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
How do I install this agent skill?
npx skills add https://github.com/hotriluan/ai-command-center --skill docs-seekerIs this agent skill safe to install?
- Gen Agent Trust Hubwarn
This skill provides a toolkit for documentation discovery using scripts to interface with context7.com. It contains a potential command injection vulnerability in its workflow instructions and an indirect prompt injection surface through the processing of untrusted documentation files.
- Socketpass
No alerts
- Snykwarn
Risk: MEDIUM · 2 issues
What does this agent skill do?
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:
# 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 - Fastest path (10-15s)
General Library Search - Comprehensive coverage (30-60s)
Repository Analysis - Fallback strategy
References
context7-patterns.md - URL patterns, known repositories
errors.md - Error handling, fallback strategies
advanced.md - Edge cases, versioning, multi-language
Execution Principles
- Scripts first - Execute scripts instead of manual URL construction
- Zero-token overhead - Scripts run without context loading
- Automatic fallback - Scripts handle topic → general → error chains
- Progressive disclosure - Load workflows/references only when needed
- Agent distribution - Scripts recommend parallel agent strategy
Quick Start
Topic query: "How do I use date picker in shadcn?"
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"
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.
How can the creator link this skill?
Add the canonical catalog link to the repository README so users can inspect current installs and available audits. The publishing guide covers the complete discovery path.
<a href="https://skillzs.dev/skills/hotriluan/ai-command-center/docs-seeker">View docs-seeker on skillZs</a>