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To clean a Python project for distribution, remove caches, virtual environments, and build artifacts to produce a minimal, shareable codebase. Use a dry-run preview first to see removals, then execute cleanup to apply changes and sanitize secrets.

Sanitize API keys in settings.yaml by running a project cleanup script with sanitization enabled. The process targets sensitive config values in settings.yaml, removing or masking them to prevent secret leakage when distributing your Python project.

Yes, you can preview file removals by running the cleanup script in dry-run mode. Dry-run previews show exactly which caches, venvs, build artifacts, and data files would be deleted before any actual changes are applied to your project.

To keep data files while removing caches and virtual environments, use the --keep-data safety toggle during cleanup execution. This tailors the workflow to preserve data directories while still removing build artifacts and sanitizing secrets for distribution.

The best way to prepare a reproducible Python distribution workflow is cleaning the project to remove non-essential artifacts. This involves dry-run previewing, then executing removal of caches, venvs, and secrets, ensuring a minimal, safe, reproducible codebase.

You should skip config sanitization during project cleanup when your settings.yaml contains non-sensitive values needed for execution. Use the --no-sanitize toggle to bypass secret removal, keeping config values intact while still removing caches and build artifacts.