Thank you for your interest in contributing to SentinelPrime! This is a research project exploring AI-driven security control planes.
- Share findings from your experiments
- Propose new detection algorithms
- Improve ML model accuracy
- Document attack scenarios
- Fork the repository
- Create a feature branch (
git checkout -b feature/amazing-feature) - Make your changes
- Add tests if applicable
- Commit your changes (
git commit -m 'Add amazing feature') - Push to the branch (
git push origin feature/amazing-feature) - Open a Pull Request
Use GitHub Issues to report bugs. Include:
- Description of the issue
- Steps to reproduce
- Expected vs. actual behavior
- Environment details (OS, Docker version, etc.)
- Logs if applicable
We welcome ideas! Open an issue with:
- Clear description of the feature
- Use case / motivation
- Potential implementation approach
- Clone the repository
- Install development dependencies:
pip install -r control-plane/requirements.txt pip install pytest black flake8
- Run tests:
pytest
- Format code:
black .
- Follow PEP 8 for Python code
- Use meaningful variable names
- Add docstrings to functions and classes
- Keep functions focused and concise
- Add comments for complex logic
- Write tests for new features
- Ensure existing tests pass
- Test with different configurations
- Document test scenarios
- Update README.md for user-facing changes
- Update ARCHITECTURE.md for design changes
- Add examples in docs/ for new features
- Keep comments up-to-date
This project deals with security and attack simulation:
- Only test in isolated lab environments
- Respect privacy and data protection
- Follow responsible disclosure for vulnerabilities
- Document safety considerations
Open an issue or reach out to the maintainers.
Thank you for contributing to SentinelPrime! 🛡️