ai-slop-cleaner

Behavior-preserving code cleanup removes unnecessary repetitions, dead code, and excessive abstractions from AI-generated codebases while enforcing regression tests to verify safety. A separate reviewer-only mode ensures no unintended changes are introduced.

The best way to remove excessive abstractions is applying a behavior-preserving cleanup that uses a deletion-first strategy to remove unnecessary code elements. This ensures the program's intended behavior remains intact while reducing AI code slop.

Yes, AI code cleanup supports scoped execution, allowing you to bound the cleanup process to specific files or file lists for focused refactoring without touching the entire codebase.

Yes, the code cleanup process is compatible with GitHub Copilot CLI and Ralph integrations, allowing you to call it as a cleanup pass within your existing toolchain for integrated workflows.

To verify no unintended changes were introduced during AI code refactoring, you can enable a separate reviewer pass that checks the code alongside enforced regression tests to ensure behavior preservation.

You should use a deletion-first strategy for AI code cleanup when you need to safely remove dead code and unnecessary repetitions post-AI generation, relying on regression tests to confirm the intended behavior remains unchanged.