What problem does it solve?
Provides a script-first approach to discovering technical documentation. It detects whether a query is topic-specific or general, fetches documentation sources (llms.txt) from context7 or fallback sources, and analyzes results to guide agent distribution for efficient research.
Core Features & Use Cases
- Topic Detection: Classifies queries into topic-specific vs general to target the right documentation sources.
- Automated Fetching: Builds context7 URLs or falls back to general library URLs to retrieve llms.txt.
- Results Analysis: Categorizes URLs by priority and suggests agent distribution for efficient research.
- Use Case: A developer asks for Next.js caching strategies; docs-seeker locates relevant llms.txt and prioritizes the most impactful sources.
Quick Start
Run the topic detection script, then fetch docs, and finally analyze llms.txt to obtain a structured plan for agent distribution.
