name: antivibe description: Code learning and audit framework. Analyze any codebase — new, legacy, or AI-generated — and produce educational explanations or architectural audits. Use when the user wants to understand WHAT and WHY behind any code, not just accept it. triggers:

• phrase: "/antivibe"
• phrase: "deep dive"
• phrase: "anti-vibecode"
• phrase: "why did AI write"
• phrase: "learn from this code"
• phrase: "understand what AI wrote"
• phrase: "explain what AI wrote"
• phrase: "walk me through"
• phrase: "explain this file"
• phrase: "explain this codebase"
• phrase: "analyze this module"
• phrase: "audit this"
• phrase: "just the trade-offs"
• phrase: "what should I worry about"
• phrase: "code review"

AntiVibe - Code Learning & Audit Framework

Purpose

AntiVibe generates learning-focused explanations or architectural audits of any code — AI-generated, legacy, or otherwise. It helps developers 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)

Works on any codebase — you don't need recent git history or AI-authored files.

When to Use

Use AntiVibe when:

1. Manual invocation: User types /antivibe or "deep dive"
2. Post-task learning: After a feature/phase completes, user wants to learn from it
3. Legacy codebases: User wants to understand existing code they didn't write
4. Proactive: User says "explain what AI wrote", "walk me through", "audit this", or points at a file/directory

What AntiVibe Produces

Output saved to deep-dive/ folder as markdown:

deep-dive/
├── auth-system-2026-01-15.md
├── api-layer-2026-01-15.md
└── database-models-2026-01-15.md

The exact sections depend on the output mode (see Output Mode):

Configuration

Known Concepts (Skip List)

Concepts listed here will not be explained in full — the explainer will only note that they were used and in what context. Edit this list to match your current knowledge.

known_concepts:
  - async/await
  - React hooks
  - REST APIs

Output Mode

Controls how much detail is generated per run. Default is compact to keep token costs low.

output_mode: compact

Override inline in your request:

• "/antivibe full", "full deep dive", "include resources" → full mode
• Default: compact

Default Skill Level

Sets the explanation depth when no level is specified in the request. Options: junior, mid, senior. Default: mid.

default_level: mid

Level can also be specified inline in the request:

• "explain for a junior", "I'm new to this" → junior
• "I know the basics", "mid level" → mid
• "senior mode", "skip the basics", "just the trade-offs" → senior

Workflow

Step 0: Apply User Configuration

Before analyzing, read the configuration above:

• Load the known_concepts skip list. Any concept in this list will be acknowledged in one sentence instead of fully explained.
• Detect the skill level: check the user's request first (inline phrases take priority), then fall back to default_level. Apply this level consistently throughout the entire output.
• If level = senior, route to agents/auditor.md instead of continuing this workflow.

Step 1: Identify Code to Analyze

Use the first applicable mode:

1. Explicit — User named specific files, a directory, or a module in their request → use those directly. No git needed. Example: "explain src/auth/" or "walk me through api/routes.py".

2. Recent — No explicit target given, project is a git repo, and git diff HEAD has output → use those changed files (current behavior for post-AI-task learning).

3. Scan — No explicit target, no usable git diff (legacy project, no recent changes, or not a git repo) → ask the user: "Which file, directory, or module would you like to analyze?" Do not attempt to guess.

The code does not need to be AI-generated. AntiVibe analyzes any code.

Step 2: Analyze Code Structure

For each file:

• Identify main purpose and responsibilities
• Note key functions, classes, modules
• Identify design patterns used (factory, singleton, observer, etc.)
• Find any complex logic or algorithms

Step 3: Explain Concepts

For each concept/pattern found:

• What: Plain-language explanation
• Why: Why this approach was chosen over alternatives
• When: When to use this pattern (with context)
• Alternatives: Other approaches and trade-offs
• Prerequisites: 2–4 foundational concepts the developer must understand first (e.g., "To understand JWT, you need: HTTP request/response, Base64 encoding, cryptographic signing")

Step 4: Find External Resources

Only run this step in full mode. Skip entirely in compact mode.

Search for and include:

• Official documentation for libraries/frameworks used
• Quality tutorials or blog posts
• Video resources (if available)
• Related concepts for further learning

Step 5: Generate Output

Create markdown file in deep-dive/ folder:

• Name format: [component]-[timestamp].md
• Detect output mode from the request or output_mode config (default: compact)
• Compact mode: Use the compact template. No line-by-line, no resources, no Next Steps. Max 5 files — if more are in scope, summarize extras in one line each and offer to go deeper.
• Full mode: Use the full template from templates/deep-dive.md. Include all sections. No 5-file limit — analyze every file in scope; for very large inputs, split the output across multiple deep-dive files rather than truncating.
• Make it educational, not just descriptive

Auto-Trigger Configuration

AntiVibe can be configured to auto-trigger via hooks:

• SubagentStop: After a Task completes a feature
• Stop: At session end

To enable auto-trigger, configure hooks in your project (see hooks/hooks.json).

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 Google 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

Dependencies

Optional scripts in scripts/ folder:

• capture-phase.sh - Detect implementation phase boundaries
• analyze-code.sh - Parse code structure
• find-resources.sh - Search for external resources
• generate-deep-dive.sh - Create markdown output

These are helpers - you can also do everything via direct code analysis.

Examples

Input: "Explain the auth system Claude wrote" (recent AI code) → Mode: Recent (git diff). Output: deep-dive/auth-system-2026-01-15.md

Input: "Walk me through src/payments/" (explicit target — legacy codebase) → Mode: Explicit. Analyzes files in that directory directly, no git needed.

Input: "Deep dive" (no target, legacy project with no recent changes) → Mode: Scan. Asks: "Which file or module would you like to analyze?"

Input: "Audit this, just the trade-offs" (senior mode) → Routes to agents/auditor.md. Produces architectural audit, not an explanation.

AntiVibe

✨ What is AntiVibe?

AntiVibe is a code learning & audit framework that transforms any code — AI-generated, legacy, or otherwise — into educational content or architectural audits. You don't need recent git history or AI-authored files; point it at any file, directory, or module. 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 — and for code you already know, it audits the trade-offs instead.

🎯 Features

🚀 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 (compact, mid-level by default)
/antivibe full                   # Full deep dive with resources + line-by-line
"deep dive"                      # Analyze recently written code (git diff)
"walk me through src/auth/"      # Explain an explicit file or directory
"explain this codebase"          # Understand existing / legacy code
"learn from this code"           # Generate learning guide

Tune the depth and detail inline:

"explain for a junior"           # Define terms, analogies, full snippets
"I know the basics"              # Mid level — focus on design decisions
"audit this, just the trade-offs"  # Senior mode — routes to the auditor agent

📁 Output Example

Run a full deep dive (/antivibe full) and get a file like the one below. The default compact mode produces a shorter version — Overview, Key Components, and Concepts (what + why) — without the walkthrough or resources.

# 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

Behavior (in SKILL.md)

Tune how AntiVibe analyzes code by editing the config blocks in SKILL.md:

output_mode: compact      # compact (default) or full
default_level: mid        # junior, mid, or senior
known_concepts:           # skipped in explanations (one-line note only)
  - async/await
  - React hooks
  - REST APIs

All three can also be overridden inline in a request (e.g. "/antivibe full", "explain for a junior", "just the trade-offs").

Auto-Trigger Hooks

Enable automatic deep-dive generation after task completion:

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

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 learning-focused explanations
│   └── auditor.md              # Subagent for senior-level architectural audits
├── 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

• Setup Guide - Detailed installation
• Skill Format - Claude Code skills

⚠️ License

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