docs-seeker

name	docs-seeker
description	Searches technical docs via llms.txt standard, GitHub repositories via Repomix, and parallel Explorer agents. Use for library docs, llms.txt format, GitHub repo analysis, or multi-source research.
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. Extract library/framework name from user request
2. Note version requirements (default: latest)
3. Identify if target is GitHub repository or website
4. Search for llms.txt:

   WebSearch: "[library name] llms.txt site:[docs domain]"
   

   Common patterns: https://docs.[library].com/llms.txt, https://[library].dev/llms.txt

Phase 2: llms.txt Processing

• Single URL: WebFetch to retrieve content
• Multiple URLs (3+): Launch multiple Explorer agents in parallel (max 5 per batch)

Phase 3: Repository Analysis

When llms.txt not found:

1. Find GitHub repository via WebSearch
2. Clone and run Repomix:

   git clone [repo-url] /tmp/docs-analysis
   cd /tmp/docs-analysis && repomix --output repomix-output.xml
   

3. Read repomix-output.xml and extract documentation

Phase 4: Fallback Research

When no GitHub repository: Launch multiple Researcher agents in parallel, aggregate findings.

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)

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:

• 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

Key Principles

1. Search for llms.txt first — Most efficient path to AI-friendly documentation
2. Use parallel agents aggressively — Faster results, better coverage
3. Verify official sources — Confirm documentation is from official domains
4. Report methodology — Tell user which approach was used
5. Handle versions explicitly — Don't assume latest

Reference Guides

Workflows:

• WORKFLOWS.md — Detailed workflow examples and strategies

Tool usage:

• Tool Selection — Tools and when to use them
• Decision Trees — Tool and agent count selection

Sources:

• llms.txt & Registries — Known llms.txt URLs and package registries
• Hosting & Search Patterns — Hosting platforms and search strategies

Error handling:

• llms.txt & Repository Errors — Resolution for common failures
• Conflicts & Rate Limits — Conflict resolution and rate limiting

Best practices:

• Discovery & Sources — llms.txt priority, version handling, source verification
• Parallel Agents — Agent parallelism and aggregation
• Methodology & Caching — Reporting, time management, caching

Performance:

• Core Principles — Parallelism, batching, early termination patterns
• Optimization & Benchmarks — Techniques, benchmarks, common issues

Limitations:

• Access Constraints — Auth, rate limits, real-time, interactive, video
• Difficult Scenarios — Large repos, PDFs, non-English, scattered, legacy
• Success Criteria — Quality metrics and performance targets