Documentation Discovery & Analysis Overview
Intelligent discovery and analysis of technical documentation through multiple strategies:
llms.txt-first: Search for standardized AI-friendly documentation Repository analysis: Use Repomix to analyze GitHub repositories Parallel exploration: Deploy multiple Explorer agents for comprehensive coverage Fallback research: Use Researcher agents when other methods unavailable Core Workflow Phase 1: Initial Discovery
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
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:
Find GitHub repository via WebSearch Use Repomix to pack repository: npm install -g repomix # if needed git clone [repo-url] /tmp/docs-analysis cd /tmp/docs-analysis repomix --output repomix-output.xml
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
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 Prioritize context7.com for llms.txt — Most comprehensive and up-to-date aggregator Use topic parameters when applicable — Enables targeted searches with ?topic=... Use parallel agents aggressively — Faster results, better coverage Verify official sources as fallback — Use when context7.com unavailable Report methodology — Tell user which approach was used Handle versions explicitly — Don't assume latest Detailed Documentation
For comprehensive guides, examples, and best practices:
Workflows:
WORKFLOWS.md — Detailed workflow examples and strategies
Reference guides:
Tool Selection — Complete guide to choosing and using tools Documentation Sources — Common sources and patterns across ecosystems Error Handling — Troubleshooting and resolution strategies Best Practices — 8 essential principles for effective discovery Performance — Optimization techniques and benchmarks Limitations — Boundaries and success criteria