ai-slop-cleaner

To remove AI-generated code slop safely, apply a deletion-first cleanup workflow that eliminates dead or duplicated code while mandating regression verification to preserve existing behavior. This approach minimizes risk to your Flutter/Dart codebase by validating changes with evidence-based reporting.

A deletion-first code cleanup strategy targets AI slop by prioritizing the removal of bloated patterns and redundant code over adding new abstractions. It enforces bounded cleanup tasks and relies on regression verification to ensure functionality remains intact during the refactoring process.

You can review AI code refactoring changes by enabling a reviewer-only mode via the --review flag for stricter oversight. This enforces evidence-dense reporting and regression verification, allowing you to validate that the cleanup strictly preserves existing software behavior.

This AI slop cleanup workflow is designed specifically for bounded code-cleanup tasks to minimize risk. Applying it to large, unbounded software engineering changes is not recommended because the deletion-first strategy requires strict regression verification to safely preserve existing behavior.

The code cleanup workflow applies to common AI-generated slop scenarios across Flutter/Dart codebases. It uses a bounded cleanup strategy with regression verification to safely remove bloated patterns while preserving behavior in these specific environments.