[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6DBBEZ150AXCPK3WX77RB2K":3,"skill-same:01M6DBBEZ150AXCPK3WX77RB2K":67},{"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":42,"linkOnly":43,"markdownContent":44,"markdownBody":45,"frontMatter":46,"refName":47,"commitSha":48,"skillDirectory":49,"forksCount":14,"repositoryHtmlUrl":50,"repositoryHomepageUrl":6,"ownerHtmlUrl":51,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":52,"install":53,"resources":66},"01M6DBBEZ150AXCPK3WX77RB2K","drug-discovery-literature-bridge",null,"Use when extracting molecules, targets, assays, and clinical context from a paper (via the Scholarly Access MCP paper_* tools) to seed the drug-discovery stack — ToolUniverse DDI\u002FADMET\u002Ftarget-validation\u002Fmechanism tools and the RDKit MCP — keeping DOI provenance on every extracted entity.","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\u002Fdrug-discovery-literature-bridge\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],"discovery","bridge","drug discovery literature bridge","MCP","validation","Tools","provenance","rdkit","access","stack","context","use","seed","clinical","paper","2026-11-10 10:12:30",false,"---\nname: drug-discovery-literature-bridge\ndescription: Use when extracting molecules, targets, assays, and clinical context from a paper (via the Scholarly Access MCP paper_* tools) to seed the drug-discovery stack — ToolUniverse DDI\u002FADMET\u002Ftarget-validation\u002Fmechanism tools and the RDKit MCP — keeping DOI provenance on every extracted entity.\n---\n\n# Drug Discovery Literature Bridge\n\nConnects findings in scholarly papers to the drug-discovery toolchain.\n\n## When to use\n\n- You have a DOI, PMID, arXiv id, or title and want to mine its molecules, targets, assays, or clinical context for downstream modeling.\n- You want to seed a ToolUniverse query (drug-drug-interaction, ADMET, target validation, drug mechanism) from what a paper actually reports.\n- You want to hand a SMILES or compound name from a paper into an RDKit MCP descriptor\u002Fproperty workflow.\n- You are building an evidence trail that links a downstream result back to its source DOI.\n\n## The MCP tools it uses\n\nThis skill is a consumer of the Scholarly Access MCP. It primarily uses:\n\n- `paper_resolve(identifier)` — DOI\u002FPMID\u002FarXiv\u002Ftitle to canonical metadata, normalized ids (doi, pmid, arxiv, openalex), and a confidence score. Use first to lock the identifier.\n- `paper_fetch_metadata(identifier)` — metadata plus abstract plus references where legally available. This is the primary extraction surface.\n- `paper_summarize(identifier or doi)` — a structured digest assembled from legally available metadata and abstract. The agent writes the prose; the tool supplies the fields.\n- `paper_batch_resolve(identifiers)` — when a reference list or candidate set needs status and OA links in one pass.\n\nDownstream it hands entities to the drug-discovery stack: ToolUniverse skills (for example tooluniverse-drug-drug-interaction, tooluniverse-admet-prediction, tooluniverse-drug-target-validation, tooluniverse-drug-mechanism-research, tooluniverse-pharmacovigilance, tooluniverse-clinical-trial-matching) and the RDKit MCP for molecule descriptors and property calculation.\n\n## Workflow steps\n\n1. Resolve and confirm. Call `paper_resolve` on the identifier; record the canonical DOI and confidence. If a title resolves below confidence or comes back unresolved, ask for a DOI or arXiv id rather than guessing.\n2. Call `paper_access_status` so you know how deep the text goes.\n3. Pull metadata. Call `paper_fetch_metadata` (and optionally `paper_summarize`) to get the title, abstract, authors, and references that are legally available.\n4. Extract entities from the returned text only. Identify, from the abstract\u002Fmetadata: molecules and any SMILES or compound names, protein\u002Fgene targets, assay types and readouts, DDI \u002F ADMET \u002F CYP signals (for example CYP3A4 inhibition, hepatotoxicity flags), and clinical context (indication, phase, population). Tag every extracted entity with the source DOI.\n5. Route into the stack:\n   - Targets and indications go to ToolUniverse target-validation, drug-mechanism, or gene-disease association tools.\n   - Compound names \u002F SMILES go to the RDKit MCP for descriptors and properties, and to ToolUniverse chemical-compound retrieval.\n   - DDI \u002F ADMET \u002F CYP signals go to ToolUniverse drug-drug-interaction, admet-prediction, and pharmacovigilance tools.\n   - Clinical context goes to ToolUniverse clinical-trial-matching or clinical-data-integration tools.\n6. Preserve provenance.\n\n## Concrete examples\n\n- Resolve a landmark methods paper before extraction: `paper_resolve(\"10.1371\u002Fjournal.pmed.0020124\")` resolves to \"Why Most Published Research Findings Are False\" — use the canonical DOI it returns as the provenance tag on every extracted entity.\n\n## Limitations\n\n- A too-vague title may resolve below the similarity threshold and come back unresolved — supply a DOI or arXiv id in that case.\n- Citation-graph and reference coverage depend on the upstream metadata APIs and may be incomplete for a given DOI.\n\n## Tested invocation\n\n```\npaper_resolve(\"10.1371\u002Fjournal.pmed.0020124\")\n# -> resolved; title \"Why Most Published Research Findings Are False\"\n\npaper_access_status(doi=\"10.1371\u002Fjournal.pone.0000308\")\n\npaper_access_status(doi=\"10.1056\u002FNEJMoa2034577\")\n# -> subscription \u002F free_to_read_not_redistributable\n```\n","# Drug Discovery Literature Bridge\n\nConnects findings in scholarly papers to the drug-discovery toolchain.\n\n## When to use\n\n- You have a DOI, PMID, arXiv id, or title and want to mine its molecules, targets, assays, or clinical context for downstream modeling.\n- You want to seed a ToolUniverse query (drug-drug-interaction, ADMET, target validation, drug mechanism) from what a paper actually reports.\n- You want to hand a SMILES or compound name from a paper into an RDKit MCP descriptor\u002Fproperty workflow.\n- You are building an evidence trail that links a downstream result back to its source DOI.\n\n## The MCP tools it uses\n\nThis skill is a consumer of the Scholarly Access MCP. It primarily uses:\n\n- `paper_resolve(identifier)` — DOI\u002FPMID\u002FarXiv\u002Ftitle to canonical metadata, normalized ids (doi, pmid, arxiv, openalex), and a confidence score. Use first to lock the identifier.\n- `paper_fetch_metadata(identifier)` — metadata plus abstract plus references where legally available. This is the primary extraction surface.\n- `paper_summarize(identifier or doi)` — a structured digest assembled from legally available metadata and abstract. The agent writes the prose; the tool supplies the fields.\n- `paper_batch_resolve(identifiers)` — when a reference list or candidate set needs status and OA links in one pass.\n\nDownstream it hands entities to the drug-discovery stack: ToolUniverse skills (for example tooluniverse-drug-drug-interaction, tooluniverse-admet-prediction, tooluniverse-drug-target-validation, tooluniverse-drug-mechanism-research, tooluniverse-pharmacovigilance, tooluniverse-clinical-trial-matching) and the RDKit MCP for molecule descriptors and property calculation.\n\n## Workflow steps\n\n1. Resolve and confirm. Call `paper_resolve` on the identifier; record the canonical DOI and confidence. If a title resolves below confidence or comes back unresolved, ask for a DOI or arXiv id rather than guessing.\n2. Call `paper_access_status` so you know how deep the text goes.\n3. Pull metadata. Call `paper_fetch_metadata` (and optionally `paper_summarize`) to get the title, abstract, authors, and references that are legally available.\n4. Extract entities from the returned text only. Identify, from the abstract\u002Fmetadata: molecules and any SMILES or compound names, protein\u002Fgene targets, assay types and readouts, DDI \u002F ADMET \u002F CYP signals (for example CYP3A4 inhibition, hepatotoxicity flags), and clinical context (indication, phase, population). Tag every extracted entity with the source DOI.\n5. Route into the stack:\n   - Targets and indications go to ToolUniverse target-validation, drug-mechanism, or gene-disease association tools.\n   - Compound names \u002F SMILES go to the RDKit MCP for descriptors and properties, and to ToolUniverse chemical-compound retrieval.\n   - DDI \u002F ADMET \u002F CYP signals go to ToolUniverse drug-drug-interaction, admet-prediction, and pharmacovigilance tools.\n   - Clinical context goes to ToolUniverse clinical-trial-matching or clinical-data-integration tools.\n6. Preserve provenance.\n\n## Concrete examples\n\n- Resolve a landmark methods paper before extraction: `paper_resolve(\"10.1371\u002Fjournal.pmed.0020124\")` resolves to \"Why Most Published Research Findings Are False\" — use the canonical DOI it returns as the provenance tag on every extracted entity.\n\n## Limitations\n\n- A too-vague title may resolve below the similarity threshold and come back unresolved — supply a DOI or arXiv id in that case.\n- Citation-graph and reference coverage depend on the upstream metadata APIs and may be incomplete for a given DOI.\n\n## Tested invocation\n\n```\npaper_resolve(\"10.1371\u002Fjournal.pmed.0020124\")\n# -> resolved; title \"Why Most Published Research Findings Are False\"\n\npaper_access_status(doi=\"10.1371\u002Fjournal.pone.0000308\")\n\npaper_access_status(doi=\"10.1056\u002FNEJMoa2034577\")\n# -> subscription \u002F free_to_read_not_redistributable\n```\n",{"name":5,"description":7},"main","a298c929b6cbd662e1fce54302ce520d5ceea1a5",".agents\u002Fskills\u002Fdrug-discovery-literature-bridge","https:\u002F\u002Fgithub.com\u002FPrekzursil\u002Fagent-skills-toolchain","https:\u002F\u002Fgithub.com\u002FPrekzursil","2026-11-02 14:30:57",{"source":8,"skillName":5,"commands":54,"prompt":64,"zipAvailable":65},[55,58,61],{"tool":56,"command":57},"npx","npx skills add Prekzursil\u002Fagent-skills-toolchain --skill drug-discovery-literature-bridge",{"tool":59,"command":60},"bunx","bunx skills add Prekzursil\u002Fagent-skills-toolchain --skill drug-discovery-literature-bridge",{"tool":62,"command":63},"pnpm","pnpm dlx skills add Prekzursil\u002Fagent-skills-toolchain --skill drug-discovery-literature-bridge","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FPrekzursil\u002Fagent-skills-toolchain\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fdrug-discovery-literature-bridge\u002FSKILL.md",true,[],[68,88,113,140,154,170],{"skillNo":69,"name":70,"nameZh":6,"description":71,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":72,"categories":73,"keywords":74,"publishedAt":42},"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",[],[75,76,77,78,79,80,81,82,83,38,37,84,85,86,87],"clarify requirements","clarify","requirements","risk","options","task","prompts","goal","scanning","repo","injection","execution","file",{"skillNo":89,"name":90,"nameZh":6,"description":91,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":92,"categories":93,"keywords":95,"publishedAt":42},"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",[94],{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},[96,97,98,99,100,101,102,36,103,104,105,106,107,108,38,109,110,111,112,30],"openmm md","how","decision","pick","linux","analyze","tool","open","NVIDIA","gpu","license","workflow","windows","free","molecular dynamics","local","do",{"skillNo":114,"name":115,"nameZh":6,"description":116,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":117,"categories":118,"keywords":125,"publishedAt":42},"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",[119,122],{"categoryNo":120,"categoryName":121,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000003","演示文稿",{"categoryNo":123,"categoryName":124,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000008","测试与质量",[126,127,128,129,130,131,132,133,134,135,97,136,137,138,139],"review","visual","visual review","pages","web","false","iteration","take","check","slides","ui","screenshot","playwright","screenshots",{"skillNo":141,"name":142,"nameZh":6,"description":143,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":144,"categories":145,"keywords":146,"publishedAt":42},"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",[],[142,147,148,149,150,151,152,153],"setup","report","project","installation","project setup","issues","state",{"skillNo":155,"name":156,"nameZh":6,"description":157,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":158,"categories":159,"keywords":161,"publishedAt":42},"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",[160],{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},[156,162,80,163,164,165,38,166,167,168,169],"start issue","issue","ready","work on issue","user","work","work on","development",{"skillNo":171,"name":147,"nameZh":6,"description":172,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":173,"categories":174,"keywords":175,"publishedAt":42},"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",[],[147,149,111,176,151,177],"files","interactive"]