ai-slop-cleaner

To clean up AI generated code without breaking functionality, apply a regression-safe, deletion-first workflow. This process removes duplication, dead code, and needless abstraction while preserving intended behavior through focused passes and quality gate verification.

The best way to remove dead code and needless abstractions is using a structured, deletion-first cleanup workflow. This approach targets specific code smells across focused passes and verifies critical behaviors through quality gate checks before reporting changes.

Yes, you can review AI code for duplication and boundary violations without automatic modification by enabling a reviewer-only mode. This mode performs a separate review pass to assess cleanup integrity and identify code slop without applying deletions.

Yes, regression-safe code refactoring requires a structured plan before removing bloat. The workflow mandates focused passes for specific smells, targeting issues like repetitive code and boundary violations, followed by quality gate verification to ensure maintainability.

AI generated code often contains bloat and redundant helper functions due to overly abstract generation patterns. A targeted cleanup workflow removes this slop by deleting dead code and simplifying complex module structures while ensuring tests cover critical behaviors.

You should avoid using a deletion-first workflow for code cleanup when your module lacks test coverage for critical behaviors. Without focused testing and quality gate verification, deleting AI code slop and needless abstractions risks altering intended functionality.