Thank you for your interest in contributing to this Python decorator tutorial! 🎉
We're running a community challenge to build better AI agent tools. Here's how to participate:
- Fork this repository
- Create a new branch for your implementation
- Add your
@graceful_tooldecorator to a new file (e.g.,your_name_graceful_tool.py) - Include tests showing how your decorator handles different error scenarios
- Submit a Pull Request with:
- Clear description of your design choices
- Why you chose specific return messages for different errors
- How your solution helps LLMs self-correct
- What should the decorator return for different error types?
- How can the error message help an LLM understand and fix the problem?
- Should different exceptions return different messages?
- New decorator examples
- Bug fixes in existing decorators
- Performance improvements
- Better error handling
- Improve explanations in the notebook
- Add more examples or use cases
- Fix typos or clarify confusing sections
- Translate documentation
- Add unit tests for decorators
- Clone and setup (see README.md)
- Create a virtual environment
- Install dependencies:
pip install -r requirements.txt - Run tests:
python -m pytest(if tests exist)
- Use GitHub Issues to report bugs
- Include Python version, OS, and steps to reproduce
- For decorator-related issues, include the decorator code and expected vs actual behavior
- Open an issue with the "enhancement" label
- Describe the problem and proposed solution
- Explain why this would benefit the tutorial
By contributing, you agree that your contributions will be licensed under the same license as this project.
Contributors will be acknowledged in the README and repository contributors list. Special recognition for creative @graceful_tool implementations!
Ready to contribute? Check out the Issues page for current challenges and opportunities! 🚀