ai-slop-remover

To remove AI-generated slop from a single file, run a cleanup process that strips noisy comments and redundant boilerplate while validating changes against the original semantics to prevent regressions and preserve exact behavior.

AI slop refers to noisy comments, redundant boilerplate, and non-functional patterns. You should clean up this slop from a single file before merging to ensure the codebase remains clean while strictly preserving functionality.

Yes, the slop removal process is designed for per-file cleanup tasks and is suitable for parallel operation, allowing you to process one file per invocation to efficiently clean multiple files simultaneously.

Proper code cleanup prevents regressions by validating changes against the original semantics before finalizing the removal of non-functional patterns. It reports any deviations to ensure exact behavior is strictly preserved.

The best way to refactor a single AI-generated function is to use a safety-focused cleanup approach that removes non-functional slop while validating the refactored code against the original semantics to prevent regressions.

Removing boilerplate code can cause deviations if the cleanup process lacks semantic validation. To avoid this, slop removal must validate changes against the original semantics and report any deviations to preserve exact behavior.