[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6DCT553TD1941WT51CFJXHZ":3,"skill-same:01M6DCT553TD1941WT51CFJXHZ":81},{"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":29,"publishedAt":50,"linkOnly":51,"markdownContent":52,"markdownBody":53,"frontMatter":54,"refName":55,"commitSha":56,"skillDirectory":57,"forksCount":14,"repositoryHtmlUrl":58,"repositoryHomepageUrl":6,"ownerHtmlUrl":59,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":60,"install":61,"resources":74},"01M6DCT553TD1941WT51CFJXHZ","pdf-reader",null,"Read a PDF and ground every extracted claim in a real source span (cited_text plus a page or char location) rather than a model-invented citation. Two paths, use together, the native Anthropic Citations API (attach the PDF as a document content block with citations enabled, so cited claims carry cited_text plus page_location) and the pdf-reader MCP server (read_pdf, search_pdf, pdf_evidence) for literal page and region evidence. Use when a question over a PDF needs defensible, quotable answers, when summarizing or extracting facts from a report or paper or contract, or when a claim about a PDF must point at the exact page and text it came from. Do NOT use for bulk OCR of scans (use ocr-suite) or for academic metadata and access tagging (use scholarly-literature-review).","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\u002Fpdf-reader\u002FSKILL.md",[18,23,26],{"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},"01JBSEEDCAT000000000000010","后端与接口",{"categoryNo":27,"categoryName":28,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",[30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49],"pdf reader","reader","PDF","question","as","contract","claim","do","Literal","document","evidence","use","page","model","access","ocr","Citations","search","mcp server","MCP","2026-11-10 10:12:30",false,"---\nname: pdf-reader\ndescription: Read a PDF and ground every extracted claim in a real source span (cited_text plus a page or char location) rather than a model-invented citation. Two paths, use together, the native Anthropic Citations API (attach the PDF as a document content block with citations enabled, so cited claims carry cited_text plus page_location) and the pdf-reader MCP server (read_pdf, search_pdf, pdf_evidence) for literal page and region evidence. Use when a question over a PDF needs defensible, quotable answers, when summarizing or extracting facts from a report or paper or contract, or when a claim about a PDF must point at the exact page and text it came from. Do NOT use for bulk OCR of scans (use ocr-suite) or for academic metadata and access tagging (use scholarly-literature-review).\n---\n\n# PDF Reader (citation-grounded)\n\nRead a PDF and make every claim **provably traceable to a span in the source** —\na verbatim `cited_text` plus a page (or character) location — instead of a\nmodel-generated `[1]` that maps to nothing. Two complementary paths; prefer to\nrun both so the API's grounding and the MCP's literal evidence corroborate.\n\n> **`pdf-reader` is an MCP server, not a workflow you reimplement.** The\n> `mcp__pdf-reader__*` tools do the extraction; this skill is the convention for\n> *grounding* what they return. The native Citations API is the anti-hallucination\n> guarantee — a cited span provably came from the PDF, not from the model.\n\n## When to Activate\n\n- Any Q&A, summary, or fact-extraction over a PDF where the answer must be defensible.\n- \"What does this report\u002Fpaper\u002Fcontract say about X\" and you must quote the exact page.\n- Building a cited claim list from a PDF (findings, obligations, figures, dates).\n- A downstream consumer (deep-research, a review) needs per-claim `cited_text` + location.\n\nDo **not** use for: bulk OCR of image-only scans (use `ocr-suite`); academic\nmetadata \u002F access classification \u002F citation graphs (use `scholarly-literature-review`\nor `scholarly-access`); pure page-image rendering with no claim to ground.\n\n## Path A — Native Citations API (the grounding guarantee)\n\nAttach the PDF as a `document` content block with **`citations: {enabled: true}`**.\nThe response splits into `text` blocks; every cited block carries a `citations`\narray whose entries are real spans from the PDF.\n\n### Path B — pdf-reader MCP (literal page + region evidence)\n### Workflow\n### Quality Rules\n### Pairs with\n","# PDF Reader (citation-grounded)\n\nRead a PDF and make every claim **provably traceable to a span in the source** —\na verbatim `cited_text` plus a page (or character) location — instead of a\nmodel-generated `[1]` that maps to nothing. Two complementary paths; prefer to\nrun both so the API's grounding and the MCP's literal evidence corroborate.\n\n> **`pdf-reader` is an MCP server, not a workflow you reimplement.** The\n> `mcp__pdf-reader__*` tools do the extraction; this skill is the convention for\n> *grounding* what they return. The native Citations API is the anti-hallucination\n> guarantee — a cited span provably came from the PDF, not from the model.\n\n## When to Activate\n\n- Any Q&A, summary, or fact-extraction over a PDF where the answer must be defensible.\n- \"What does this report\u002Fpaper\u002Fcontract say about X\" and you must quote the exact page.\n- Building a cited claim list from a PDF (findings, obligations, figures, dates).\n- A downstream consumer (deep-research, a review) needs per-claim `cited_text` + location.\n\nDo **not** use for: bulk OCR of image-only scans (use `ocr-suite`); academic\nmetadata \u002F access classification \u002F citation graphs (use `scholarly-literature-review`\nor `scholarly-access`); pure page-image rendering with no claim to ground.\n\n## Path A — Native Citations API (the grounding guarantee)\n\nAttach the PDF as a `document` content block with **`citations: {enabled: true}`**.\nThe response splits into `text` blocks; every cited block carries a `citations`\narray whose entries are real spans from the PDF.\n\n### Path B — pdf-reader MCP (literal page + region evidence)\n### Workflow\n### Quality Rules\n### Pairs with\n",{"name":5,"description":7},"main","a298c929b6cbd662e1fce54302ce520d5ceea1a5","platform\u002Frouting\u002Ffixtures\u002Fhome\u002F.agents\u002Fskills\u002Fpdf-reader","https:\u002F\u002Fgithub.com\u002FPrekzursil\u002Fagent-skills-toolchain","https:\u002F\u002Fgithub.com\u002FPrekzursil","2026-11-02 14:30:57",{"source":8,"skillName":5,"commands":62,"prompt":72,"zipAvailable":73},[63,66,69],{"tool":64,"command":65},"npx","npx skills add Prekzursil\u002Fagent-skills-toolchain --skill pdf-reader",{"tool":67,"command":68},"bunx","bunx skills add Prekzursil\u002Fagent-skills-toolchain --skill pdf-reader",{"tool":70,"command":71},"pnpm","pnpm dlx skills add Prekzursil\u002Fagent-skills-toolchain --skill pdf-reader","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\u002Fpdf-reader\u002FSKILL.md",true,[75],{"path":76,"resourceType":77,"resourceTypeDesc":78,"sizeBytes":79,"sourceHtmlUrl":80},"agents\u002Fopenai.yaml","REFERENCE","参考文档",113,"https:\u002F\u002Fgithub.com\u002FAGI4Sci\u002FSciForge\u002Fblob\u002Fmain\u002F.codex\u002Fskills\u002Fgrilling\u002Fagents\u002Fopenai.yaml",[82,103,128,155,169,185],{"skillNo":83,"name":84,"nameZh":6,"description":85,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":86,"categories":87,"keywords":88,"publishedAt":50},"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",[],[89,90,91,92,93,94,95,96,97,41,98,99,100,101,102],"clarify requirements","clarify","requirements","risk","options","task","prompts","goal","scanning","context","repo","injection","execution","file",{"skillNo":104,"name":105,"nameZh":6,"description":106,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":107,"categories":108,"keywords":110,"publishedAt":50},"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",[109],{"categoryNo":27,"categoryName":28,"taxonomyType":21,"taxonomyTypeDesc":22},[111,112,113,114,115,116,117,118,119,120,121,122,123,124,41,125,126,127,37,49],"openmm md","how","decision","pick","linux","analyze","tool","stack","open","NVIDIA","gpu","license","workflow","windows","free","molecular dynamics","local",{"skillNo":129,"name":130,"nameZh":6,"description":131,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":132,"categories":133,"keywords":140,"publishedAt":50},"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",[134,137],{"categoryNo":135,"categoryName":136,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000003","演示文稿",{"categoryNo":138,"categoryName":139,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000008","测试与质量",[141,142,143,144,145,146,147,148,149,150,112,151,152,153,154],"review","visual","visual review","pages","web","false","iteration","take","check","slides","ui","screenshot","playwright","screenshots",{"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":161,"publishedAt":50},"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",[],[157,162,163,164,165,166,167,168],"setup","report","project","installation","project setup","issues","state",{"skillNo":170,"name":171,"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":176,"publishedAt":50},"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",[175],{"categoryNo":27,"categoryName":28,"taxonomyType":21,"taxonomyTypeDesc":22},[171,177,94,178,179,180,41,181,182,183,184],"start issue","issue","ready","work on issue","user","work","work on","development",{"skillNo":186,"name":162,"nameZh":6,"description":187,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":188,"categories":189,"keywords":190,"publishedAt":50},"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",[],[162,164,127,191,166,192],"files","interactive"]