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ai-slop-cleaner

Clean AI-generated code by identifying smells and running regression-guided fixes.

Updated Apr 23, 2026
Search Tags:#ai#cleanup#refactor#tests#code-smells#deslop#regression-tests
One-click install
npx skills add https://github.com/wmasfoe/notch-pet --skill ai-slop-cleaner-wmasfoe
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ai-slop-cleaner
Source: https://github.com/wmasfoe/notch-pet/tree/main/.codex/skills/ai-slop-cleaner
Command: npx skills add https://github.com/wmasfoe/notch-pet --skill ai-slop-cleaner-wmasfoe

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Automates disciplined cleanup of AI-generated slop to preserve behavior while reducing bloat.

Core Features & Use Cases

  • Identify and fix code smells (duplication, dead code, naming inconsistencies) before they escape into production.
  • Run regression tests first to lock behavior, then apply scoped, bounded edits across files or features.
  • Generate an evidence-rich finish report detailing changes, remaining risks, and follow-up tasks.

Quick Start

Provide a changed-files list or target scope and run the cleanup workflow to begin.

Frequently Asked Questions about ai-slop-cleaner

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

FAQPage Schema
How do I clean up AI-generated code without breaking existing behavior?▼

You can clean up AI-generated code by running regression tests first to lock behavior, then applying scoped, bounded edits across files to remove duplication, dead code, and needless abstraction while preserving functionality.

What are common code smells produced by AI code generation tools?▼

Common AI code smells include duplication, dead code, needless abstraction, naming inconsistencies, and boundary violations. These issues often escape into production without targeted, multi-pass cleanup workflows guided by regression tests.

How do I refactor AI code bloat safely across a feature set?▼

To refactor AI code bloat safely, provide a changed-files list or target scope and run a bounded, multi-pass workflow that identifies smells, applies targeted fixes in order, and validates changes against regression tests.

Can I use automated cleanup on codebases with missing test coverage?▼

Automated cleanup requires regression tests to guide validation and lock behavior before applying fixes. Codebases with missing test coverage lack the evidence needed to safely remove bloat or restructure AI-generated output without risk.

What's the best way to track changes and remaining risks after refactoring AI code?▼

The best way to track changes is to generate an evidence-rich finish report detailing all modifications, remaining risks, and follow-up tasks after the multi-pass cleanup workflow completes its scoped edits.

Why does AI-generated code need targeted edits instead of full rewrites?▼

AI-generated code needs targeted, scoped edits because full rewrites risk breaking existing behavior. A bounded, multi-pass workflow preserves functionality by fixing identified smells in order while regression tests validate each change.

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