ai-slop-cleaner

Refactor AI-generated code by removing code smells while preserving behavior with regression tests.

Updated Apr 2, 2026

Search Tags:#refactoring #regression testing #AI code #code cleanup #AI-generated code

Ready to run

npx skills add https://github.com/tuandzung/flink-k8s-ui --skill ai-slop-cleaner-tuandzung

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: ai-slop-cleaner
Source: https://github.com/tuandzung/flink-k8s-ui/tree/main/.codex/skills/ai-slop-cleaner
Command: npx skills add https://github.com/tuandzung/flink-k8s-ui --skill ai-slop-cleaner-tuandzung

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

This Skill helps to reduce slop in AI-generated code by providing a structured cleanup/refactor workflow that preserves behavior and raises signal quality.

Core Features & Use Cases

Regression Tests First: Ensures code behavior remains unchanged after cleanup.
Smell-by-Smell Cleanup: Identifies and removes code smells systematically.
Scoped File Lists: Accepts file lists for localized cleanup.
Ralph Workflow Integration: Supports use within the Ralph workflow for specific file updates.

Quick Start

Use the ai-slop-cleaner skill on the 'src/main/python/slop_code.py' file to begin the cleanup process.

Frequently Asked Questions about ai-slop-cleaner

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I refactor AI-generated code without breaking existing functionality?▼

To refactor AI-generated code without breaking functionality, run regression tests first to establish a behavior baseline, then systematically identify and remove code smells in a controlled manner. This preserves behavior while improving readability and maintainability.

What are common code smells in AI-generated code that need cleanup?▼

Common code smells in AI-generated code include duplicated logic, redundant patterns, and low-signal implementations. A structured cleanup workflow targets these issues systematically, refactoring file by file to raise code quality and reduce slop while maintaining performance.

Can I run cleanup on a specific list of files instead of the whole project?▼

Yes, you can run cleanup on a specific list of files. The workflow accepts scoped file lists for localized cleanup, allowing you to target individual files like 'src/main/python/slop_code.py' rather than processing the entire project directory.

What's the best way to reduce slop in AI-driven development environments?▼

The best way to reduce slop in AI-driven development is to apply a structured post-implementation cleanup workflow that uses regression tests to preserve behavior and removes code smells systematically, raising the overall signal quality of the generated codebase.

Do I need regression tests before cleaning up AI code?▼

Yes, regression tests are required before cleaning up AI code. The workflow mandates a regression-tests-first approach to ensure existing functionality remains unchanged after refactoring, preventing behavioral drift during the code smell removal process.