antivibe

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: Manual invocation : User types /antivibe or "deep dive" Post-task learning : After a feature/phase completes, user wants to learn from it Legacy codebases : User wants to understand existing code they didn't write 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. output_mode: compact Mode What's included compact (default) Overview, key components (function-level, one line each), concepts (what + why only). No resources. No line-by-line. Max 5 files. full Everything in compact, plus: line-by-line walkthrough, prerequisites, curated resources, Next Steps. 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 Behavior junior Define all terms. Use analogies. Explain language features. Show full code snippets with inline comments. mid Skip basics. Focus on design decisions and trade-offs. Brief code references only. senior Skip obvious patterns. Focus only on non-obvious choices, edge cases, and architectural trade-offs. 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: 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 ". 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). 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 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 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.