docs-seeker

The docs-seeker skill enables intelligent discovery and analysis of technical documentation by using multiple strategies, such as searching for standardized llms.txt documentation, analyzing GitHub repositories with Repomix, and deploying parallel exploration for comprehensive documentation coverage. This skill provides a robust solution for developers and technical users who need up-to-date and structured documentation across a variety of sources. It solves the problem of finding the latest library or framework documentation, whether in llms.txt format or not, and provides easy access to GitHub repositories when llms.txt is unavailable. By leveraging parallel exploration, this skill maximizes coverage across diverse documentation sources.

Main features include llms.txt-first search for AI-friendly documentation, repository analysis using Repomix for AI optimization, and the ability to handle missing or incomplete documentation with fallback research using AI agents. The skill is capable of gathering data from multiple sources at once, and can even analyze GitHub repositories or websites with no direct llms.txt support. Users can depend on this skill for a more efficient documentation retrieval process, particularly in scenarios where multiple documentation formats or sources need to be consulted.

Use cases for docs-seeker include developers seeking the latest library documentation, users needing GitHub repository analysis for technical projects, and those looking to consolidate documentation from various sources. It's ideal for professionals working in fast-paced technical environments where accessing multiple sources of documentation quickly is crucial for productivity. The skill can be applied across multiple programming domains, including web development, machine learning, and more, enhancing both documentation discovery and analysis workflows.