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AI-Assisted Development

This project uses an AI-first development workflow. Most features, bug fixes, and improvements are implemented through AI coding agents (Claude Code, GitHub Copilot, OpenAI Codex) working from structured issue descriptions.

How It Works

  1. Issues are the starting point — Every task begins as a GitHub issue with a structured format: an overview, a human-readable plan, a detailed implementation plan (in a collapsible block), and optionally the original prompt that generated the issue.

  2. AI agents pick up issues — Issues can be assigned to AI coding agents (e.g. GitHub Copilot) which read the issue description, AGENTS.md, and CLAUDE.md for context, then implement the changes autonomously.

  3. Human review — All AI-generated pull requests are reviewed by maintainers before merging.

Creating an Issue

When opening an issue, please use the provided issue templates. The Feature / Task Request template follows our standard format:

  • Overview — What and why, in 2-4 sentences
  • Plan — High-level bullet points (human-readable)
  • Detailed implementation plan — File paths, steps, key files (in a collapsible block)
  • Original Prompt — If you used an AI to help draft the issue, include the original prompt

If your feature involves a specific calculation, algorithm, or small piece of functionality — include example code. Even a rough script, a working prototype, or a snippet showing the existing behaviour you want to change makes a huge difference. Code examples give AI agents and human contributors concrete context to work from, and dramatically reduce misunderstandings about what you're asking for.

This structure ensures that both human contributors and AI agents can understand and act on the issue effectively.

Contributing Without AI

Traditional contributions are equally welcome! If you prefer to work without AI tools, simply follow the development setup and pull request guidelines below. The issue templates are helpful for any contributor, AI or human.

Contributing

Contributions are welcome and greatly appreciated!

Types of Contributions

Report Bugs

Report bugs at https://github.com/rhayes777/PyAutoFit/issues

If you are playing with the PyAutoFit library and find a bug, please reporting it including:

  • Your operating system name and version.
  • Any details about your Python environment.
  • Detailed steps to reproduce the bug.

Propose New NonLinearSearch or Features

The best way to send feedback is to open an issue at https://github.com/rhayes777/PyAutoFit/issues with tag enhancement.

If you are proposing a new NonLinearSearch or a new feature:

  • Explain in detail how it should work.
  • Keep the scope as narrow as possible, to make it easier to implement.

Implement NonLinearSearch or Features

Look through the Git issues for operator or feature requests. Anything tagged with enhancement is open to whoever wants to implement it.

Add Examples or improve Documentation

Writing new features is not the only way to get involved and contribute. Create examples with existing non-linear searches as well as improving the documentation of existing operators is as important as making new non-linear searches and very much encouraged.

Getting Started to contribute

Ready to contribute?

  1. Follow the installation instructions for installing PyAutoFit from source root on our readthedocs.

  2. Create a branch for local development:

    git checkout -b name-of-your-branch

    Now you can make your changes locally.

  3. When you're done making changes, check that old and new tests pass succesfully:

    cd PyAutoFit/test_autofit
python3 -m pytest

  4. Commit your changes and push your branch to GitLab::

    git add .
git commit -m "Your detailed description of your changes."
git push origin name-of-your-branch

    Remember to add -u when pushing the branch for the first time.

  5. Submit a pull request through the GitHub website.

Pull Request Guidelines

Before you submit a pull request, check that it meets these guidelines:

  1. The pull request should include new tests for all the core routines that have been developed.
  2. If the pull request adds functionality, the docs should be updated accordingly.