home / skills / jjuidev / jss / docs-seeker
/.claude/skills/docs-seeker
This skill discovers library documentation by executing scripted workflows to fetch, categorize, and analyze context7 docs for fast API lookups.
npx playbooks add skill jjuidev/jss --skill docs-seekerReview the files below or copy the command above to add this skill to your agents.
---
name: docs-seeker
description: Search library/framework documentation via llms.txt (context7.com). Use for API docs, GitHub repository analysis, technical documentation lookup, latest library features.
version: 3.1.0
---
# 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.
This skill offers script-first documentation discovery using the llms.txt standard and context7.com URL patterns. It automates detection, fetching, and analysis of library and framework docs so you can find API references, GitHub repo content, and technical guides without manually building URLs. The workflow is optimized for fast topic queries and comprehensive library searches.
Run the provided scripts in sequence: detect-topic to classify the query and extract library/topic, fetch-docs to construct context7.com URLs and retrieve llms.txt, and analyze-llms-txt to categorize results and recommend agent distribution. Scripts handle fallback chains (topic → general → repo) and error conditions automatically and return JSON or llms.txt output. Environment configuration is loaded from .env locations so scripts run with zero-token overhead.
What order should I run the scripts in?
Always run detect-topic first, then fetch-docs, and finally analyze-llms-txt when multiple URLs are returned.
Do the scripts require special tokens or API keys?
No. Scripts are designed for zero-token execution; environment variables are loaded from local .env files for configuration.