AntiVibe - AI Code Learning Framework

Purpose

AntiVibe generates learning-focused explanations of AI-written code. Not generic summaries - actual educational content that 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)

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. Proactive: User says "explain what AI wrote", "learn from this code", or "understand what AI wrote"

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

Each file contains:

• Overview: What this code does and why it exists
• Code Walkthrough: File-by-file explanation with line-by-line notes
• Concepts Explained: Design patterns, algorithms, CS concepts used
• Learning Resources: Curated docs, tutorials, videos
• Related Code: Links to other files in the codebase

Workflow

Step 1: Identify Code to Analyze

• Check for explicit file list in user request
• Or use git diff to find recently modified/created files
• Or ask user which files/components they want to understand

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

Step 4: Find External Resources

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
• Follow the template in templates/deep-dive.md
• Include code snippets where helpful
• Make it educational, not just descriptive

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" Output: deep-dive/auth-system-2026-01-15.md containing:

• JWT structure explanation
• Password hashing rationale
• Session management concepts
• Learning resources for auth patterns

Input: "I want to understand this API layer" Output: deep-dive/api-layer-2026-01-15.md containing:

• REST design decisions
• Middleware explanation
• Error handling patterns
• Further reading on API design