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antivibe

Learn what AI writes, not just accept it. A Claude Code skill that turns AI-generated code into educational deep dives.

16von @mohi-devhubvor 3d aktualisiertMITGitHub →

Installation

Kompatibilitaet

Claude Code

Beschreibung

AntiVibe


✨ What is AntiVibe?

AntiVibe is a learning-focused code explanation framework that transforms AI-generated code into educational content. Unlike generic code summaries, AntiVibe helps you understand:

  • What the code does (functionality)
  • Why it was written this way (design decisions)
  • When to use these patterns (context)
  • What alternatives exist (broader knowledge)

The Problem: AI writes code, developers copy-paste it, nobody learns anything.

🛡️ The Solution: AntiVibe explains the reasoning so you actually understand.


🎯 Features

| Feature | Description | |---------|-------------| | Deep Dives | Generate comprehensive learning guides from AI code | | Concept Mapping | Connect code to underlying CS principles | | Curated Resources | Quality links to docs, tutorials, videos | | Phase-Aware | Group explanations by implementation phase | | Auto-Trigger | Optional hooks for automatic generation | | Multi-Language | Works with any language/framework |


🚀 Quick Start

Installation

# Clone the repository
git clone https://github.com/mohi-devhub/antivibe.git

# Install as a global Claude Code skill
cp -r antivibe ~/.claude/skills/antivibe

Usage

/antivibe                        # Start a deep dive
"deep dive"                      # Analyze recently written code
"learn from this code"           # Generate learning guide
"explain what AI wrote"          # Explain specific files
"understand what AI wrote"       # Understand design decisions

📁 Output Example

Generate a deep dive and get a file like:

# Deep Dive: Authentication System

## Overview
This auth system uses JWT tokens with refresh token rotation...

## Code Walkthrough
### auth/service.ts
- **Purpose**: Token generation and validation
- **Key Components**: 
  - `generateTokens()`: Creates access/refresh tokens
  - `verifyToken()`: Validates JWT signatures

## Concepts Explained
### JWT (JSON Web Tokens)
- **What**: Stateless authentication tokens...
- **Why**: Server doesn't need to store sessions...
- **When**: APIs, SPAs, microservices...

## Learning Resources
- [JWT.io](https://jwt.io): Official documentation
- [Auth0 Guide](https://auth0.com/blog): Best practices

Saved to: deep-dive/auth-system-2026-04-10.md


🔧 Configuration

Auto-Trigger Hooks

Enable automatic deep-dive generation after task completion:

# Copy hooks to your project
cp framework/hooks/hooks.json your-project/.claude/hooks.json

| Hook | When | Use Case | |------|------|----------| | SubagentStop | Task completes | Phase-based learning | | Stop | Session ends | End-of-session summary |

Customize Output Directory

Edit scripts/generate-deep-dive.sh:

OUTPUT_DIR="your-folder"  # Default: "deep-dive"

📂 File Structure

antivibe/
├── SKILL.md                     # Main skill definition
├── README.md                    # This file
├── hooks/
│   └── hooks.json              # Auto-trigger configuration
├── scripts/
│   ├── capture-phase.sh        # Detect implementation phases
│   ├── analyze-code.sh         # Parse code structure
│   ├── find-resources.sh       # Find external resources
│   └── generate-deep-dive.sh   # Generate markdown output
├── agents/
│   └── explainer.md            # Subagent for detailed analysis
├── templates/
│   └── deep-dive.md            # Output template
├── reference/
│   ├── language-patterns.md    # Framework-specific patterns
│   └── resource-curation.md    # Curated learning resources
└── docs/
    ├── PLAN.md                  # Planning document
    └── setup.md                 # Detailed setup guide

📚 Principles

  1. Why over what - Always explain design decisions
  2. Context matters - Explain when/why to use patterns
  3. Curated resources - Quality links, not random results
  4. Phase-aware - Group by implementation phase
  5. Learning path - Suggest next steps for deeper study
  6. Concept mapping - Connect code to underlying CS concepts

🛠️ Supported Languages & Frameworks

  • JavaScript/TypeScript: React, Node.js, Express
  • Python: Django, FastAPI, Flask
  • Go: Standard library, Gin, Echo
  • Rust: Standard library, Actix
  • Java: Spring Boot
  • And more - Extensible pattern system

🤝 Contributing

Contributions welcome! To extend AntiVibe:

  1. Add patterns to reference/language-patterns.md
  2. Add resources to reference/resource-curation.md
  3. Customize the template in templates/deep-dive.md

📖 Documentation


⚠️ License

MIT License - Use it, learn from it, share it.


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