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ai-slop-cleaner

Identify and remove AI-generated code slop using regression-test-first cleanup passes.

1|Updated Sep 22, 2025
Search Tags:#workflow#cleanup#refactor#code-quality#ai-generated#regression-tests
One-click install
npx skills add https://github.com/prthik/prathik-astro --skill ai-slop-cleaner-prthik
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ai-slop-cleaner
Source: https://github.com/prthik/prathik-astro/tree/main/.codex/skills/ai-slop-cleaner
Command: npx skills add https://github.com/prthik/prathik-astro --skill ai-slop-cleaner-prthik

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Reduce AI-generated slop with a regression-tests-first, smell-by-smell cleanup workflow that preserves behavior and raises signal quality.

Core Features & Use Cases

  • Regression-tests-first cleanup: lock behavior by adding/running targeted regression tests before edits.
  • Bounded scope: operate on a provided file list or specific feature area to avoid broad rewrites.
  • Structured issue categorization: identify Duplication, Dead code, Needless abstraction, Boundary violations, and Missing tests.
  • Ralph-guided workflow: apply passes in sequence (dead code deletion, duplication removal, naming/error handling cleanup, test reinforcement) with verification between passes.
  • Evidence-rich reporting: finish with a concise, evidence-dense report of changes, tests, and remaining risks.

Quick Start

Apply a regression-tests-first cleanup plan to the provided changed files and generate an evidence-dense report.

Frequently Asked Questions about ai-slop-cleaner

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

FAQPage Schema
How do I safely refactor AI-generated code without breaking existing behavior?▼

To safely refactor AI-generated code, apply a regression-test-first cleanup workflow that locks behavior by adding and running targeted tests before making edits. This ensures verifiable improvements while maintaining functionality.

What is the best way to remove dead code and duplication from AI-generated files?▼

The best way to remove dead code and duplication from AI-generated files is using sequential cleanup passes. This structured approach categorizes issues and applies targeted deletions with verification between each pass.

Can I limit code cleanup to only changed files to avoid broad rewrites?▼

Yes, you can limit code cleanup to a bounded scope by providing a specific file list or feature area. This prevents broad rewrites and ensures improvements remain verifiable within the targeted boundary.

How do I report code quality improvements after a refactoring session?▼

You can report code quality improvements by generating an evidence-dense report. This report concisely documents the applied changes, tests reinforced, and any remaining risks identified during the cleanup workflow.

What types of code smells does a cleanup workflow target in AI output?▼

A cleanup workflow targets specific code smells in AI output by categorizing issues into duplication, dead code, needless abstraction, boundary violations, and missing tests for structured removal.

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