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review-ai-slop

Detect AI-generated slop patterns in source code and report grouped findings.

6|Updated Feb 25, 2026
Search Tags:#code review#anti-patterns#audit#code-quality#git-diff#ai-detection
One-click install
npx skills add https://github.com/archibate/archibate-skills --skill review-ai-slop
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: review-ai-slop
Source: https://github.com/archibate/archibate-skills/tree/main/skills/review-ai-slop
Command: npx skills add https://github.com/archibate/archibate-skills --skill review-ai-slop

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill identifies common sloppy patterns introduced by AI-generated code that reduce reliability, introduce bugs, or make maintenance harder, helping reviewers focus on real fixes rather than style noise.

Core Features & Use Cases

  • Groups findings by category and reports file path, line number, pattern name, a brief description, and a suggested fix so maintainers can decide on changes.
  • Covers defensive-programming, over-engineering, unnecessary infrastructure, code bloat, testing slop, and other anti-patterns, and applies an external anti-defensive checklist of ten patterns when available.
  • Use cases include pre-merge code audits, triaging AI-assisted patches, and cleaning third-party or generated code before adoption.

Quick Start

Please review for AI slop in the current git diff and report findings grouped by category.

Frequently Asked Questions about review-ai-slop

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

FAQPage Schema
How do I detect AI-generated code slop in a git diff?▼

You can detect AI-generated code slop by scanning the current git diff to produce grouped findings by category, including file path, line number, pattern name, description, and suggested fix for maintainers.

What are common AI code anti-patterns found during code review?▼

Common AI code anti-patterns include defensive-programming, over-engineering, unnecessary infrastructure, code bloat, and testing slop, which are identified and grouped by category to reduce reliability issues and maintenance burdens.

How do I audit third-party or generated code before merging?▼

Auditing third-party or generated code before merging involves applying pattern detection to specified files or the current git diff, generating categorized findings with line numbers and suggested fixes to triage AI-assisted patches.

Does this code review tool apply an anti-defensive checklist?▼

Yes, the code review process applies an external anti-defensive checklist of ten patterns when available, helping reviewers identify defensive-programming and over-engineering slop introduced by AI-generated code.

What is the best way to clean up AI-generated patches in source code?▼

The best way to clean up AI-generated patches is to scan specified files for anti-patterns, producing grouped reports with pattern names and suggested fixes so maintainers can decide on changes before adoption.

When should I not use an automated AI slop detector?▼

You should avoid relying solely on an automated AI slop detector when you need to focus on stylistic noise rather than reliability, as this tool specifically targets anti-patterns that introduce bugs or make maintenance harder.

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