ai-slop-cleaner

AI code cleanup removes generated slop like duplication and needless abstraction by enforcing a regression-tests-first workflow. This approach requires targeted tests to lock behavior before any modifications, ensuring safe refactoring without breaking existing functionality.

A regression-tests-first cleanup workflow requires writing targeted tests to lock current behavior before removing code smells like duplication or dead code. This bounded process validates scope, performs stepwise passes, and produces an evidence-rich report of all changes.

Scope AI code refactoring by providing a specific file list or changed feature area to limit cleanup. This bounded approach prevents broad rewrites by focusing only on caller-provided files and validating the scope before any editing begins.

AI-assisted code cleanup removes smells such as duplication, dead code, needless abstraction, and boundary violations. It targets these specific issues within a bounded scope to improve maintainability while preserving original software behavior.

Yes, regression-first cleanup accepts a file list or feature area to limit modifications. It strictly bounds the cleanup scope to caller-provided files, preventing unintended broad rewrites across the entire project.

Avoid bounded AI code cleanup when you lack the ability to define a specific file list or feature area. The workflow requires a bounded scope and a regression test gate, making it unsuitable for unscoped or untested legacy codebases.