| name | antivibe |
| description | Anti-vibecoding learning framework. Generate detailed explanations of code written by AI with curated external resources for deeper learning. Use when the user wants to understand WHAT and WHY behind AI-generated 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"}] |
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:
- Manual invocation: User types
/antivibe or "deep dive"
- Post-task learning: After a feature/phase completes, user wants to learn from it
- 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
- Why over what - Always explain design decisions
- Context matters - Explain when/why to use patterns
- Curated resources - Quality links, not random Google results
- Phase-aware - Group by implementation phase
- Learning path - Suggest next steps for deeper study
- Concept mapping - Connect code to underlying CS concepts
Dependencies
Optional scripts in scripts/ folder:
capture-phase.sh - Detect implementation phase boundariesanalyze-code.sh - Parse code structurefind-resources.sh - Search for external resourcesgenerate-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
