ai-slop-cleaner

AI slop cleanup removes duplication, dead code, and needless abstractions while locking intended behavior through regression verification gates. A protect-first workflow ensures deletion-first edits are validated by targeted regression tests before finalizing changes.

Dead code elimination and duplication removal are structured through anti-slop classification, prioritizing safe deletion before risky consolidation. This ordered, smell-focused editing approach targets specific code smells incrementally to simplify bloated codebases without drift.

Yes, an optional reviewer-only mode activated with the --review flag separates writer edits from reviewer approval. This allows you to verify the cleanup plan and evidence before any high-impact changes are finalized on your changed files.

Refactoring over-abstracted code requires a protect-first workflow that applies ordered smell-focused edits and regression verification gates. Removing needless abstractions and boundary leaks is validated by targeted regression tests to prevent drift.

Yes, the cleanup workflow applies to bounded file-scope and changed-files post-review passes. It supports incremental cleanup and anti-slop refactors for weakly tested or repetitive codebases, ensuring changes remain scoped and safe.

A deletion-first approach prioritizes removing dead code and redundancy before risky consolidation to prevent behavior drift. This regression-safe strategy reduces over-abstracted code slop while maintaining locked behavior through verification gates.