docs-seeker（文档搜索者）

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

1. 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: → Found? Proceed to Phase 2 → Not found? Proceed to Phase 3

- https://docs.[library].com/llms.txt - https://[library].dev/llms.txt - https://[library].io/llms.txt

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: 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

# 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 — 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