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):

Section	compact (default)	full
Overview, what the code does and why it exists	✅	✅
Key Components / Concepts, design patterns, algorithms, CS concepts used	✅	✅
Code Walkthrough, file-by-file, line-by-line notes	,	✅
Learning Resources, curated docs, tutorials, videos	,	✅
Related Code, links to other files in the codebase	,	✅

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.