docs-seeker

Automating library documentation discovery uses a script-first workflow with topic detection and llms.txt lookups to fetch curated API reference URLs from official sites and GitHub. It coordinates scripts to handle precise feature queries and broad library lookups automatically.

The llms.txt mechanism provides structured documentation resources for frameworks. Analyzing llms.txt allows scripts to extract targeted URLs for official docs and API references, enabling rapid, automated retrieval of technical documentation across official sites and repositories.

Yes, you can retrieve topic-specific docs from Context7 and GitHub repositories. The discovery workflow constructs targeted URLs based on detected topics and applies automatic fallbacks, gracefully degrading from specific topic URLs to general docs and repository analyses when needed.

The best way to handle missing official library docs is using an automatic fallback workflow that degrades from topic-specific URLs to general documentation and repository analyses. This script-driven approach ensures you still retrieve relevant technical context when primary sources fail.

You need agent-based retrieval to synthesize documentation lookup results after the scripts fetch them. The workflow provides guidance on deploying agents to analyze and synthesize the curated URLs, ensuring the fetched documentation is processed into targeted, usable context.

Topic detection may fail on broad library lookups without specific features because the scripts are optimized for precise feature queries. The workflow handles this by applying automatic fallbacks to general docs and repository analyses, ensuring broad queries still return relevant results.