raw list of biomedical papers into a ranked, access-tagged shortlist graded T1-T4 by human\u002Fanimal\u002Fin-vitro evidence strength via Scholarly Access MCP ; do NOT use for generic literature metadata or citation graphs (use scholarly-literature-review) ; returns defensible ranked shortlist with evidence grades.","Prekzursil\u002Fagent-skills-toolchain","Prekzursil","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F54636077?v=4","01M6DACZ1PCQHD99SCR1YXG28B","Andrei Mihai Visalon","01M6DACZ670WZF535V3WYYSS4Z",0,"MIT","https:\u002F\u002Fgithub.com\u002FPrekzursil\u002Fagent-skills-toolchain\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fbiomedical-paper-triage\u002FSKILL.md",[18,23],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000021","资料检索与研究","SCENARIO","使用场景",{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",[27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42],"triage","biomedical","paper","biomedical paper triage","MCP","review","metadata","access","returns","list","use","evidence","do","Citation","human","graphs","2026-11-10 10:12:30",false,"---\nname: biomedical-paper-triage\ndescription: Use when turning a raw list of biomedical papers into a ranked, access-tagged shortlist graded T1-T4 by human\u002Fanimal\u002Fin-vitro evidence strength via Scholarly Access MCP ; do NOT use for generic literature metadata or citation graphs (use scholarly-literature-review) ; returns defensible ranked shortlist with evidence grades.\n---\n\n# Biomedical Paper Triage\n\nTurn a raw list of biomedical papers (DOIs, PMIDs, arXiv ids, or titles) into a defensible, ranked shortlist. Pairs with the `consensus-tooluniverse-research` evidence-grading scheme (T1-T4 x human \u002F animal \u002F in-vitro) so the shortlist carries both a triage rank and an evidence tier.\n\n## When to use\n\n- You ran a search (PubMed, Consensus, a colleague's bibliography) and got back 20-200 candidate papers and need to decide which 10-20 to actually read.\n- You must justify inclusion\u002Fexclusion for a mini-review or grant background and want recency, study-type, citation-signal, and access reasons recorded per paper.\n- You want to avoid wasting time queuing papers that turn out to be paywalled, retracted, or non-redistributable.\n- You need an access-honest list to hand to the `consensus-tooluniverse-research` workflow for evidence grading and download.\n\n## The MCP tools it uses\n\nIt primarily uses three tools (spec dot-name -> MCP underscore-name):\n\n- `paper.batch_resolve` -> `paper_batch_resolve(identifiers[])` : resolve a whole list at once into a table of canonical metadata, normalized ids (doi, pmid, arxiv, openalex), access status, OA links, and a list of unresolved items. This is the workhorse for the first pass.\n- `paper.access_status` -> `paper_access_status(identifier|doi|arxiv)` : confirm or refine the honest access category for a single paper when the batch result is `unknown` or you need provenance.\n- `paper.citation_graph` -> `paper_citation_graph(identifier|doi, limit)` : pull references and citing works from Semantic Scholar \u002F OpenAlex to derive a citation signal and to discover high-impact neighbors worth adding to the pool.\n\nSupporting tools when you need them: `paper.resolve` (single id sanity check), `paper.fetch_metadata` (abstract + references for closer study-type reading), `paper.find_oa_pdf` and `paper.download_oa_pdf` (only after triage, only for `oa_pdf` items at allowlisted hosts), and `paper.summarize` (structured digest from legally-available metadata).\n\n## Access categories (honest)\n\n- `oa_pdf` : downloadable OA at a legit source.\n- `subscription` : paywalled. Cite only.\n- `unavailable` : no available full text exists.\n- `unknown` : access could not be determined; run `paper_access_status` to refine.\n\n## Workflow steps\n\n1. Gather the candidate identifiers into a single list (DOIs, PMIDs, arXiv ids, or titles). Deduplicate obvious repeats.\n2. Run `paper_batch_resolve(identifiers)` to get one table: canonical title, year, normalized ids, access status, OA link, and the unresolved set.\n3. For any item with status `unknown`, call `paper_access_status` on its best id to get an honest category and provenance. Triage unresolved items separately (often title typos or non-indexed preprints).\n4. Derive a citation signal: for the top candidates by relevance\u002Frecency, call `paper_citation_graph(doi, limit)` and record citing-work count plus any high-impact neighbors that should join the pool.\n5. Infer study type from metadata\u002Fabstract (`paper_fetch_metadata` when the title is ambiguous) and map it to an evidence tier using the paired `consensus-tooluniverse-research` T1-T4 x human \u002F animal \u002F in-vitro scheme (e.g. RCT \u002F systematic review = higher human-tier; cell-line only = in-vitro tier).\n6. Score and rank each paper across five axes: relevance to the question, recency, study-type strength, citation signal, and availability. Keep availability as a tag, not a hard filter, unless the user wants only readable papers.\n7. Produce the ranked, access-tagged shortlist (rank, title, year, ids, study type, evidence tier, citation signal, access category, one-line reason). Hand `oa_pdf` items to download only after the shortlist is approved.\n\n## Concrete examples\n\n- Resolve a known landmark: `paper_resolve(\"10.1371\u002Fjournal.pmed.0020124\")` returns the resolved record titled \"Why Most Published Research Findings Are False\".\n- Confirm openly downloadable: `paper_access_status(doi=\"10.1371\u002Fjournal.pone.0000308\")` returns `oa_pdf` with a PLOS OA url, so this paper sits high on the availability axis.\n- Citation neighbors: `paper_citation_graph(doi=\"10.1371\u002Fjournal.pmed.0020124\", limit=25)` returns references and citing works to compute citation signal and surface high-impact additions.\n\n## Limitations\n\n- Triage scores are heuristic; a low rank is a reading-priority signal, not a quality verdict. A human reviews the final shortlist.\n- Citation counts depend on Semantic Scholar \u002F OpenAlex coverage and lag for very recent or niche work.\n- Study-type inference from titles\u002Fabstracts can be wrong; verify before assigning a high evidence tier.\n- `unknown` and `unavailable` items are surfaced, not silently dropped; decide their fate explicitly.\n\n## Tested invocation\n\n```\npaper_batch_resolve([\"10.1371\u002Fjournal.pmed.0020124\", \"10.1371\u002Fjournal.pone.0000308\", \"10.1056\u002FNEJMoa2034577\"])\n# -> table: each id resolved with canonical title, normalized ids, and access status\n#    (PLOS Medicine + PLOS ONE = oa_pdf; NEJM = subscription \u002F free_to_read_not_redistributable)\n\npaper_access_status(doi=\"10.1371\u002Fjournal.pone.0000308\")\n# -> { category: \"oa_pdf\", oa_url: \"\u003CPLOS OA url>\", provenance: ... }\n\npaper_citation_graph(doi=\"10.1371\u002Fjournal.pmed.0020124\", limit=25)\n# -> references + citing works for the citation-signal axis\n```\n","# Biomedical Paper Triage\n\nTurn a raw list of biomedical papers (DOIs, PMIDs, arXiv ids, or titles) into a defensible, ranked shortlist. Pairs with the `consensus-tooluniverse-research` evidence-grading scheme (T1-T4 x human \u002F animal \u002F in-vitro) so the shortlist carries both a triage rank and an evidence tier.\n\n## When to use\n\n- You ran a search (PubMed, Consensus, a colleague's bibliography) and got back 20-200 candidate papers and need to decide which 10-20 to actually read.\n- You must justify inclusion\u002Fexclusion for a mini-review or grant background and want recency, study-type, citation-signal, and access reasons recorded per paper.\n- You want to avoid wasting time queuing papers that turn out to be paywalled, retracted, or non-redistributable.\n- You need an access-honest list to hand to the `consensus-tooluniverse-research` workflow for evidence grading and download.\n\n## The MCP tools it uses\n\nIt primarily uses three tools (spec dot-name -> MCP underscore-name):\n\n- `paper.batch_resolve` -> `paper_batch_resolve(identifiers[])` : resolve a whole list at once into a table of canonical metadata, normalized ids (doi, pmid, arxiv, openalex), access status, OA links, and a list of unresolved items. This is the workhorse for the first pass.\n- `paper.access_status` -> `paper_access_status(identifier|doi|arxiv)` : confirm or refine the honest access category for a single paper when the batch result is `unknown` or you need provenance.\n- `paper.citation_graph` -> `paper_citation_graph(identifier|doi, limit)` : pull references and citing works from Semantic Scholar \u002F OpenAlex to derive a citation signal and to discover high-impact neighbors worth adding to the pool.\n\nSupporting tools when you need them: `paper.resolve` (single id sanity check), `paper.fetch_metadata` (abstract + references for closer study-type reading), `paper.find_oa_pdf` and `paper.download_oa_pdf` (only after triage, only for `oa_pdf` items at allowlisted hosts), and `paper.summarize` (structured digest from legally-available metadata).\n\n## Access categories (honest)\n\n- `oa_pdf` : downloadable OA at a legit source.\n- `subscription` : paywalled. Cite only.\n- `unavailable` : no available full text exists.\n- `unknown` : access could not be determined; run `paper_access_status` to refine.\n\n## Workflow steps\n\n1. Gather the candidate identifiers into a single list (DOIs, PMIDs, arXiv ids, or titles). Deduplicate obvious repeats.\n2. Run `paper_batch_resolve(identifiers)` to get one table: canonical title, year, normalized ids, access status, OA link, and the unresolved set.\n3. For any item with status `unknown`, call `paper_access_status` on its best id to get an honest category and provenance. Triage unresolved items separately (often title typos or non-indexed preprints).\n4. Derive a citation signal: for the top candidates by relevance\u002Frecency, call `paper_citation_graph(doi, limit)` and record citing-work count plus any high-impact neighbors that should join the pool.\n5. Infer study type from metadata\u002Fabstract (`paper_fetch_metadata` when the title is ambiguous) and map it to an evidence tier using the paired `consensus-tooluniverse-research` T1-T4 x human \u002F animal \u002F in-vitro scheme (e.g. RCT \u002F systematic review = higher human-tier; cell-line only = in-vitro tier).\n6. Score and rank each paper across five axes: relevance to the question, recency, study-type strength, citation signal, and availability. Keep availability as a tag, not a hard filter, unless the user wants only readable papers.\n7. Produce the ranked, access-tagged shortlist (rank, title, year, ids, study type, evidence tier, citation signal, access category, one-line reason). Hand `oa_pdf` items to download only after the shortlist is approved.\n\n## Concrete examples\n\n- Resolve a known landmark: `paper_resolve(\"10.1371\u002Fjournal.pmed.0020124\")` returns the resolved record titled \"Why Most Published Research Findings Are False\".\n- Confirm openly downloadable: `paper_access_status(doi=\"10.1371\u002Fjournal.pone.0000308\")` returns `oa_pdf` with a PLOS OA url, so this paper sits high on the availability axis.\n- Citation neighbors: `paper_citation_graph(doi=\"10.1371\u002Fjournal.pmed.0020124\", limit=25)` returns references and citing works to compute citation signal and surface high-impact additions.\n\n## Limitations\n\n- Triage scores are heuristic; a low rank is a reading-priority signal, not a quality verdict. A human reviews the final shortlist.\n- Citation counts depend on Semantic Scholar \u002F OpenAlex coverage and lag for very recent or niche work.\n- Study-type inference from titles\u002Fabstracts can be wrong; verify before assigning a high evidence tier.\n- `unknown` and `unavailable` items are surfaced, not silently dropped; decide their fate explicitly.\n\n## Tested invocation\n\n```\npaper_batch_resolve([\"10.1371\u002Fjournal.pmed.0020124\", \"10.1371\u002Fjournal.pone.0000308\", \"10.1056\u002FNEJMoa2034577\"])\n# -> table: each id resolved with canonical title, normalized ids, and access status\n#    (PLOS Medicine + PLOS ONE = oa_pdf; NEJM = subscription \u002F free_to_read_not_redistributable)\n\npaper_access_status(doi=\"10.1371\u002Fjournal.pone.0000308\")\n# -> { category: \"oa_pdf\", oa_url: \"\u003CPLOS OA url>\", provenance: ... }\n\npaper_citation_graph(doi=\"10.1371\u002Fjournal.pmed.0020124\", limit=25)\n# -> references + citing works for the citation-signal axis\n```\n",{"name":5,"description":7},"main","a298c929b6cbd662e1fce54302ce520d5ceea1a5",".agents\u002Fskills\u002Fbiomedical-paper-triage","https:\u002F\u002Fgithub.com\u002FPrekzursil\u002Fagent-skills-toolchain","https:\u002F\u002Fgithub.com\u002FPrekzursil","2026-11-02 14:30:57",{"source":8,"skillName":5,"commands":55,"prompt":65,"zipAvailable":66},[56,59,62],{"tool":57,"command":58},"npx","npx skills add Prekzursil\u002Fagent-skills-toolchain --skill biomedical-paper-triage",{"tool":60,"command":61},"bunx","bunx skills add Prekzursil\u002Fagent-skills-toolchain --skill biomedical-paper-triage",{"tool":63,"command":64},"pnpm","pnpm dlx skills add Prekzursil\u002Fagent-skills-toolchain --skill biomedical-paper-triage","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FPrekzursil\u002Fagent-skills-toolchain\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fbiomedical-paper-triage\u002FSKILL.md",true,[],[69,90,115,141,155,171],{"skillNo":70,"name":71,"nameZh":6,"description":72,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":73,"categories":74,"keywords":75,"publishedAt":43},"01M6DF7D7478R55P46REYH4QAC","clarify-requirements","Resolve genuine pre-execution ambiguity by scanning repo context, generating 1-6 grounded options, and asking 1-3 focused questions via AskUserQuestion. NOT for mid-task use (injection risk); NOT when the goal is already unambiguous; use to disambiguate vague prompts before any file edits.","https:\u002F\u002Fgithub.com\u002FPrekzursil\u002Fagent-skills-toolchain\u002Fblob\u002Fmain\u002Ftools\u002Fworkspace-hooks\u002Fskills\u002Fclarify-requirements\u002FSKILL.md",[],[76,77,78,79,80,81,82,83,84,37,85,86,87,88,89],"clarify requirements","clarify","requirements","risk","options","task","prompts","goal","scanning","context","repo","injection","execution","file",{"skillNo":91,"name":92,"nameZh":6,"description":93,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":94,"categories":95,"keywords":97,"publishedAt":43},"01M6DF79CA4BHJS66J8EZDBPWJ","openmm-md","Run FREE, open-source, Windows-native GPU molecular dynamics via the openmm-md MCP (OpenMM + CUDA on the local NVIDIA GPU). Use for protein \u002F protein-ligand MD when you want a free stack or are on Windows without a Schrodinger license. Covers the full workflow (prepare, solvate, minimize, equilibrate, production, analyze, mmgbsa) AND an honest decision guide vs Schrodinger Desmond, including how to run Desmond legitimately on Linux or a cloud GPU with a real license. This is a deliberately LESSER free alternative and does NOT replace the schrodinger MCP tools. Do NOT use it to pick a free tool when a validated FEP+, WaterMap, IFD-MD, or Prime MM-GBSA result is actually required.","https:\u002F\u002Fgithub.com\u002FPrekzursil\u002Fagent-skills-toolchain\u002Fblob\u002Fmain\u002Ftools\u002Fmcp\u002Fopenmm-md-mcp\u002Fskill\u002Fopenmm-md\u002FSKILL.md",[96],{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},[98,99,100,101,102,103,104,105,106,107,108,109,110,111,37,112,113,114,39,31],"openmm md","how","decision","pick","linux","analyze","tool","stack","open","NVIDIA","gpu","license","workflow","windows","free","molecular dynamics","local",{"skillNo":116,"name":117,"nameZh":6,"description":118,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":119,"categories":120,"keywords":127,"publishedAt":43},"01M6DF76C1MG85VEWP8VZCBNMN","visual-review","Take screenshots of web pages and UI using Playwright for visual review and iteration","https:\u002F\u002Fgithub.com\u002FPrekzursil\u002Fagent-skills-toolchain\u002Fblob\u002Fmain\u002Ftools\u002Feditor\u002Fmetaswarm\u002Fskills\u002Fvisual-review\u002FSKILL.md",[121,124],{"categoryNo":122,"categoryName":123,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000003","演示文稿",{"categoryNo":125,"categoryName":126,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000008","测试与质量",[32,128,129,130,131,132,133,134,135,136,99,137,138,139,140],"visual","visual review","pages","web","false","iteration","take","check","slides","ui","screenshot","playwright","screenshots",{"skillNo":142,"name":143,"nameZh":6,"description":144,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":145,"categories":146,"keywords":147,"publishedAt":43},"01M6DF73AQVCBZKEN0YXEX9FXP","status","Diagnostic status report — shows metaswarm installation state, project setup, and potential issues","https:\u002F\u002Fgithub.com\u002FPrekzursil\u002Fagent-skills-toolchain\u002Fblob\u002Fmain\u002Ftools\u002Feditor\u002Fmetaswarm\u002Fskills\u002Fstatus\u002FSKILL.md",[],[143,148,149,150,151,152,153,154],"setup","report","project","installation","project setup","issues","state",{"skillNo":156,"name":157,"nameZh":6,"description":158,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":159,"categories":160,"keywords":162,"publishedAt":43},"01M6DF6X6C2XB0P6GX76P7TTYV","start","Use when starting work on any task, when the user mentions metaswarm, or when the user wants to begin tracked development work","https:\u002F\u002Fgithub.com\u002FPrekzursil\u002Fagent-skills-toolchain\u002Fblob\u002Fmain\u002Ftools\u002Feditor\u002Fmetaswarm\u002Fskills\u002Fstart\u002FSKILL.md",[161],{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},[157,163,81,164,165,166,37,167,168,169,170],"start issue","issue","ready","work on issue","user","work","work on","development",{"skillNo":172,"name":148,"nameZh":6,"description":173,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":174,"categories":175,"keywords":176,"publishedAt":43},"01M6DF6QMJ9AJKFKHHKRT005PQ","Interactive project setup — detects your project, configures metaswarm, writes project-local files","https:\u002F\u002Fgithub.com\u002FPrekzursil\u002Fagent-skills-toolchain\u002Fblob\u002Fmain\u002Ftools\u002Feditor\u002Fmetaswarm\u002Fskills\u002Fsetup\u002FSKILL.md",[],[148,150,114,177,152,178],"files","interactive"]