AI Slop Cleaner

A corrective discipline for cleaning AI-generated code. Runs after code generation — whether from run-plan, a manual session, or any other source.

The core problem: LLMs produce code that works but carries distinctive smells. Over-commenting, unnecessary abstractions, defensive paranoia for impossible scenarios, verbose naming. Left unchecked, these accumulate into a codebase that is harder to read and maintain than hand-written code.

This skill removes those smells systematically, one category at a time, without changing behavior.

Hard Gates

These rules have no exceptions.

1. Lock behavior before cleaning. Run existing tests. If coverage is insufficient, add regression tests for the code you're about to touch. No test coverage, no cleanup.
2. One smell category per pass. Do not mix dead code removal with naming fixes. Complete one pass, verify, then start the next.
3. Run tests after every pass. If tests fail, revert the pass and investigate. Do not proceed to the next category.
4. Stay in scope. Only touch files that were generated or modified by AI. Do not expand into "nearby" code that looks like it could use improvement.
5. Preserve behavior exactly. If a cleanup changes observable behavior — even if you think the new behavior is "better" — revert it. Behavior changes require a separate task.

When To Use