ai-slop-cleaner

To remove AI-generated slop safely, enforce a regression-tests-first workflow that validates behavior preservation. Scope cleanup strictly to changed files, execute structured multi-pass refactoring, and finish with an auditable report documenting all modifications and remaining risks.

A structured cleanup workflow enforces a strict planning phase before any code changes. It categorizes AI slop, bounds scope to changed files, runs defined multi-pass refactoring with regression tests, and produces an evidence-dense report documenting modifications and risks.

Yes, you can bound the cleanup scope strictly to changed files. This Ralph-style workflow targets only the modified code, applying regression tests and lint checks to ensure that refactoring removes AI slop without introducing regressions elsewhere.

You audit code quality after slop removal by generating a concise, evidence-dense report. This final report documents the specific cleanup passes executed, the changes made, and any remaining risks identified during the regression-tests-first workflow.

Use a regression-tests-first approach when cleaning AI-generated slop to guarantee behavior preservation. It is necessary when refactoring changed files, ensuring that multi-pass cleanup, lint, and type checks do not inadvertently break existing application logic.