deslop-code

Remove AI-generated code slop from feature branch diffs against main.

Updated Jan 17, 2026

Search Tags:#code-review #code-cleanup #git-diff #ai-diffs #style-normalization #defensive-checks #minimal-refactor

Ready to run

npx skills add https://github.com/heykvnzhao/dotfiles --skill deslop-code

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: deslop-code
Source: https://github.com/heykvnzhao/dotfiles/tree/main/skills/deslop-code
Command: npx skills add https://github.com/heykvnzhao/dotfiles --skill deslop-code

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Clean AI-generated code changes by reviewing diffs against main and removing artifacts that distort intent or readability.

Core Features & Use Cases

Diff-based cleanup: Compare your feature branch against main to identify AI-inserted changes and irrelevant noise.
Slop removal: Delete verbose explanations, redundant defensive checks, noisy try/catch blocks, and unnecessary any-casts to align with project conventions.
Style normalization: Reflow and harmonize naming, ordering, error handling, and control flow with the local codebase style.
Minimal impact guarantee: Preserve behavior and perform changes only where needed, with small, targeted refactors when necessary.
Use Case: When you want to align a branch’s edits with the team's conventions and reduce AI-generated clutter before merging.

Quick Start

Ask it to review your current branch against main and clean AI slop from modified files, preserving behavior.

Frequently Asked Questions about deslop-code

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

FAQPage Schema

How do I clean up AI-generated code slop from my feature branch?▼

To clean up AI-generated code slop, diff your feature branch against main to identify and remove verbose explanations, redundant defensive checks, and noisy try/catch blocks. This process aligns the changes with your project's conventions while preserving behavior.

What is AI code slop and how does it affect a codebase?▼

AI code slop refers to artifacts like over-commenting, unnecessary any-casts, and inconsistent style inserted by AI assistants. It distorts code intent and readability, making feature branches noisy and misaligned with local codebase conventions before merging.

How do I normalize code style in a feature branch without changing behavior?▼

You can normalize code style with minimal impact by diffing changes against main and applying targeted refactors. This reflows naming, ordering, and error handling to match the local codebase style, ensuring minimal behavioral changes while cleaning up artifacts.

Does diff-based code cleanup work across multi-language codebases?▼

Yes, diff-based code cleanup works across multi-language codebases by comparing feature branch edits against main. It identifies AI-inserted changes and irrelevant noise regardless of language, applying style normalization and slop removal with a concise per-file summary.

When should I review AI diffs against main before merging?▼

You should review AI diffs against main when you want to align a branch’s edits with team conventions and reduce AI-generated clutter before merging. This ensures diff-based cleanup addresses over-commenting and defensive checks while preserving behavior.

What is the best way to remove unnecessary try/catch blocks and any-casts from AI diffs?▼

The best way to remove unnecessary try/catch blocks and any-casts is to run a diff-based cleanup against main. This targets AI-inserted defensive checks and noisy type casts, deleting them to align with project conventions while performing only minimal, targeted refactors.