deslop

Removes AI-generated clutter from code while preserving architectural intent.

Updated Aug 27, 2026
One-click install
npx skills add https://github.com/seanwachs/cursor-skills --skill deslop-seanwachs
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: deslop
Source: https://github.com/seanwachs/cursor-skills/tree/main/.agents/skills/deslop
Command: npx skills add https://github.com/seanwachs/cursor-skills --skill deslop-seanwachs

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill helps developers remove AI-generated code slop, reduce defensive bloat, and enforce consistent naming and structure, saving time during code reviews.

Core Features & Use Cases

  • Identify and remove unnecessary comments, verbose patterns, and defensive checks that do not align with local style.
  • Normalize naming, simplify deeply nested code, and enforce architectural decisions without changing behavior.
  • Use Case: When reviewing a feature branch populated with AI-generated boilerplate, deslopping cleans the code before review.

Quick Start

Analyze a feature branch and remove AI slop while preserving architecture.

Frequently Asked Questions about deslop

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

FAQPage Schema
How do I remove AI-generated code slop from a feature branch?

Remove AI-generated code slop from a feature branch by analyzing commits to delete unnecessary comments, verbose patterns, and defensive checks while preserving the project's architectural intent and behavior.

What is code deslopping and when do I need it?

Code deslopping is the process of identifying and removing AI-generated boilerplate, defensive bloat, and verbose naming. You need it when AI-generated noise obscures readability and slows down code reviews.

How do I clean up verbose comments and defensive patterns without changing program behavior?

Clean up verbose comments and defensive patterns by normalizing naming, simplifying deeply nested code, and enforcing architectural decisions. This process removes structural noise without altering the underlying program behavior.

Does deslopping preserve existing architectural patterns like two-layer model splits and explicit mappers?

Deslopping preserves existing architectural patterns by enforcing project-specific rules like two-layer model splits, explicit mappers, dual-identity tool nodes, and SharedContextData with MessageCaptureHandler while recommending minimal changes.

What is the best way to tidy branches populated with AI boilerplate before a code review?

The best way to tidy branches populated with AI boilerplate is to apply deslopping, which targets and removes AI-generated noise, normalizes local style, and enforces structural consistency before submitting code for review.

Are there limitations when removing AI slop from deeply nested code structures?

A limitation of removing AI slop is that changes must be minimal to preserve architectural intent. Complex, deeply nested code structures require careful normalization to ensure defensive checks are removed without altering behavior.