ai-slop-cleaner

You remove AI slop by applying a regression-tests-first cleanup workflow that locks behavior during refactoring. This process requires explicit regression tests and bounded, stepwise cleanup passes to safely eliminate dead code and needless abstractions without altering functionality.

Regression-tests-first code cleanup is a workflow that locks existing behavior with explicit tests before modifying code structure. You need it during refactor sessions to safely remove AI-generated slop like duplication, dead code, and needless abstractions while ensuring no functional regressions occur.

You categorize and fix code smells by applying a bounded cleanup plan that identifies specific issues like dead code, duplication, boundary violations, and missing tests. This stepwise process uses per-pass verification and quality gates to ensure safe, auditable structural improvements.

Yes, explicit regression tests are required to clean up redundant code patterns. The cleanup workflow enforces a regression-tests-first approach to lock behavior, meaning you must provide these tests to verify each pass and ensure the removal of AI slop does not introduce regressions.

The best way to plan code refactoring for production-like environments is using a bounded, stepwise cleanup plan with explicit priorities and reversibility. This guided workflow ensures evidence-dense reporting and per-pass verification to guarantee safe, auditable changes during execution.

You should avoid automated code cleanup workflows when you cannot establish explicit regression tests or a bounded cleanup plan. Without these prerequisites, per-pass verification cannot guarantee that behavior is preserved, making the removal of AI slop unsafe and unauditable.