[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6DCFYRRTXFMR6N9E57HBRJ4":3,"skill-same:01M6DCFYRRTXFMR6N9E57HBRJ4":72},{"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":47,"linkOnly":48,"markdownContent":49,"markdownBody":50,"frontMatter":51,"refName":52,"commitSha":53,"skillDirectory":54,"forksCount":14,"repositoryHtmlUrl":55,"repositoryHomepageUrl":6,"ownerHtmlUrl":56,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":57,"install":58,"resources":71},"01M6DCFYRRTXFMR6N9E57HBRJ4","consensus-tooluniverse-research",null,"Use when downloading or systematically harvesting research from Consensus.app — extract each shared search browser-free from the page __NEXT_DATA__ payload, resolve every cited paper to a DOI and open-access PDF with ToolUniverse (Crossref, OpenAlex, Unpaywall, EuropePMC), organize by topic with a DOI-linked bibliography, capture the Consensus Meter graphs, and verify headline claims against primary sources. Bundles paste-ready AI-scope and user-scope instructions for mechanistic neuropharmacology and harm-reduction research.","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\u002Fplatform\u002Frouting\u002Ffixtures\u002Fhome\u002F.agents\u002Fskills\u002Fconsensus-tooluniverse-research\u002FSKILL.md",[18,23],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000002","文档处理","SCENARIO","使用场景",{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000021","资料检索与研究",[27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46],"research","tooluniverse","consensus tooluniverse research","consensus","verify","open","access","page","search","instructions","use","payload","ready","next","shared","paper","app","free","browser","PDF","2026-11-10 10:12:30",false,"---\nname: consensus-tooluniverse-research\ndescription: Use when downloading or systematically harvesting research from Consensus.app — extract each shared search browser-free from the page __NEXT_DATA__ payload, resolve every cited paper to a DOI and open-access PDF with ToolUniverse (Crossref, OpenAlex, Unpaywall, EuropePMC), organize by topic with a DOI-linked bibliography, capture the Consensus Meter graphs, and verify headline claims against primary sources. Bundles paste-ready AI-scope and user-scope instructions for mechanistic neuropharmacology and harm-reduction research.\n---\n\n# Consensus + ToolUniverse Research Harvest\n\nTurn Consensus.app searches into an organized, DOI-linked, open-access paper library — deterministically\nand without a browser — then verify the claims against primary literature with ToolUniverse.\n\n**Why two tools:** Consensus gives the *human synthesis* (meter, claims-evidence tables) but keys papers by\nan internal `hash_paper_id` with **no DOI**. ToolUniverse gives the *machine provenance* (DOI → OA full-text).\nThey compose; neither alone is enough. Full rationale: `references\u002FBEST-METHOD.md`.\n\n---\n\n## When to use\n- \"Download \u002F save these Consensus searches as text + the papers + the graph.\"\n- \"Build me a paper library on \u003Ctopic> with PDFs and a bibliography.\"\n- Any systematic literature harvest where you want primary-source PDFs + provenance, not just a summary.\n\n## Prerequisites\n- **ToolUniverse** installed (`import tooluniverse`; `tu.load_tools()` before any call). `pip install requests tooluniverse` (or run with the ToolUniverse venv).\n- Consensus shared-search URLs (`https:\u002F\u002Fconsensus.app\u002Fsearch\u002F\u003Cslug>\u002F\u003Cthread_id>\u002F`) — public, no login.\n- **Config (no hardcoding — the scripts read these):** `CONSENSUS_WS` = workspace dir (or `--ws`, default cwd);\n  `UNPAYWALL_EMAIL` = your contact email (or `--email`; **required** by Unpaywall\u002FOpenAlex, fails fast if unset).\n\n## Run\n```bash\nexport CONSENSUS_WS=\u002Fpath\u002Fto\u002Fworkspace\nexport UNPAYWALL_EMAIL=you@example.com\npython scripts\u002Fextract_consensus.py --urls searches.txt   # one Consensus URL per line, OR pass URLs as args\n\n### Pipeline (3 stages — scripts in `scripts\u002F`)\n### Stage 1 — extract text + paper list  (`scripts\u002Fextract_consensus.py`)\n### Stage 2 — resolve + download papers, OA-only  (`scripts\u002Fresolve_and_download.py`)\n### Stage 3 — the graph (Consensus Meter)\n### ToolUniverse tool reference (this pipeline)\n### Verify — the Consensus `analysis` is an UNVERIFIED secondary claim\n### Caveats (calibrated on real runs)\n### Output layout\n### Research conduct\n","# Consensus + ToolUniverse Research Harvest\n\nTurn Consensus.app searches into an organized, DOI-linked, open-access paper library — deterministically\nand without a browser — then verify the claims against primary literature with ToolUniverse.\n\n**Why two tools:** Consensus gives the *human synthesis* (meter, claims-evidence tables) but keys papers by\nan internal `hash_paper_id` with **no DOI**. ToolUniverse gives the *machine provenance* (DOI → OA full-text).\nThey compose; neither alone is enough. Full rationale: `references\u002FBEST-METHOD.md`.\n\n---\n\n## When to use\n- \"Download \u002F save these Consensus searches as text + the papers + the graph.\"\n- \"Build me a paper library on \u003Ctopic> with PDFs and a bibliography.\"\n- Any systematic literature harvest where you want primary-source PDFs + provenance, not just a summary.\n\n## Prerequisites\n- **ToolUniverse** installed (`import tooluniverse`; `tu.load_tools()` before any call). `pip install requests tooluniverse` (or run with the ToolUniverse venv).\n- Consensus shared-search URLs (`https:\u002F\u002Fconsensus.app\u002Fsearch\u002F\u003Cslug>\u002F\u003Cthread_id>\u002F`) — public, no login.\n- **Config (no hardcoding — the scripts read these):** `CONSENSUS_WS` = workspace dir (or `--ws`, default cwd);\n  `UNPAYWALL_EMAIL` = your contact email (or `--email`; **required** by Unpaywall\u002FOpenAlex, fails fast if unset).\n\n## Run\n```bash\nexport CONSENSUS_WS=\u002Fpath\u002Fto\u002Fworkspace\nexport UNPAYWALL_EMAIL=you@example.com\npython scripts\u002Fextract_consensus.py --urls searches.txt   # one Consensus URL per line, OR pass URLs as args\n\n### Pipeline (3 stages — scripts in `scripts\u002F`)\n### Stage 1 — extract text + paper list  (`scripts\u002Fextract_consensus.py`)\n### Stage 2 — resolve + download papers, OA-only  (`scripts\u002Fresolve_and_download.py`)\n### Stage 3 — the graph (Consensus Meter)\n### ToolUniverse tool reference (this pipeline)\n### Verify — the Consensus `analysis` is an UNVERIFIED secondary claim\n### Caveats (calibrated on real runs)\n### Output layout\n### Research conduct\n",{"name":5,"description":7},"main","a298c929b6cbd662e1fce54302ce520d5ceea1a5","platform\u002Frouting\u002Ffixtures\u002Fhome\u002F.agents\u002Fskills\u002Fconsensus-tooluniverse-research","https:\u002F\u002Fgithub.com\u002FPrekzursil\u002Fagent-skills-toolchain","https:\u002F\u002Fgithub.com\u002FPrekzursil","2026-11-02 14:30:57",{"source":8,"skillName":5,"commands":59,"prompt":69,"zipAvailable":70},[60,63,66],{"tool":61,"command":62},"npx","npx skills add Prekzursil\u002Fagent-skills-toolchain --skill consensus-tooluniverse-research",{"tool":64,"command":65},"bunx","bunx skills add Prekzursil\u002Fagent-skills-toolchain --skill consensus-tooluniverse-research",{"tool":67,"command":68},"pnpm","pnpm dlx skills add Prekzursil\u002Fagent-skills-toolchain --skill consensus-tooluniverse-research","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FPrekzursil\u002Fagent-skills-toolchain\u002Fblob\u002Fmain\u002Fplatform\u002Frouting\u002Ffixtures\u002Fhome\u002F.agents\u002Fskills\u002Fconsensus-tooluniverse-research\u002FSKILL.md",true,[],[73,94,121,148,162,177],{"skillNo":74,"name":75,"nameZh":6,"description":76,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":77,"categories":78,"keywords":79,"publishedAt":47},"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",[],[80,81,82,83,84,85,86,87,88,37,89,90,91,92,93],"clarify requirements","clarify","requirements","risk","options","task","prompts","goal","scanning","context","repo","injection","execution","file",{"skillNo":95,"name":96,"nameZh":6,"description":97,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":98,"categories":99,"keywords":103,"publishedAt":47},"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",[100],{"categoryNo":101,"categoryName":102,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",[104,105,106,107,108,109,110,111,32,112,113,114,115,116,37,44,117,118,119,120],"openmm md","how","decision","pick","linux","analyze","tool","stack","NVIDIA","gpu","license","workflow","windows","molecular dynamics","local","do","MCP",{"skillNo":122,"name":123,"nameZh":6,"description":124,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":125,"categories":126,"keywords":133,"publishedAt":47},"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",[127,130],{"categoryNo":128,"categoryName":129,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000003","演示文稿",{"categoryNo":131,"categoryName":132,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000008","测试与质量",[134,135,136,137,138,139,140,141,142,143,105,144,145,146,147],"review","visual","visual review","pages","web","false","iteration","take","check","slides","ui","screenshot","playwright","screenshots",{"skillNo":149,"name":150,"nameZh":6,"description":151,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":152,"categories":153,"keywords":154,"publishedAt":47},"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",[],[150,155,156,157,158,159,160,161],"setup","report","project","installation","project setup","issues","state",{"skillNo":163,"name":164,"nameZh":6,"description":165,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":166,"categories":167,"keywords":169,"publishedAt":47},"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",[168],{"categoryNo":101,"categoryName":102,"taxonomyType":21,"taxonomyTypeDesc":22},[164,170,85,171,39,172,37,173,174,175,176],"start issue","issue","work on issue","user","work","work on","development",{"skillNo":178,"name":155,"nameZh":6,"description":179,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":180,"categories":181,"keywords":182,"publishedAt":47},"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",[],[155,157,118,183,159,184],"files","interactive"]