ai-slop-cleaner

To clean up AI generated code safely, enforce a regression-tests-first workflow that validates behavior after every edit. This approach runs bounded cleanup passes on changed files and verifies stability with targeted regression tests.

The best way to remove dead code and duplicates is through structured cleanup passes executed in a defined order. This workflow targets bloated code paths by explicitly categorizing code smells before performing scoped edits.

A regression-first cleanup workflow handles refactoring by creating a structured cleanup plan, running targeted edits for specific code smells, and validating the results with regression tests to ensure behavior stays stable.

Yes, you can scope AI code cleanup to only changed files or a user-specified scope. Bounding the cleanup scope limits risk and impact by preventing the workflow from modifying unrelated code paths.

An automated code quality cleanup pass involves dead code deletion, duplicate removal, and naming or error handling cleanup. These passes are executed in a defined order and conclude with test reinforcement and an evidence-dense finish report.