ai-slop-cleaner

To clean up AI-generated code without behavior changes, you need a deletion-first workflow that requires a cleanup plan, prioritizes removing bloated code over adding new code, and validates changes using targeted regression checks.

Removing repetitive patterns from AI-generated code is best handled by a bounded cleanup workflow that operates on a restricted file scope, ensuring the cleanup pass reduces bloat and over-abstraction while preserving the original intended functionality.

Yes, you can run a review-only anti-slop pass to identify AI code slop by using the review mode flag, which evaluates the targeted files and surfaces bloated or weakly tested patterns before committing to actual code deletion.

Safely refactoring AI code slop involves limiting the surface area to a scoped file list, generating an explicit cleanup plan, prioritizing minimal and reversible diffs, and validating the refactoring with regression tests.

You should restrict file scope during an AI code cleanup when working within bounded tasks in real-world projects, as limiting the surface area ensures the anti-slop pass remains focused, reduces regression risks, and produces minimal, reversible diffs.

AI code cleanup requires a planning step to ensure proactive risk management, allowing the workflow to prioritize deletion over addition, maintain minimal and reversible diffs, and validate behavior preservation through targeted regression checks.