docs-seeker
Search technical documentation using executable scripts to detect query type, fetch from llms.txt sources (context7.com), and analyze results. Use when user needs: (1) Topic-specific documentation (features/components/concepts), (2) Library/framework documentation, (3) GitHub repository analysis, (4) Documentation discovery with automated agent distribution strategy
Best use case
docs-seeker is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Search technical documentation using executable scripts to detect query type, fetch from llms.txt sources (context7.com), and analyze results. Use when user needs: (1) Topic-specific documentation (features/components/concepts), (2) Library/framework documentation, (3) GitHub repository analysis, (4) Documentation discovery with automated agent distribution strategy
Teams using docs-seeker should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/docs-seeker/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How docs-seeker Compares
| Feature / Agent | docs-seeker | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Search technical documentation using executable scripts to detect query type, fetch from llms.txt sources (context7.com), and analyze results. Use when user needs: (1) Topic-specific documentation (features/components/concepts), (2) Library/framework documentation, (3) GitHub repository analysis, (4) Documentation discovery with automated agent distribution strategy
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Documentation Discovery via Scripts
## Overview
**Script-first** documentation discovery using llms.txt standard.
Execute scripts to handle entire workflow - no manual URL construction needed.
## Primary Workflow
**ALWAYS execute scripts in this order:**
```bash
# 1. DETECT query type (topic-specific vs general)
node scripts/detect-topic.js "<user query>"
# 2. FETCH documentation using script output
node scripts/fetch-docs.js "<user query>"
# 3. ANALYZE results (if multiple URLs returned)
cat llms.txt | node scripts/analyze-llms-txt.js -
```
Scripts handle URL construction, fallback chains, and error handling automatically.
## Scripts
**`detect-topic.js`** - Classify query type
- Identifies topic-specific vs general queries
- Extracts library name + topic keyword
- Returns JSON: `{topic, library, isTopicSpecific}`
- Zero-token execution
**`fetch-docs.js`** - Retrieve documentation
- Constructs context7.com URLs automatically
- Handles fallback: topic → general → error
- Outputs llms.txt content or error message
- Zero-token execution
**`analyze-llms-txt.js`** - Process llms.txt
- Categorizes URLs (critical/important/supplementary)
- Recommends agent distribution (1 agent, 3 agents, 7 agents, phased)
- Returns JSON with strategy
- Zero-token execution
## Workflow References
**[Topic-Specific Search](./workflows/topic-search.md)** - Fastest path (10-15s)
**[General Library Search](./workflows/library-search.md)** - Comprehensive coverage (30-60s)
**[Repository Analysis](./workflows/repo-analysis.md)** - Fallback strategy
## References
**[context7-patterns.md](./references/context7-patterns.md)** - URL patterns, known repositories
**[errors.md](./references/errors.md)** - Error handling, fallback strategies
**[advanced.md](./references/advanced.md)** - Edge cases, versioning, multi-language
## Execution Principles
1. **Scripts first** - Execute scripts instead of manual URL construction
2. **Zero-token overhead** - Scripts run without context loading
3. **Automatic fallback** - Scripts handle topic → general → error chains
4. **Progressive disclosure** - Load workflows/references only when needed
5. **Agent distribution** - Scripts recommend parallel agent strategy
## Quick Start
**Topic query:** "How do I use date picker in shadcn?"
```bash
node scripts/detect-topic.js "<query>" # → {topic, library, isTopicSpecific}
node scripts/fetch-docs.js "<query>" # → 2-3 URLs
# Read URLs with WebFetch
```
**General query:** "Documentation for Next.js"
```bash
node scripts/detect-topic.js "<query>" # → {isTopicSpecific: false}
node scripts/fetch-docs.js "<query>" # → 8+ URLs
cat llms.txt | node scripts/analyze-llms-txt.js - # → {totalUrls, distribution}
# Deploy agents per recommendation
```
## Environment
Scripts load `.env`: `process.env` > `.claude/skills/docs-seeker/.env` > `.claude/skills/.env` > `.claude/.env`
See `.env.example` for configuration options.Related Skills
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