Automate Workflows and Maintain Your Package

Once you’ve created your package, published it, and set up a repository for it, the next step is to automate development and maintenance workflows. Automation makes maintaining your package easier, more robust, and more secure. It also helps new contributors get started quickly without having to manually set up complex development environments and testing workflows.

Why automate?

When you automate repetitive tasks like running tests, checking code style, and building documentation, you ensure that these important steps happen consistently every time. This consistency helps you catch bugs early, maintain code quality, and make it easier for others to contribute to your package. Automation also saves you time—instead of remembering and typing long command sequences, you can run everything with simple commands or have workflows run automatically when you push code to GitHub.

What you’ll learn

This section will walk you through two key automation strategies for Python packages:

Task runners help you automate common development tasks locally— things like running tests, building documentation, formatting code, and checking for errors. Instead of typing out long command sequences every time, you define tasks once and run them with simple commands. Task runners like Hatch and Nox also manage isolated environments for different workflows, ensuring you have the right dependencies for each task.

Continuous Integration (CI) takes automation further by running your tests and checks automatically every time code is pushed to GitHub or when someone opens a pull request. CI ensures that all changes are tested across different Python versions and operating systems before they’re merged. You can also use Continuous Deployment (CD) to automate publishing your package to PyPI and deploying your documentation.

Together, task runners and CI/CD create a robust development workflow that makes your package easier to maintain and more welcoming to contributors.