explaingit

yusufkaraaslan/skill_seekers

★ 13,509PythonAudience · developerComplexity · 2/5LicenseSetup · easy

TLDR

Skill Seekers converts docs from websites, PDFs, GitHub repos, YouTube videos, and 14 other sources into structured knowledge packages that AI tools like Claude, Gemini, or OpenAI assistants can consume directly.

Mindmap

mindmap
  root((repo))
    What it does
      Docs to knowledge
      AI-ready packages
      Two commands
    Input sources
      Websites PDFs
      GitHub repos
      YouTube videos
      EPUB notebooks
    Output targets
      Claude skills
      OpenAI assistants
      Pinecone ChromaDB
      LangChain LlamaIndex
    Install
      pip install
      GitHub Action
      Homebrew tap
Click or tap to explore — scroll the page freely

Code map

Detail Auto

An interactive map of this repo's files and how they connect — its source is parsed live in your browser. Click Visualize to build it.

filefunction / class

Things people build with this

USE CASE 1

Turn a documentation website into a Claude skill or OpenAI custom assistant knowledge base with two commands.

USE CASE 2

Package a GitHub repository's code and docs into a vector store like Pinecone or ChromaDB for RAG-powered search.

USE CASE 3

Convert a PDF manual or EPUB book into a structured knowledge file ready for a LangChain or LlamaIndex pipeline.

USE CASE 4

Build a GitHub Action that automatically regenerates your AI knowledge package whenever your documentation is updated.

Tech stack

Pythonpip

Getting it running

Difficulty · easy Time to first run · 5min
Free to use for any purpose including commercial projects, with no restrictions beyond keeping the copyright notice.

In plain English

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.

Copy-paste prompts

Prompt 1
Use Skill Seekers to package the FastAPI documentation website into a Claude skill I can use in my project.
Prompt 2
Write the two Skill Seekers commands to create a knowledge asset from a GitHub repo and export it to Pinecone.
Prompt 3
How do I configure Skill Seekers to read a local PDF and export the result to a LlamaIndex vector store?
Prompt 4
Set up a GitHub Action using Skill Seekers to rebuild my documentation knowledge package on every push to main.
Open on GitHub → Explain another repo

← yusufkaraaslan on gitmyhub — every repo by this author, as a profile.

Verify against the repo before relying on details.