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