yusufkaraaslan/Skill_Seekers

Convert documentation websites, GitHub repositories, and PDFs into Claude AI skills with automatic conflict detection

What it solves

Skill Seekers is a data preparation layer for AI systems that automates the process of turning unstructured data from various sources into structured knowledge assets. It eliminates the manual effort of preparing documentation, codebases, and other materials to power AI skills, RAG pipelines, and AI coding assistants.

How it works

The tool follows a multi-step pipeline:

  1. Scrape: It extracts content from 18 different source types, including documentation websites (using llms.txt for speed), GitHub repositories, PDFs, videos (via transcripts and OCR), and wikis.
  2. Categorize: It organizes the extracted content into logical topics like APIs, guides, and tutorials.
  3. Enhance: An AI agent (either via API or local) analyzes the data to write a comprehensive SKILL.md file containing examples, patterns, and guides.
  4. Package: The processed data is bundled into a platform-ready artifact for one of 22 export targets, such as Claude, Gemini, LangChain, or various vector databases.
  5. Upload/Install: The resulting "skill" can be uploaded to AI platforms or installed directly into AI coding agents like Cursor, Windsurf, or Cline.

Who it’s for

  • AI Developers: Those building RAG pipelines or custom AI agents that need high-quality, structured context.
  • Software Engineers: Developers who want to provide their AI coding assistants with deep, accurate knowledge of a specific library or internal codebase.
  • Technical Writers: People managing documentation that needs to be converted into AI-ready formats.

Highlights

  • Massive Source Support: Handles 18 source types, including YouTube/Vimeo videos, Notion, Confluence, and Jupyter Notebooks.
  • C3.x Codebase Analysis: Uses AST parsing and design pattern detection to extract usage examples and architecture overviews from code.
  • Broad Export Ecosystem: Supports 22 targets, including 12 LLM platforms and 8 RAG/vector database formats.
  • MCP Integration: Includes an MCP server with 40 tools for seamless integration with Claude Code and other IDEs.
  • AI-Driven Project Scan: Automatically detects frameworks and generates configurations based on a project's manifests and README.
  • Scale-Ready: Features streaming ingestion for huge documentation sets (10K-40K+ pages) and quality scoring gates.

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