Docs Seeker

Documentation Discovery via Scripts

Overview

Script-first documentation discovery using llms.txt standard.

Execute Python 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)
python scripts/detect_topic.py "<user query>"

# 2. FETCH documentation using script output
python scripts/fetch_docs.py "<user query>"

# 3. ANALYZE results (if multiple URLs returned)
cat llms.txt | python scripts/analyze_llms_txt.py -

Scripts handle URL construction, fallback chains, and error handling automatically.

Scripts

detect_topic.py - Classify query type

• Identifies topic-specific vs general queries
• Extracts library name + topic keyword
• Returns JSON: {topic, library, isTopicSpecific}
• Zero-token execution

fetch_docs.py - 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.py - 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 using codemap

References

context7-patterns.md - URL patterns, known repositories

errors.md - Error handling, fallback strategies

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?"

python scripts/detect_topic.py "<query>"  # → {topic, library, isTopicSpecific}
python scripts/fetch_docs.py "<query>"    # → 2-3 URLs
# Read URLs with WebFetch

General query: "Documentation for Next.js"

python scripts/detect_topic.py "<query>"         # → {isTopicSpecific: false}
python scripts/fetch_docs.py "<query>"           # → 8+ URLs
cat llms.txt | python scripts/analyze_llms_txt.py -  # → {totalUrls, distribution}
# Deploy agents per recommendation

Environment

Scripts load .env: os.environ > .claude/skills/docs-seeker/.env > .claude/skills/.env > .claude/.env

See .env.example for configuration options.

Requirements

• Python 3.8+
• No external dependencies (uses stdlib only)

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