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CharlesYang030 244e346b83 SkillOpt v0.1.0: initial release

- Skill optimization framework with training loop analogy
- 11 benchmarks, 4 model backends (Azure OpenAI, Claude, Codex, Qwen)
- WebUI for browser-based training control
- Pluggable architecture for extending benchmarks and backends

2026-05-21 17:22:04 +00:00

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Contributing to SkillOpt

Thank you for your interest in contributing to SkillOpt! This guide covers how to get started.

Development Setup

git clone https://github.com/microsoft/SkillOpt.git
cd SkillOpt
pip install -e ".[dev]"

Ways to Contribute

🐛 Bug Reports

Open an issue with:

Steps to reproduce
Expected vs actual behavior
Config file used (sanitize API keys)
Python version and OS

🔧 New Benchmark

See Add a New Benchmark for the implementation guide.

Checklist:

Data loader in skillopt/envs/<benchmark>/loader.py
Environment adapter in skillopt/envs/<benchmark>/env.py
Config file in configs/<benchmark>/default.yaml
Registration in skillopt/envs/__init__.py
Documentation page in docs/

🤖 New Model Backend

See Add a New Model Backend for the implementation guide.

Checklist:

Backend in skillopt/model/<backend>.py
Registration in skillopt/model/__init__.py
API key entry in .env.example
Documentation update

📝 Documentation

Documentation is built with MkDocs Material:

pip install -e ".[docs]"
mkdocs serve  # Preview at http://localhost:8000

Code Style

Follow existing patterns in the codebase
Use type hints for function signatures
Keep docstrings concise

Pull Request Process

Fork the repository
Create a feature branch: git checkout -b feature/my-benchmark
Make your changes
Test with an existing benchmark config
Submit a PR with a clear description

License

By contributing, you agree that your contributions will be licensed under the MIT License.

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