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- 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
70 lines
1.7 KiB
Markdown
70 lines
1.7 KiB
Markdown
# Contributing to SkillOpt
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Thank you for your interest in contributing to SkillOpt! This guide covers how to get started.
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## Development Setup
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```bash
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git clone https://github.com/microsoft/SkillOpt.git
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cd SkillOpt
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pip install -e ".[dev]"
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```
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## Ways to Contribute
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### 🐛 Bug Reports
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Open an issue with:
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- Steps to reproduce
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- Expected vs actual behavior
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- Config file used (sanitize API keys)
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- Python version and OS
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### 🔧 New Benchmark
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See [Add a New Benchmark](guide/new-benchmark.md) for the implementation guide.
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**Checklist:**
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- [ ] Data loader in `skillopt/envs/<benchmark>/loader.py`
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- [ ] Environment adapter in `skillopt/envs/<benchmark>/env.py`
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- [ ] Config file in `configs/<benchmark>/default.yaml`
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- [ ] Registration in `skillopt/envs/__init__.py`
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- [ ] Documentation page in `docs/`
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### 🤖 New Model Backend
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See [Add a New Model Backend](guide/new-backend.md) for the implementation guide.
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**Checklist:**
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- [ ] Backend in `skillopt/model/<backend>.py`
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- [ ] Registration in `skillopt/model/__init__.py`
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- [ ] API key entry in `.env.example`
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- [ ] Documentation update
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### 📝 Documentation
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Documentation is built with MkDocs Material:
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```bash
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pip install -e ".[docs]"
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mkdocs serve # Preview at http://localhost:8000
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```
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## Code Style
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- Follow existing patterns in the codebase
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- Use type hints for function signatures
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- Keep docstrings concise
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## Pull Request Process
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1. Fork the repository
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2. Create a feature branch: `git checkout -b feature/my-benchmark`
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3. Make your changes
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4. Test with an existing benchmark config
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5. Submit a PR with a clear description
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## License
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By contributing, you agree that your contributions will be licensed under the MIT License.
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