ai-slop-cleaner

To refactor AI-generated code without breaking functionality, run regression tests first to establish a behavior baseline, then systematically identify and remove code smells in a controlled manner. This preserves behavior while improving readability and maintainability.

Common code smells in AI-generated code include duplicated logic, redundant patterns, and low-signal implementations. A structured cleanup workflow targets these issues systematically, refactoring file by file to raise code quality and reduce slop while maintaining performance.

Yes, you can run cleanup on a specific list of files. The workflow accepts scoped file lists for localized cleanup, allowing you to target individual files like 'src/main/python/slop_code.py' rather than processing the entire project directory.

The best way to reduce slop in AI-driven development is to apply a structured post-implementation cleanup workflow that uses regression tests to preserve behavior and removes code smells systematically, raising the overall signal quality of the generated codebase.

Yes, regression tests are required before cleaning up AI code. The workflow mandates a regression-tests-first approach to ensure existing functionality remains unchanged after refactoring, preventing behavioral drift during the code smell removal process.