docs-seeker

You can find library documentation by orchestrating topic detection, URL discovery, and llms.txt analysis to deliver targeted results. The process distinguishes topic-specific queries from general queries and fetches relevant docs automatically.

Retrieving docs for a specific framework query is best handled by a fallback-driven workflow that automatically falls back from topic URLs to general URLs and finally to repository analysis when needed, ensuring you get a structured, actionable report.

Topic detection for AI-assisted library searches works by classifying the query to distinguish topic-specific requests from general queries. It then coordinates URL discovery and llms.txt retrieval to produce a concise set of URLs with a summary suitable for rapid review.

Yes, you can get a structured summary of Next.js caching guidance by running the detection, fetching, and analysis sequence. It returns a concise set of URLs with a summary suitable for rapid review, coordinating topic detection and llms.txt analysis.

When topic-specific documentation URLs are not found, the fallback-driven workflow automatically falls back to general URLs and finally to repository analysis. This ensures the library search still produces a structured, actionable report for the developer.

No external dependencies are required to analyze llms.txt files for library docs. The process relies entirely on internal scripts for topic detection, fetching, and analysis to produce a structured, actionable report without external packages.