Documentation Discovery via Scripts

Overview

Primary 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

detect-topic.js

• Identifies topic-specific vs general queries
• Extracts library name + topic keyword
• Returns JSON:

  {topic, library, isTopicSpecific}

• Zero-token execution

fetch-docs.js

• Constructs context7.com URLs automatically
• Handles fallback: topic → general → error
• Outputs llms.txt content or error message
• Zero-token execution

analyze-llms-txt.js

• Categorizes URLs (critical/important/supplementary)
• Recommends agent distribution (1 agent, 3 agents, 7 agents, phased)
• Returns JSON with strategy
• Zero-token execution

Workflow References

References

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

node scripts/detect-topic.js "<query>"  # → {topic, library, isTopicSpecific}
node scripts/fetch-docs.js "<query>"    # → 2-3 URLs
# Read URLs with WebFetch

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

.env

process.env

.claude/skills/docs-seeker/.env

.claude/skills/.env

.claude/.env

.env.example