ai-slop-cleaner

Name: ai-slop-cleaner
Availability: InStock
Author: gtpgg1013

Enforce a regression-tests-first cleanup workflow for AI-generated code.

Updated Mar 21, 2026

Search Tags:#testing #debugging #cleanup #refactor #code-quality #regression-tests #ai-code

Ready to run

npx skills add https://github.com/gtpgg1013/claude-skills-collection --skill ai-slop-cleaner-gtpgg1013

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: ai-slop-cleaner
Source: https://github.com/gtpgg1013/claude-skills-collection/tree/main/skills/agents/ai-slop-cleaner
Command: npx skills add https://github.com/gtpgg1013/claude-skills-collection --skill ai-slop-cleaner-gtpgg1013

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Reduce AI-generated slop in code and artifacts by applying a regression-tests-first cleanup workflow that preserves behavior while systematically removing smells.

Core Features & Use Cases

Regression-tests-first: lock behavior before edits to ensure stability.
Structured cleanup passes: remove smells in defined, reversible steps.
Evidence-rich workflow: plan, execute, verify with tests and diffs.
Use Cases: proactively clean bloated AI-generated code, wrappers, or scaffolds.

Quick Start

Start the cleanup by running the guided regression-tests-first pass on the target codebase.

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?▼

To clean up AI-generated code without breaking behavior, apply a regression-tests-first workflow that locks existing functionality before removing duplication, dead code, or brittle boundaries, ensuring systematic cleanup while preserving the original logic.

What is a regression-tests-first cleanup workflow for AI code?▼

A regression-tests-first cleanup workflow is a structured process that locks code behavior with tests before executing bounded, reversible passes to remove AI-generated smells, verifying stability through diffs and linting after each step.

How do I refactor AI-assisted codebases to improve maintainability?▼

You refactor AI-assisted codebases by defining a cleanup plan, executing bounded passes for identified smells like wrappers or scaffolds, and verifying changes through tests and linting to ensure maintainability without altering behavior.

When should I use a structured cleanup pass on bloated AI code?▼

You should use a structured cleanup pass on bloated AI code when duplication, dead code, or brittle boundaries hinder maintainability, requiring a defined plan and evidence-rich verification to safely remove scaffolds.

Does AI code cleanup require existing tests to start refactoring?▼

AI code cleanup requires defining regression tests first to lock behavior before edits, ensuring that the structured cleanup passes remain reversible and verifiable through testing and diffs throughout the refactoring process.