vibe-check-mcp

🥈Silver

Real-time anti-pattern detection for AI coding. Prevents overengineering and costly mistakes. Integrates with Claude for collaborative reasoning. Ideal for operations teams using AI tools.

2050Updated Today

Intermediate30min to implementautomation

Saves ~75 min per use

Quick InstallView Source

git clone https://github.com/kesslerio/vibe-check-mcp.git

Works with:

Claude

Overview

About This Skill

Real-time anti-pattern detection for AI coding. Prevents overengineering and costly mistakes. Integrates with Claude for collaborative reasoning. Ideal for operations teams using AI tools.

Setup & Installation

Quick Install

No install command available. Check the GitHub repository for manual installation instructions.

Alternative Install (Git Clone)

git clone https://github.com/kesslerio/vibe-check-mcp

Requirements

Claude Code or compatible AI agent
Works with: Claude

Quick Start Guide

Install the Skill

Copy the install command above and run it in your terminal.

Open Your AI Agent

Launch Claude Code, Cursor, or your preferred AI coding agent.

Try It Out

Use the prompt template or examples below to test the skill.

Customize

Adapt the skill to your specific use case and workflow.

Usage Examples

Prompt Template

Analyze this code snippet for anti-patterns and potential overengineering. [CODE]. The code is part of a [SYSTEM] used by [COMPANY] in the [INDUSTRY] sector. Flag any issues and suggest improvements.

Example Output

# Code Analysis Report

## Detected Anti-Patterns
- **Premature Optimization**: The sorting algorithm is over-optimized for a dataset that rarely exceeds 1000 items.
- **Over-Engineered Error Handling**: The error handling system has 5 layers of nested try-catch blocks for a simple CRUD operation.
- **Unnecessary Abstraction**: The data access layer has been abstracted into 3 separate classes with identical functionality.

## Recommendations
- Simplify the sorting algorithm to use a standard library function.
- Reduce error handling to 2 layers, focusing on critical failure points.
- Consolidate the data access layer into a single class with clear, documented methods.

## Risk Assessment
- **Technical Debt**: High. Current implementation will require significant refactoring in the future.
- **Maintenance Overhead**: Medium. Additional complexity increases debugging time.
- **Performance Impact**: Low. Current optimizations do not provide measurable benefits.