ai-slop-cleaner

To clean up AI-generated code slop safely, apply a regression-first workflow using deletion-first edits. Remove dead code, duplicated logic, needless abstractions, and boundary leaks while locking behavior with narrow regression tests and verification after each pass.

A deletion-first code cleanup workflow simplifies bloated modules by removing dead code, duplicate logic, and boundary violations before adding anything new. This anti-slop approach prevents accidental regressions by enforcing smell-ordered passes and targeted test gates.

Remove needless abstractions and dead code during a code review follow-up by running incremental cleanup passes on bloated modules. The process enforces smell-ordered deletions, targeted lint and typecheck gates, and produces an evidence-dense report of changed files and remaining risks.

Yes, you can validate a refactoring and cleanup plan without self-approval by using a reviewer-only mode. This separate validation pass checks the cleanup plan, evidence, and changed files to ensure safe code refactoring without mixing writing and approval logic.

Automated AI slop removal for bloated modules requires existing or newly written regression tests to ensure behavior preservation. Without these targeted test, lint, and typecheck gates, safe code cleanup cannot be guaranteed and accidental regressions may occur.

Yes, ai-slop-cleaner works for incremental refactoring passes on modules that work but feel bloated. It drafts a bounded, behavior-preserving cleanup plan and implements safe deletions of dead code, duplicate logic, and boundary leaks using targeted verification gates.