ai-slop-cleaner

To clean up AI-generated code without breaking behavior, use a regression-tests-first workflow that deletes dead code and refactors noisy paths while validating that existing functionality remains intact.

The best way to remove dead code and duplicates from AI-assisted development is to perform explicit cleanup passes in order, starting with dead code deletion and duplicate removal, followed by naming and error handling cleanup.

Yes, you can restrict AI code cleanup to changed files by setting a bounded scope, which confines the refactoring and dead code deletion to a defined scope and prevents blast-radius changes across the entire project.

A regression-tests-first workflow handles code refactoring by reinforcing tests before modifying code, ensuring that dead code deletion and duplicate removal passes preserve the original behavior and produce evidence-rich reports.

Automated AI slop cleanup is limited by its scope boundaries; it should not be used for unbounded refactoring outside requested files, and it relies on existing regression tests to safely verify behavior preservation.