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Contributing to MAGI Researchers

Thank you for your interest in contributing! This guide will help you get started.

Development Setup

  1. Clone the repository:

    git clone https://github.com/Axect/magi-researchers.git
cd magi-researchers

  2. Install dependencies with uv:

    uv sync

  3. Set up MCP servers for Claude Code (see README.md).

  4. Run the plugin locally:

    claude --plugin-dir .

Ways to Contribute

Domain Templates

One of the most impactful contributions is adding domain templates. Create a new markdown file in templates/domains/:

templates/domains/your_domain.md

Follow the structure of existing templates (physics.md, ai_ml.md, statistics.md, mathematics.md, paper.md) — include domain-specific guidance for brainstorming, implementation, and evaluation.

Bug Reports

Found a bug? Open an issue with:

  • Steps to reproduce
  • Expected vs actual behavior
  • Your environment (Python version, OS, Claude Code version)

Feature Requests

Have an idea? Open a feature request describing:

  • The problem you're trying to solve
  • Your proposed solution
  • Any alternatives you've considered

Code Contributions

  1. Fork the repository
  2. Create a feature branch from dev: 
    git checkout dev
git checkout -b feature/your-feature
  3. Make your changes
  4. Test locally with claude --plugin-dir .
  5. Submit a pull request to the dev branch

Pull Request Guidelines

  • Target the dev branch (not main)
  • Keep changes focused — one feature or fix per PR
  • Update documentation if your change affects usage
  • Add or update domain templates if applicable
  • Describe what your PR does and why

Project Structure

magi-researchers/
├── .claude-plugin/       # Plugin metadata
├── agents/               # Agent definitions
├── magi_researchers/     # Python package
├── skills/               # Skill definitions (phases)
├── templates/            # Domain & report templates
└── outputs/              # Generated research outputs

Code Style

  • Python: Follow existing conventions in the codebase
  • Markdown: Use ATX-style headers, fenced code blocks
  • YAML/JSON: Preserve full decimal precision for numeric values

Questions?

Open a discussion or reach out via issues.