
Vc Docs Seeker
Search library/framework documentation via llms.txt (context7.com).
Install
npx skills add withkynam/vibecode-pro-max-kit --skill vc-docs-seeker -g -a claude-code -y- Repository
- withkynam/vibecode-pro-max-kit1.2k
- Category
- Git and review
- Updated
- 5mo ago
- License
- MIT
- Context cost
- Lightabout 922 tokens
- Runs scripts
- Yes, review before use
What it does
Search library/framework documentation via llms.txt (context7.com). Use for API docs, GitHub repository analysis, technical documentation lookup, latest library features.
Documentation Discovery
Output style: Follow
process/development-protocols/communication-standards.md, answer-first, plain language, no unexplained jargon, TL;DR on long responses.
Overview
Use Context7 MCP as the default path for documentation lookup. The local scripts in this skill are fallback helpers for llms.txt-based discovery when Context7 does not cover the target cleanly.
Primary Workflow
Default workflow: Context7 first
- Resolve the library with Context7.
- Query the exact API/config/setup question with Context7.
- Only fall back to the scripts below when Context7 coverage is missing, incomplete, or the user explicitly wants llms.txt/repository-oriented discovery.
Fallback script workflow:
# 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)

