[IMPORTANT] Use TaskCreate to break ALL work into small tasks BEFORE starting — including tasks for each file read. This prevents context loss from long files. For simple tasks, AI MUST ask user whether to skip.

Quick Summary

Goal: Search and fetch technical documentation using executable scripts with llms.txt standard (context7.com).

Workflow:

• Detect — Run scripts/detect-topic.js to classify query type (topic-specific vs general)

• Fetch — Run scripts/fetch-docs.js to retrieve documentation with automatic fallback

• Analyze — Run scripts/analyze-llms-txt.js to categorize URLs and recommend agent distribution

Key Rules:

• Always execute scripts in order: detect -> fetch -> analyze

• Scripts handle URL construction and fallback chains automatically; no manual URL building

• Zero-token overhead: scripts run without context loading

Be skeptical. Apply critical thinking, sequential thinking. Every claim needs traced proof, confidence percentages (Idea should be more than 80%).

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.

IMPORTANT Task Planning Notes (MUST FOLLOW)

• Always plan and break work into many small todo tasks

• Always add a final review todo task to verify work quality and identify fixes/enhancements