docs-seeker

To find technical documentation without an llms.txt file, this Skill falls back to analyzing GitHub repositories via Repomix and deploying parallel agents to search diverse sources. It extracts API references and installation steps when standardized documentation is missing.

The best way to locate up-to-date API references is by prioritizing llms.txt files for standardized AI-friendly documentation, then falling back to repository analysis. This approach handles versioning and diverse documentation sources to ensure accuracy.

Yes, you can analyze entire GitHub repositories to understand library documentation using Repomix. This allows the Skill to extract key information like installation steps and API references directly from the codebase when external docs are insufficient.

Parallel agent exploration works by deploying multiple agents simultaneously to search diverse documentation sources. This speeds up the process of locating scattered technical docs and API references for libraries and frameworks.

If standard web searches fail to find the documentation you need, this Skill utilizes Researcher agents as a fallback method. These agents conduct deeper research to locate and extract the necessary technical documentation from various sources.

Yes, docs-seeker works effectively with documentation scattered across multiple sources by using parallel exploration and Researcher agents. It consolidates information from llms.txt files, repositories, and diverse web sources to present a clear technical overview.