— name: docs-seeker description: "Searching internet for technical documentation using llms.txt standard, GitHub repositories via Repomix, and parallel exploration. Use when user needs: (1) Latest documentation for libraries/frameworks, (2) Documentation in llms.txt format, (3) GitHub repository analysis, (4) Documentation without direct llms.txt support, (5) Multiple documentation sources in parallel" version: 1.0.0 — # Documentation Discovery & Analysis ## Overview Intelligent discovery and analysis of technical documentation through multiple strategies: 1. **llms.txt-first**: Search for standardized AI-friendly documentation 2. **Repository analysis**: Use Repomix to analyze GitHub repositories 3. **Parallel exploration**: Deploy multiple Explorer agents for comprehensive coverage 4. **Fallback research**: Use Researcher agents when other methods unavailable ## Core Workflow ### Phase 1: Initial Discovery 1. **Identify target** – Extract library/framework name from user request – Note version requirements (default: latest) – Clarify scope if ambiguous – Identify if target is GitHub repository or website 2. **Search for llms.txt (PRIORITIZE context7.com)** **First: Try context7.com patterns** For GitHub repositories: “` Pattern: https://context7.com/{org}/{repo}/llms.txt Examples: – https://github.com/imagick/imagick → https://context7.com/imagick/imagick/llms.txt – https://github.com/vercel/next.js → https://context7.com/vercel/next.js/llms.txt – https://github.com/better-auth/better-auth → https://context7.com/better-auth/better-auth/llms.txt “` For websites: “` Pattern: https://context7.com/websites/{normalized-domain-path}/llms.txt Examples: – https://docs.imgix.com/ → https://context7.com/websites/imgix/llms.txt – https://docs.byteplus.com/en/docs/ModelArk/ → https://context7.com/websites/byteplus_en_modelark/llms.txt – https://docs.haystack.deepset.ai/docs → https://context7.com/websites/haystack_deepset_ai/llms.txt – https://ffmpeg.org/doxygen/8.0/ → https://context7.com/websites/ffmpeg_doxygen_8_0/llms.txt “` **Topic-specific searches** (when user asks about specific feature): “` Pattern: https://context7.com/{path}/llms.txt?topic={query} Examples: – https://context7.com/shadcn-ui/ui/llms.txt?topic=date – https://context7.com/shadcn-ui/ui/llms.txt?topic=button – https://context7.com/vercel/next.js/llms.txt?topic=cache – https://context7.com/websites/ffmpeg_doxygen_8_0/llms.txt?topic=compress “` **Fallback: Traditional llms.txt search** “` WebSearch: "[library name] llms.txt site:[docs domain]" “` Common patterns: – `https://docs.[library].com/llms.txt` – `https://[library].dev/llms.txt` – `https://[library].io/llms.txt` → Found? Proceed to Phase 2 → Not found? Proceed to Phase 3 ### Phase 2: llms.txt Processing **Single URL:** – WebFetch to retrieve content – Extract and present information **Multiple URLs (3+):** – **CRITICAL**: Launch multiple Explorer agents in parallel – One agent per major documentation section (max 5 in first batch) – Each agent reads assigned URLs – Aggregate findings into consolidated report Example: “` Launch 3 Explorer agents simultaneously: – Agent 1: getting-started.md, installation.md – Agent 2: api-reference.md, core-concepts.md – Agent 3: examples.md, best-practices.md “` ### Phase 3: Repository Analysis **When llms.txt not found:** 1. Find GitHub repository via WebSearch 2. Use Repomix to pack repository: “`bash npm install -g repomix # if needed git clone [repo-url] /tmp/docs-analysis cd /tmp/docs-analysis repomix –output repomix-output.xml “` 3. Read repomix-output.xml and extract documentation **Repomix benefits:** – Entire repository in single AI-friendly file – Preserves directory structure – Optimized for AI consumption ### Phase 4: Fallback Research **When no GitHub repository exists:** – Launch multiple Researcher agents in parallel – Focus areas: official docs, tutorials, API references, community guides – Aggregate findings into consolidated report ## Agent Distribution Guidelines – **1-3 URLs**: Single Explorer agent – **4-10 URLs**: 3-5 Explorer agents (2-3 URLs each) – **11+ URLs**: 5-7 Explorer agents (prioritize most relevant) ## Version Handling **Latest (default):** – Search without version specifier – Use current documentation paths **Specific version:** – Include version in search: `[library] v[version] llms.txt` – Check versioned paths: `/v[version]/llms.txt` – For repositories: checkout specific tag/branch ## Output Format “`markdown # Documentation for [Library] [Version] ## Source – Method: [llms.txt / Repository / Research] – URLs: [list of sources] – Date accessed: [current date] ## Key Information [Extracted relevant information organized by topic] ## Additional Resources [Related links, examples, references] ## Notes [Any limitations, missing information, or caveats] “` ## Quick Reference **Tool selection:** – WebSearch → Find llms.txt URLs, GitHub repositories – WebFetch → Read single documentation pages – Task (Explore) → Multiple URLs, parallel exploration – Task (Researcher) → Scattered documentation, diverse sources – Repomix → Complete codebase analysis **Popular llms.txt locations (try context7.com first):** – Astro: https://context7.com/withastro/astro/llms.txt – Next.js: https://context7.com/vercel/next.js/llms.txt – Remix: https://context7.com/remix-run/remix/llms.txt – shadcn/ui: https://context7.com/shadcn-ui/ui/llms.txt – Better Auth: https://context7.com/better-auth/better-auth/llms.txt **Fallback to official sites if context7.com unavailable:** – Astro: https://docs.astro.build/llms.txt – Next.js: https://nextjs.org/llms.txt – Remix: https://remix.run/llms.txt – SvelteKit: https://kit.svelte.dev/llms.txt ## Error Handling – **llms.txt not accessible** → Try alternative domains → Repository analysis – **Repository not found** → Search official website → Use Researcher agents – **Repomix fails** → Try /docs directory only → Manual exploration – **Multiple conflicting sources** → Prioritize official → Note versions ## Key Principles 1. **Prioritize context7.com for llms.txt** — Most comprehensive and up-to-date aggregator 2. **Use topic parameters when applicable** — Enables targeted searches with ?topic=… 3. **Use parallel agents aggressively** — Faster results, better coverage 4. **Verify official sources as fallback** — Use when context7.com unavailable 5. **Report methodology** — Tell user which approach was used 6. **Handle versions explicitly** — Don't assume latest ## Detailed Documentation For comprehensive guides, examples, and best practices: **Workflows:** – [WORKFLOWS.md](./WORKFLOWS.md) — Detailed workflow examples and strategies **Reference guides:** – [Tool Selection](./references/tool-selection.md) — Complete guide to choosing and using tools – [Documentation Sources](./references/documentation-sources.md) — Common sources and patterns across ecosystems – [Error Handling](./references/error-handling.md) — Troubleshooting and resolution strategies – [Best Practices](./references/best-practices.md) — 8 essential principles for effective discovery – [Performance](./references/performance.md) — Optimization techniques and benchmarks – [Limitations](./references/limitations.md) — Boundaries and success criteria