ai-slop-cleaner

AI slop cleanup removes duplication, dead code, and needless wrappers while preserving intended behavior. A deletion-first workflow runs ordered smell passes for dead-code removal, duplication consolidation, and naming cleanup, locking changes behind regression tests.

Refactoring AI-generated code bloat involves simplifying weakly structured implementations through a smell-focused cleanup workflow. This process targets duplication, dead code, and boundary leaks sequentially, ensuring behavior preservation by adding the smallest necessary regression tests first.

Yes, a reviewer-only mode drafts and critiques a cleanup plan without performing writer-level changes. This allows you to verify the intended deletion-first edits and smell-pass ordering before any modifications are applied to your source files.

AI slop cleanup supports bounded cleanup by explicit file lists or changed-file scope. This means you can target specific directories or modified files for dead code removal and duplication consolidation without scanning the entire project.

A regression-safe workflow requires adding the smallest regression tests needed first, followed by ordered smell passes. It executes dead-code deletion, duplication removal, naming and error-handling cleanup, then test reinforcement to guarantee behavior preservation.

You should avoid automated refactoring when you lack explicit anti-slop intent or cannot provide regression tests. This cleanup method requires a deletion-first, regression-locked workflow, making it unsuitable for untested codebases or vague refactoring requests.