docs-seeker

SKILL.md

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