[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M7CPCBFWW4KS001DTPKWWVC3":3,"skill-same:01M7CPCBFWW4KS001DTPKWWVC3":63},{"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":18,"publishedAt":19,"linkOnly":20,"markdownContent":21,"markdownBody":22,"frontMatter":23,"refName":43,"commitSha":44,"skillDirectory":45,"forksCount":14,"repositoryHtmlUrl":46,"repositoryHomepageUrl":6,"ownerHtmlUrl":47,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":48,"install":49,"resources":62},"01M7CPCBFWW4KS001DTPKWWVC3","md-document",null,"Converts long-form markdown (specs, RFCs, reports, plans, explainers) into a single-file, lightly-interactive HTML document with sticky TOC, scrollspy, search filter, code-copy buttons, and design-system-driven brand tokens. Triggers when the markdown-html-orchestrator classifies an input as DOCUMENT, or when invoked directly via \u002Fcs:md-document. Reads the design-system config via config_loader.py and inlines the user's 12 derived CSS custom properties; refuses to render if onboarding hasn't run. Single-file output — Google Fonts + Prism.js CDN are the only externals; no framework runtime, no build step. Use after orchestrator routing or after design-system onboarding is confirmed.","azhri1990\u002Fjarvis-mega-repo","azhri1990","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F281207543?v=4","01M7BMC0WHXB2ZA9SH1PFJDYNS","Nashriq Mokhtar","01M7C4WMQVQ450E0NA99BG7BHA",0,"MIT","https:\u002F\u002Fgithub.com\u002Fazhri1990\u002Fjarvis-mega-repo\u002Fblob\u002Fmain\u002Frepos\u002Fproject-genesis\u002Fclaude-skills\u002F.gemini\u002Fskills\u002Fmd-document\u002FSKILL.md",[],[],"2026-11-15 14:19:17",false,"---\nname: md-document\ndescription: Converts long-form markdown (specs, RFCs, reports, plans, explainers) into a single-file, lightly-interactive HTML document with sticky TOC, scrollspy, search filter, code-copy buttons, and design-system-driven brand tokens. Triggers when the markdown-html-orchestrator classifies an input as DOCUMENT, or when invoked directly via \u002Fcs:md-document. Reads the design-system config via config_loader.py and inlines the user's 12 derived CSS custom properties; refuses to render if onboarding hasn't run. Single-file output — Google Fonts + Prism.js CDN are the only externals; no framework runtime, no build step. Use after orchestrator routing or after design-system onboarding is confirmed.\nversion: 2.10.1\nauthor: Alireza Rezvani\nlicense: MIT\ntags: [markdown, html, documentation, single-file, toc, scrollspy, search, code-copy, design-system]\ncompatible_tools: [claude-code, codex-cli, cursor, antigravity, opencode, gemini-cli]\n---\n\n# md-document — Long-form Markdown to HTML\n\nThe general-purpose converter — handles the 90% case Shihipar describes (specs, plans, RFCs, reports, explainers). Three stdlib tools pipeline together:\n\n```\nmarkdown_parser.py  →  html_renderer.py  →  interactivity_injector.py\n   (md → JSON AST)    (AST + tokens → HTML)    (HTML + JS behavior)\n```\n\nOutput is one `.html` file with sticky TOC, search filter, scrollspy, code-copy buttons, and the user's 12 derived brand tokens. Externals limited to Google Fonts CSS + Prism.js CDN.\n\n## When to invoke\n\n| Symptom | Action |\n|---|---|\n| `markdown-html-orchestrator` routes input as DOCUMENT | Invoke this skill |\n| User runs `\u002Fcs:md-document \u003Cpath>.md` directly | Invoke this skill |\n| User says \"convert this spec\u002Freport\u002FRFC\u002Fplan to HTML\" | Invoke this skill |\n| Input is a code review (has ` ```diff ` blocks) | Route to `md-review` instead |\n| Input is a slide deck (clear `---` boundaries) | Route to `md-slides` instead |\n| Input is \u003C 100 lines | Refuse (Shihipar threshold — markdown still wins) |\n| Design-system not onboarded | Refuse, surface `\u002Fcs:design-system` |\n\n## Pipeline\n\n```bash\n# 1. Parse markdown → JSON AST\npython3 markdown-html\u002Fskills\u002Fmd-document\u002Fscripts\u002Fmarkdown_parser.py \\\n    --input \u003Cpath>.md --output sections.json\n\n# 2. Render AST + design-system config → single-file HTML\npython3 markdown-html\u002Fskills\u002Fmd-document\u002Fscripts\u002Fhtml_renderer.py \\\n    --sections sections.json --output document.html\n\n# 3. Inject lightweight JS (search, copycode, smoothscroll, scrollspy)\npython3 markdown-html\u002Fskills\u002Fmd-document\u002Fscripts\u002Finteractivity_injector.py \\\n    --file document.html \\\n    --features search,copycode,smoothscroll,scrollspy\n```\n\nOr all-in-one (sample render):\n\n```bash\npython3 markdown-html\u002Fskills\u002Fmd-document\u002Fscripts\u002Fhtml_renderer.py --sample \\\n  | python3 markdown-html\u002Fskills\u002Fmd-document\u002Fscripts\u002Finteractivity_injector.py \\\n      --file \u002Fdev\u002Fstdin --output document.html\n```\n\n## What gets rendered\n\nCommonMark subset sufficient for agent-generated artifacts:\n- Headings H1-H6 (every H2+ gets an anchor id and TOC entry)\n- Paragraphs with inline **bold** \u002F *italic* \u002F `code` \u002F [links](url) \u002F ![images](url)\n- Fenced code blocks (` ```python `) with Prism.js highlighting on demand\n- GFM tables with per-column alignment\n- GFM callouts (`> [!NOTE]`, `> [!TIP]`, `> [!IMPORTANT]`, `> [!WARNING]`, `> [!CAUTION]`)\n- Blockquotes, ordered + unordered lists (single-level), horizontal rules\n\nOut of scope: nested lists, HTML inlines, footnotes, definition lists, task list checkboxes (rendered as plain text), reference-style links.\n\n## Hard rules\n\n1. **Refuses input \u003C 100 lines.** Markdown wins below the threshold (Shihipar).\n2. **Refuses without onboarding.** `config_loader.setup_completed()` must return `True`. Otherwise surface `\u002Fcs:design-system`.\n3. **Single-file output.** All CSS + JS inline. Only externals are `fonts.googleapis.com` and `cdn.jsdelivr.net` (Prism). Anything else is a regression.\n4. **Customization must change behavior.** `design_style=editorial` produces 720px-wide layout with 1.75 line-height; `playful` rounds the callouts and adds shadow; `technical` is dense with 0.875rem code. Smoke-tested.\n5. **WCAG-compliant tokens.** Inherits the design-system's WCAG AA palette — body text ≥ 4.5:1 contrast, links iteratively walked to 4.5:1.\n6. **Idempotent injection.** Re-injecting interactivity is a no-op (marker check). Re-rendering with a different design_style works cleanly.\n\n## Forcing-question library (Matt Pocock grill discipline)\n\n1. **What's the document for — skim, decide, or deep-read?** Recommended: name it; density follows. Canon: Shihipar; Tufte *Envisioning Information*.\n2. **Sticky-sidebar TOC or collapsible-top?** Recommended: sticky-sidebar for > 800 words \u002F 4+ H2s; collapsible-top for shorter mobile-first docs. Canon: NN\u002Fg *TOC Best Practices* (2023).\n3. **All four interactive features, or a subset?** Recommended: all four — none of them cost more than ~1 KB. Canon: Wattenberger *Why React isn't great for actually building websites*.\n4. **Code theme — light, dark, or auto?** Recommended: auto (follows OS `prefers-color-scheme`). Canon: WCAG 2.2 §1.4.3.\n5. **Does the document have a clear H1 title?** Recommended: yes — H1 becomes the page `\u003Ctitle>` and is excluded from the TOC.\n\n## Distinct from\n\n- **`md-review`** — that converter renders diff blocks + severity-tagged margin annotations. This one renders prose + tables + code + callouts.\n- **`md-slides`** — that converter splits on `---` boundaries into slides. This one renders one continuous document.\n- **`marketing\u002Flanding\u002F`** — that generates landing pages from scratch (no markdown input). This converts existing markdown.\n\n## Output artifact\n\n`{default_output_dir}\u002Fdoc-{slug}.html` (path resolved by orchestrator's `output_path_resolver.py`; collision suffix `-2`, `-3`, … by default).\n\n## References\n\n- Shihipar — *Claude Code HTML output* (Medium, 2026)\n- Tufte — *Envisioning Information* (1990), ch. 2 \"Micro\u002FMacro Readings\"\n- NN\u002Fg — *Table of Contents Best Practices* (2023)\n- WCAG 2.2 — §1.4.3 contrast, §2.4.5 multiple ways\n- Wattenberger — *Why React isn't great for actually building websites*\n- See `references\u002F` for full citations\n","# md-document — Long-form Markdown to HTML\n\nThe general-purpose converter — handles the 90% case Shihipar describes (specs, plans, RFCs, reports, explainers). Three stdlib tools pipeline together:\n\n```\nmarkdown_parser.py  →  html_renderer.py  →  interactivity_injector.py\n   (md → JSON AST)    (AST + tokens → HTML)    (HTML + JS behavior)\n```\n\nOutput is one `.html` file with sticky TOC, search filter, scrollspy, code-copy buttons, and the user's 12 derived brand tokens. Externals limited to Google Fonts CSS + Prism.js CDN.\n\n## When to invoke\n\n| Symptom | Action |\n|---|---|\n| `markdown-html-orchestrator` routes input as DOCUMENT | Invoke this skill |\n| User runs `\u002Fcs:md-document \u003Cpath>.md` directly | Invoke this skill |\n| User says \"convert this spec\u002Freport\u002FRFC\u002Fplan to HTML\" | Invoke this skill |\n| Input is a code review (has ` ```diff ` blocks) | Route to `md-review` instead |\n| Input is a slide deck (clear `---` boundaries) | Route to `md-slides` instead |\n| Input is \u003C 100 lines | Refuse (Shihipar threshold — markdown still wins) |\n| Design-system not onboarded | Refuse, surface `\u002Fcs:design-system` |\n\n## Pipeline\n\n```bash\n# 1. Parse markdown → JSON AST\npython3 markdown-html\u002Fskills\u002Fmd-document\u002Fscripts\u002Fmarkdown_parser.py \\\n    --input \u003Cpath>.md --output sections.json\n\n# 2. Render AST + design-system config → single-file HTML\npython3 markdown-html\u002Fskills\u002Fmd-document\u002Fscripts\u002Fhtml_renderer.py \\\n    --sections sections.json --output document.html\n\n# 3. Inject lightweight JS (search, copycode, smoothscroll, scrollspy)\npython3 markdown-html\u002Fskills\u002Fmd-document\u002Fscripts\u002Finteractivity_injector.py \\\n    --file document.html \\\n    --features search,copycode,smoothscroll,scrollspy\n```\n\nOr all-in-one (sample render):\n\n```bash\npython3 markdown-html\u002Fskills\u002Fmd-document\u002Fscripts\u002Fhtml_renderer.py --sample \\\n  | python3 markdown-html\u002Fskills\u002Fmd-document\u002Fscripts\u002Finteractivity_injector.py \\\n      --file \u002Fdev\u002Fstdin --output document.html\n```\n\n## What gets rendered\n\nCommonMark subset sufficient for agent-generated artifacts:\n- Headings H1-H6 (every H2+ gets an anchor id and TOC entry)\n- Paragraphs with inline **bold** \u002F *italic* \u002F `code` \u002F [links](url) \u002F ![images](url)\n- Fenced code blocks (` ```python `) with Prism.js highlighting on demand\n- GFM tables with per-column alignment\n- GFM callouts (`> [!NOTE]`, `> [!TIP]`, `> [!IMPORTANT]`, `> [!WARNING]`, `> [!CAUTION]`)\n- Blockquotes, ordered + unordered lists (single-level), horizontal rules\n\nOut of scope: nested lists, HTML inlines, footnotes, definition lists, task list checkboxes (rendered as plain text), reference-style links.\n\n## Hard rules\n\n1. **Refuses input \u003C 100 lines.** Markdown wins below the threshold (Shihipar).\n2. **Refuses without onboarding.** `config_loader.setup_completed()` must return `True`. Otherwise surface `\u002Fcs:design-system`.\n3. **Single-file output.** All CSS + JS inline. Only externals are `fonts.googleapis.com` and `cdn.jsdelivr.net` (Prism). Anything else is a regression.\n4. **Customization must change behavior.** `design_style=editorial` produces 720px-wide layout with 1.75 line-height; `playful` rounds the callouts and adds shadow; `technical` is dense with 0.875rem code. Smoke-tested.\n5. **WCAG-compliant tokens.** Inherits the design-system's WCAG AA palette — body text ≥ 4.5:1 contrast, links iteratively walked to 4.5:1.\n6. **Idempotent injection.** Re-injecting interactivity is a no-op (marker check). Re-rendering with a different design_style works cleanly.\n\n## Forcing-question library (Matt Pocock grill discipline)\n\n1. **What's the document for — skim, decide, or deep-read?** Recommended: name it; density follows. Canon: Shihipar; Tufte *Envisioning Information*.\n2. **Sticky-sidebar TOC or collapsible-top?** Recommended: sticky-sidebar for > 800 words \u002F 4+ H2s; collapsible-top for shorter mobile-first docs. Canon: NN\u002Fg *TOC Best Practices* (2023).\n3. **All four interactive features, or a subset?** Recommended: all four — none of them cost more than ~1 KB. Canon: Wattenberger *Why React isn't great for actually building websites*.\n4. **Code theme — light, dark, or auto?** Recommended: auto (follows OS `prefers-color-scheme`). Canon: WCAG 2.2 §1.4.3.\n5. **Does the document have a clear H1 title?** Recommended: yes — H1 becomes the page `\u003Ctitle>` and is excluded from the TOC.\n\n## Distinct from\n\n- **`md-review`** — that converter renders diff blocks + severity-tagged margin annotations. This one renders prose + tables + code + callouts.\n- **`md-slides`** — that converter splits on `---` boundaries into slides. This one renders one continuous document.\n- **`marketing\u002Flanding\u002F`** — that generates landing pages from scratch (no markdown input). This converts existing markdown.\n\n## Output artifact\n\n`{default_output_dir}\u002Fdoc-{slug}.html` (path resolved by orchestrator's `output_path_resolver.py`; collision suffix `-2`, `-3`, … by default).\n\n## References\n\n- Shihipar — *Claude Code HTML output* (Medium, 2026)\n- Tufte — *Envisioning Information* (1990), ch. 2 \"Micro\u002FMacro Readings\"\n- NN\u002Fg — *Table of Contents Best Practices* (2023)\n- WCAG 2.2 — §1.4.3 contrast, §2.4.5 multiple ways\n- Wattenberger — *Why React isn't great for actually building websites*\n- See `references\u002F` for full citations\n",{"name":5,"tags":24,"author":34,"license":15,"version":35,"description":7,"compatible_tools":36},[25,26,27,28,29,30,31,32,33],"markdown","html","documentation","single-file","toc","scrollspy","search","code-copy","design-system","Alireza Rezvani","2.10.1",[37,38,39,40,41,42],"claude-code","codex-cli","cursor","antigravity","opencode","gemini-cli","main","46d43f3edecde97283ac410e9051444d90120cfb","repos\u002Fproject-genesis\u002Fclaude-skills\u002F.gemini\u002Fskills\u002Fmd-document","https:\u002F\u002Fgithub.com\u002Fazhri1990\u002Fjarvis-mega-repo","https:\u002F\u002Fgithub.com\u002Fazhri1990","2026-10-10 07:58:58",{"source":8,"skillName":5,"commands":50,"prompt":60,"zipAvailable":61},[51,54,57],{"tool":52,"command":53},"npx","npx skills add azhri1990\u002Fjarvis-mega-repo --skill md-document",{"tool":55,"command":56},"bunx","bunx skills add azhri1990\u002Fjarvis-mega-repo --skill md-document",{"tool":58,"command":59},"pnpm","pnpm dlx skills add azhri1990\u002Fjarvis-mega-repo --skill md-document","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fazhri1990\u002Fjarvis-mega-repo\u002Fblob\u002Fmain\u002Frepos\u002Fproject-genesis\u002Fclaude-skills\u002F.gemini\u002Fskills\u002Fmd-document\u002FSKILL.md",true,[],[64,71,78,85,92,99],{"skillNo":65,"name":66,"nameZh":6,"description":67,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":68,"categories":69,"keywords":70,"publishedAt":19},"01M7CYPF9V9EMA4ZXS1TTCMBFH","syllabus","Generates a curated supplementary reading list from any course syllabus using Consensus academic search. Grill-me intake (syllabus input format + course audience + year range) plus a grouping forcing-options checkpoint before any search runs — so the reading list matches the course's level and recency need. Parses the syllabus to extract topics and learning outcomes, searches Consensus for recent peer-reviewed papers per topic, and produces a professionally formatted .docx with clickable Consensus links, plain-language summaries calibrated to audience level, and Bloom-higher-order discussion questions tied to course learning goals. Use when the user uploads a syllabus, course outline, or curriculum document and wants supplementary readings (e.g., 'create a reading list from this syllabus', 'find recent papers for my course') — even casual mentions with a syllabus attached should trigger this skill.","https:\u002F\u002Fgithub.com\u002Fazhri1990\u002Fjarvis-mega-repo\u002Fblob\u002Fmain\u002Frepos\u002Fproject-genesis\u002Fclaude-skills\u002Fresearch\u002Fsyllabus\u002Fskills\u002Fsyllabus\u002FSKILL.md",[],[],{"skillNo":72,"name":73,"nameZh":6,"description":74,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":75,"categories":76,"keywords":77,"publishedAt":19},"01M7CYNRJDTQCRF5KGPMBMSB1S","pulse","Multi-source recency research skill that takes the pulse of any topic across Reddit, Hacker News, the open web, and optionally X\u002FTwitter within a configurable recent window (default 30 days). Forcing intake clarifies topic specificity, angle (trend\u002Fsentiment\u002Fproblems\u002Fopportunities\u002Fcomparison), time window, and platform scope before searching. Returns a synthesized briefing with citations, engagement metrics, and cross-platform pattern analysis. Use when the user requests multi-source recency intelligence on a topic (e.g., 'pulse on [topic]', 'what's happening with [topic]', 'what are people saying about [topic]', 'current conversation about [topic]', 'take the pulse of [topic]', 'trending: [topic]', 'find me info on [topic]'), and for competitor research, trend discovery, tool comparisons, and audience sentiment analysis.","https:\u002F\u002Fgithub.com\u002Fazhri1990\u002Fjarvis-mega-repo\u002Fblob\u002Fmain\u002Frepos\u002Fproject-genesis\u002Fclaude-skills\u002Fresearch\u002Fpulse\u002Fskills\u002Fpulse\u002FSKILL.md",[],[],{"skillNo":79,"name":80,"nameZh":6,"description":81,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":82,"categories":83,"keywords":84,"publishedAt":19},"01M7CYNAVSE84QD7HAWZWTY2FS","patent","Patent prior-art and landscape intelligence skill — not generic patent help. Commits to one of five sub-use-cases via forcing intake (novelty search \u002F freedom-to-operate \u002F competitive landscape \u002F acquisition diligence \u002F litigation prior-art) before any search runs. Searches Google Patents, Espacenet, USPTO, and optionally Lens.org for citation-graph signals. Output is an editable Word document (.docx) with verdict, ranked closest art (claim-text extracted), CPC-class-aware landscape, family-resolved hits, geographic coverage, FTO flags where applicable, strategy recommendations, and full audit log. Use when the user asks for patent searching or analysis (e.g., 'prior art search for [invention]', 'freedom to operate analysis for [product]'). Produces search signal, not legal advice — always recommends consulting a patent attorney before filing or licensing decisions. Trademark, copyright, and trade-secret questions are out of scope.","https:\u002F\u002Fgithub.com\u002Fazhri1990\u002Fjarvis-mega-repo\u002Fblob\u002Fmain\u002Frepos\u002Fproject-genesis\u002Fclaude-skills\u002Fresearch\u002Fpatent\u002Fskills\u002Fpatent\u002FSKILL.md",[],[],{"skillNo":86,"name":87,"nameZh":6,"description":88,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":89,"categories":90,"keywords":91,"publishedAt":19},"01M7CYN0CE8P5ZN7WV2X06HKPC","notebooklm","Browser automation skill for controlling Google's NotebookLM. Use when the user wants anything done in NotebookLM (e.g., 'open NotebookLM', 'check my [name] notebook', 'ask my notebook about X', 'add [source] to NotebookLM', 'generate a Video Overview from my notebook', 'use NotebookLM Studio'). Handles reading and querying notebooks, adding sources (URLs, text, files, YouTube links, synthesized content), generating Studio outputs (Audio\u002FVideo Overviews, Mind Maps, Reports incl. Briefing Doc\u002FStudy Guide\u002FFAQ, Flashcards, Quiz, slide decks, infographics — discover the exact set from the live Studio panel; the UI evolves fast), and creating new notebooks. Requires browser automation environment — fails gracefully when unavailable.","https:\u002F\u002Fgithub.com\u002Fazhri1990\u002Fjarvis-mega-repo\u002Fblob\u002Fmain\u002Frepos\u002Fproject-genesis\u002Fclaude-skills\u002Fresearch\u002Fnotebooklm\u002Fskills\u002Fnotebooklm\u002FSKILL.md",[],[],{"skillNo":93,"name":94,"nameZh":6,"description":95,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":96,"categories":97,"keywords":98,"publishedAt":19},"01M7CYMHZ3QRJ9BKRB859KW8TQ","litreview","Academic literature orientation skill that searches papers via free keyless APIs (PubMed E-utilities + OpenAlex) by default — with the Consensus MCP as an optional enhancement lane when connected — builds a strategic search plan using PICO (default) or SPIDER \u002F Decomposition \u002F hybrid as fallbacks, and synthesizes findings into a formatted Word (.docx) research guide. Grill-me intake (research question specificity + framework hint + tentative depth) before the recon search; a second forcing checkpoint after Phase 2 confirms framework + sub-areas + depth before searches consume budget. Configurable depth (5\u002F10\u002F20 queries) controls coverage vs. speed. Output is a 'launching pad' — an orientation guide that lets a researcher dive in confidently, not a finished review. Use when the user starts literature-oriented research (e.g., 'litreview on [topic]', 'literature review on [topic]', 'I'm starting a literature review on X', 'I'm writing a paper on X', 'help me research X', 'I'm doing research on X', 'can you help me research X'). Do NOT use for single one-off paper searches wanting a quick list — that's a plain PubMed\u002FOpenAlex (or Consensus) query.","https:\u002F\u002Fgithub.com\u002Fazhri1990\u002Fjarvis-mega-repo\u002Fblob\u002Fmain\u002Frepos\u002Fproject-genesis\u002Fclaude-skills\u002Fresearch\u002Flitreview\u002Fskills\u002Flitreview\u002FSKILL.md",[],[],{"skillNo":100,"name":101,"nameZh":6,"description":102,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":103,"categories":104,"keywords":105,"publishedAt":19},"01M7CYM3D8YKV7R6DT4RNJSF0A","grants","NIH grant research skill for clinical researchers. Grill-me intake (research idea + career stage + preliminary data + environment + submission posture + known institute targets) locks down the funding strategy before any search runs. Runs a 5-facet Consensus positioning analysis (with draft Significance\u002FInnovation language), maps the research to the right NIH institutes and study sections via RePORTER, finds NOSIs and funded overlap, and produces an editable Word document (.docx) with budget\u002Fscope-aware mechanism recommendations, submission timelines, and a mandatory program officer recommendation. Use when the user asks about research funding or makes any grant-related request (e.g., 'grants for [topic]', 'find grants for my research idea', 'what grants match my research', 'help me find NIH funding', 'grant opportunities for my research'). NIH-only scope — non-NIH funders (PCORI, DOD CDMRP, VA, foundations) are out of scope and flagged at intake.","https:\u002F\u002Fgithub.com\u002Fazhri1990\u002Fjarvis-mega-repo\u002Fblob\u002Fmain\u002Frepos\u002Fproject-genesis\u002Fclaude-skills\u002Fresearch\u002Fgrants\u002Fskills\u002Fgrants\u002FSKILL.md",[],[]]