ai-slop-cleaner

Safe AI code cleanup requires a regression-proof workflow that plans edits first, classifies code smells, applies risk gating, and verifies regressions before changes are committed. This targets only changed files to minimize drift.

Removing AI code slop like dead code and duplication works best through bounded cleanup sessions that target explicitly specified files. This approach applies smell classification and risk gating to preserve intent while trimming noise.

Yes, you can use a reviewer-only mode to inspect planned AI code cleanup changes before execution. This regression-first workflow verifies behavior first, ensuring you maintain explicit scope controls over the refactoring process.

Bounded code cleanup targets only changed or explicitly specified files to minimize risk, rather than processing entire large codebases at once. This scoped approach prevents unintended behavioral drift when removing AI-generated slop.

You should avoid automated AI code cleanup when lacking a defined workflow for plan-first edits and regression verification. Without explicit scope controls and risk gating, removing AI slop can easily introduce behavioral drift or break tests.

Setting up a regression-first workflow for AI code slop involves defining bounded cleanup scopes, planning edits, classifying smells, and verifying regressions. Tell the AI to perform a bounded, post-review cleanup of changed source files.