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