Skill_Seekers

Skill Seekers is a command-line tool that takes documentation and turns it into a structured package an AI system can read. The problem it addresses is that AI assistants work better when you feed them clean, organized reference material, but preparing that material by hand takes a long time. This project does the preparation step for you. You point it at a source, it pulls the content out, organizes it, and writes out a knowledge file you can hand to an AI tool.

The sources it accepts are wide. According to the README it can read documentation websites, GitHub repositories, local project folders, PDFs, Word documents, EPUB e-books, Jupyter notebooks, OpenAPI specs, PowerPoint files, AsciiDoc, local HTML, RSS or Atom feeds, man pages, and videos from YouTube or local files, among others. It lists 18 supported source types in total. The basic flow is two commands: one to create the knowledge asset from a source, and one to package it for a chosen target.

The targets are the AI systems that will consume the result. The README names several, including Claude skills, Gemini, OpenAI custom assistants, and a number of retrieval setups such as LangChain, LlamaIndex, Haystack, and vector stores like Pinecone, ChromaDB, FAISS, and Qdrant. The pitch is that you prepare the data once and then export it to any of these targets without scraping the original source again.

Installation is through pip, the standard Python package installer, with the command pip install skill-seekers. The README mentions you can swap in different AI agents for an enhancement step, and it points to a companion website with ready-made config presets, plus several sibling repositories covering the website, community configs, a GitHub Action, a Claude Code plugin, and a Homebrew tap.

The project reports a large test suite and a set of framework presets, and it is offered under the MIT license. The full README is longer than what was shown.