Skill_Seekers

Skill Seekers acts as the data preprocessing layer for AI systems. It ingests documentation, GitHub repositories, PDFs, and videos, then analyzes and structures the content into reusable knowledge assets and SKILL.md files that can power AI skills across platforms. As an MCP server, you can run Skill Seekers locally and expose its capabilities to your MCP workflow, enabling other agents or tooling in your pipeline to request packaged outputs (Claude, Gemini, OpenAI, LangChain documents, etc.). The toolset focuses on extracting, organizing, and exporting data in multiple target formats so you can feed downstream AI systems without re-scraping sources.

Once running, you can leverage its common commands to create and package knowledge assets. For example, you can create a knowledge asset from a source, then package it for multiple targets (Claude, LangChain, LlamaIndex, etc.). This enables end-to-end preparation from raw sources to deployment-ready outputs that integrate with your RAG pipelines, IDEs, or AI coding assistants.

Prerequisites:

• Python 3.10+ installed on your system
• Access to install Python packages (pip)

Installation and setup:

1. Create a virtual environment (optional but recommended):

python -m venv venv
source venv/bin/activate  # on macOS/Linux
venv\Scripts\activate    # on Windows

2. Install Skill Seekers from PyPI:

pip install skill-seekers

3. Run the MCP server for Skill Seekers:

python -m skill_seekers

4. (Optional) Create a dedicated config file or environment variables if you run multiple MCP servers in one environment.

# Example: run with a custom port or settings via environment variables as needed
export SKILL_SEEKERS_CONFIG=/path/to/config.json

Tips and caveats:

• The Skill Seekers package focuses on converting diverse sources into structured outputs suitable for multiple AI targets (Claude, Gemini, OpenAI, LangChain, LlamaIndex, etc.).
• If running behind a corporate proxy, ensure HTTP(S)_PROXY environment variables are set for pip and Python network access.
• When packaging, you can export to several targets in one go to speed up integration workflows (see CLI docs in the project for exact commands).
• For best results, pin Python dependencies in a virtual environment to avoid compatibility issues across projects.
• If you encounter performance issues with large sources, consider preprocessing in chunks and using the packaging steps to produce smaller, incremental outputs.