slop-cleaner

Identify and remove AI-generated code slop from touched files.

1|1|Updated Apr 13, 2026
One-click install
npx skills add https://github.com/Cheggin/request-for-startups --skill slop-cleaner
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: slop-cleaner
Source: https://github.com/Cheggin/request-for-startups/tree/main/skills/slop-cleaner
Command: npx skills add https://github.com/Cheggin/request-for-startups --skill slop-cleaner

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Clean AI-generated code slop with a regression-safe, deletion-first workflow to keep code lean and preserve behavior.

Core Features & Use Cases

  • Bounded cleanup workflow for dead code, duplication, needless abstraction, boundary violations, weak tests, and unnecessary dependencies.
  • Stepwise process to identify changed files, classify smells, lock tests, delete dead code, remove duplication, and simplify abstractions with safety checks.
  • Use case: after a feature adds code, run the slop-cleaner to prune unused wrappers and redundant logic before merging.

Quick Start

Identify changed files, run the slop-cleaner workflow, and review the resulting report.

Frequently Asked Questions about slop-cleaner

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

FAQPage Schema
How do I remove AI-generated code slop after a feature implementation?

Code cleanup is the targeted removal of dead code, duplication, and needless abstraction from touched files. It is needed after adding features to prune unused wrappers and redundant logic, keeping the codebase lean while preserving existing behavior.

What is the best way to clean up code duplication without breaking existing tests?

Run a stepwise cleanup process to identify changed files, classify code smells, lock tests, delete dead code, remove duplication, and simplify abstractions with safety checks, concluding with a structured cleanup report of the changes.

Can I run a safe refactoring pass on my codebase without changing application behavior?

A code cleanup workflow specifically targets AI-generated code slop like dead code, duplication, needless abstraction, boundary violations, weak tests, and unnecessary dependencies, distinguishing it from broader refactoring approaches.

When should I avoid a deletion-first code cleanup approach?

Avoid deletion-first code cleanup when your codebase lacks weak test coverage, as the workflow requires locking tests before and after removing dead code and duplication to verify that application behavior remains completely unchanged.