home / skills / brixtonpham / claude-config / docs-seeker
This skill discovers and analyzes technical documentation from llms.txt, repositories, and parallel sources to deliver comprehensive, up-to-date guidance.
npx playbooks add skill brixtonpham/claude-config --skill docs-seekerReview the files below or copy the command above to add this skill to your agents.
---
name: docs-seeker
description: "Searching internet for technical documentation using llms.txt standard, GitHub repositories via Repomix, and parallel exploration. Use when user needs: (1) Latest documentation for libraries/frameworks, (2) Documentation in llms.txt format, (3) GitHub repository analysis, (4) Documentation without direct llms.txt support, (5) Multiple documentation sources in parallel"
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. **Identify target**
- Extract library/framework name from user request
- Note version requirements (default: latest)
- Clarify scope if ambiguous
2. **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`
- `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:**
1. Find GitHub repository via WebSearch
2. Use Repomix to pack repository:
```bash
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
```markdown
# 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
## 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. **Always start with llms.txt** — Most efficient method
2. **Use parallel agents aggressively** — Faster results, better coverage
3. **Verify official sources** — Avoid outdated documentation
4. **Report methodology** — Tell user which approach was used
5. **Handle versions explicitly** — Don't assume latest
## Detailed Documentation
For comprehensive guides, examples, and best practices:
**Workflows:**
- [WORKFLOWS.md](./WORKFLOWS.md) — Detailed workflow examples and strategies
**Reference guides:**
- [Tool Selection](./references/tool-selection.md) — Complete guide to choosing and using tools
- [Documentation Sources](./references/documentation-sources.md) — Common sources and patterns across ecosystems
- [Error Handling](./references/error-handling.md) — Troubleshooting and resolution strategies
- [Best Practices](./references/best-practices.md) — 8 essential principles for effective discovery
- [Performance](./references/performance.md) — Optimization techniques and benchmarks
- [Limitations](./references/limitations.md) — Boundaries and success criteria
This skill discovers and consolidates technical documentation by prioritizing llms.txt, analyzing GitHub repositories with Repomix, and running parallel exploration when needed. It targets library/framework documentation, supports versioned lookups, and aggregates findings into a clear report. Use it when you need fast, AI-friendly documentation extraction across multiple sources.
The skill first extracts the target name and optional version from the user request, then searches for llms.txt on likely domains. If llms.txt is found it fetches and parses the file; if multiple URLs exist it spins up Explorer agents in parallel to read sections. When llms.txt is absent it locates the GitHub repository and uses Repomix to produce an AI-friendly XML of the repo; if no repo is available it launches Researcher agents to comb official docs, tutorials, and community guides.
What if llms.txt is behind authentication or blocked?
The skill will fall back to repository analysis or Researcher agents and will note access limitations in the report.
How are conflicting sources resolved?
Official sources and versioned paths are prioritized; conflicts are recorded and flagged with recommendations.