ai-slop-cleaner

To clean up AI-generated code while preserving behavior, apply a regression-test-first workflow that categorizes issues by smells like duplication and dead code, delivering minimal-risk edits verified by tests.

The best way to refactor AI code duplication without breaking functionality is using a targeted cleanup pass bounded to changed files, enforcing regression tests before applying edits to ensure behavior remains unchanged.

You can remove AI-introduced dead code within a specific scope by running a cleanup bounded to changed files, categorizing the dead code as a smell, and validating its removal with regression tests.

Yes, you can automate AI code cleanup for boundary violations safely by categorizing them as code smells and applying an auditable plan with minimal-risk edits backed by regression test validation.

AI slop cleanup enforces a regression-test-first workflow, meaning you need to run regression tests on the specified files to verify behavior remains unchanged before and after the cleanup edits are applied.

You should not use automated refactoring for AI code smells when you cannot establish regression tests first, as the workflow relies on test validation to preserve behavior and assess the risk of edits.