vc-docs-seeker

name: vc-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. argument-hint: "[library-name] [topic]" trigger_keywords: how does X work, API docs, version, syntax layer: helper metadata: author: claudekit version: "3.1.0"

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

1. Resolve the library with Context7.
2. Query the exact API/config/setup question with Context7.
3. 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)

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

1. Context7 first - Use Context7 MCP before any local script fallback
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. Scripts are fallback helpers - Use them only when Context7 coverage is missing or the user wants llms.txt-style discovery

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
# Use Context7 results first; only use script-discovered URLs when you intentionally fall back

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}
# Use Context7 results first; only use script-discovered URLs when fallback discovery is required

Environment

Scripts load .env: process.env > .claude/skills/vc-docs-seeker/.env > .claude/skills/.env > .claude/.env

Optional env vars: CONTEXT7_API_KEY (Bearer token for Context7 API), DEBUG=true (verbose logging).