ai-slop-cleaner

You can remove AI-generated slop by enforcing a regression-tests-first cleanup workflow that preserves behavior. This method applies structured passes targeting dead code and duplication while using verification gates to prevent regressions.

The best way to refactor AI-affected code is a six-pass cleanup process with evidence collection. This workflow scopes changes to specific feature areas or modified files, minimizing risk while targeting code smells and loose boundaries.

Yes, you can scope AI code cleanup to changed files or specified feature areas to minimize risk and maximize signal. This bounded approach ensures the refactoring process targets only the relevant parts of your codebase.

A test-first cleanup workflow handles dead code and duplication through structured bounded passes. It enforces regression tests, lint, and typecheck gates as verification checkpoints to safely remove AI slop while collecting evidence of preserved behavior.

When removing AI slop, anticipate boundary issues and potential behavior regressions. A six-pass cleanup workflow mitigates these risks by enforcing verification gates and providing a final report detailing all changes and associated risks.