AI Code Audit Report
for vibe-coded apps.
Your AI-generated app works — but is it ready to clean up, review, refactor, or ship? LegacyDoc AI turns your local codebase into an audit-ready context pack you can act on or share with any AI tool.
Project overview
taskflow-mvp · Next.js 15 (App Router) · Supabase · Tailwind · 12k LOC · built with Cursor + Claude Code over 3 weekends. Working MVP, no tests, no docs.
Architecture map
app/
├── (auth)/ # sign in / sign up / forgot password
├── dashboard/
│ ├── tasks/ # CRUD + filters ← largest surface
│ ├── projects/ # grouping
│ └── settings/ # preferences
├── api/
│ ├── tasks/route.ts
│ └── webhooks/
└── lib/
├── supabase.ts # mixed server/client usage
├── auth.ts
└── ai.ts # behind feature flag Areas to inspect
- app/api/tasks/route.ts — input parsing
- lib/supabase.ts — server/client boundary
- TaskList.tsx — 380 lines, split candidate
- lib/ai.ts — feature flag + timeout
- next.config.mjs — env loading & image domains
Cleanup priorities
- Split TaskList into TaskList + TaskRow + TaskFilters
- Move DB-only code to server modules
- Unified API input parsing helper
- Add JSDoc to public exports in lib/*
- Document required env vars in README
AI review notes
Use this context pack with Claude Code / Cursor / Codex. Suggested prompt: "Use the architecture map and module summaries below as project context. Help me split TaskList.tsx into smaller components without changing behavior."
Sample format only — not a real customer report and not a security audit.
The problem with vibe-coded apps
Six recurring patterns that make AI-generated codebases hard to clean up safely.
Hidden architecture debt
No one documented how modules connect or why decisions were made.
Duplicated logic
AI tools often generate similar code in multiple places without noticing.
Missing docs
Functions, APIs, and configs have no explanation for the next person.
Unclear module ownership
Hard to tell what each folder does or which parts are safe to change.
Risky dependencies
Outdated packages, insecure configs, or unused code hiding in plain sight.
No cleanup plan
You know it needs work, but don't know where to start or what's safe.
Why vibe code cleanup starts with an audit
Before you clean up, refactor, hand off to a freelancer, or let another AI agent touch the code — you need context. Without it, cleanup is guesswork: you might break working logic, miss the riskiest areas, or spend hours on the wrong files.
An audit-ready context pack gives you (and any AI tool or developer you work with) a shared understanding of the architecture, risk areas, missing documentation, and cleanup priorities — before a single line changes.
What the audit-ready context pack includes
How to use it
-
01
Open your project locally in VS Code.
-
02
Run LegacyDoc AI from the sidebar.
-
03
Review the generated context pack and report.
-
04
Share selected context with Claude Code, Cursor, Codex, or a cleanup specialist.
-
05
Plan small, reviewable cleanup and refactor steps.
When to use this report
Before hiring a vibe code cleanup specialist
Hand them a context pack so they can start immediately instead of spending hours onboarding.
Before asking Claude Code, Cursor, or Codex to refactor
Give the AI tool architecture context so suggestions don't break unrelated parts of the app.
Before shipping an AI-generated MVP
Identify documentation gaps, oversized files, and areas to inspect before launch.
Before handing off to a developer
Reduce onboarding time with a clear architecture map, module summaries, and cleanup priorities.
AI code audit vs security audit vs code review
An AI code audit report is the foundation — not a replacement — for the other two.
| Type | What it produces | Who does it |
|---|---|---|
| AI code audit report | Context pack, architecture map, module summaries, cleanup priorities | LegacyDoc AI generates locally; you (or an AI tool) review |
| Security audit | Vulnerability findings, penetration test results, formal security report | Professional security firm or security engineer |
| Code review | Line-level feedback, refactor suggestions, approval/changes | Developer or team peer review |
What to do after the report
-
01
Fix documentation gaps — add missing JSDoc, README, and inline comments.
-
02
Split oversized files into smaller, single-responsibility modules.
-
03
Review environment variable and configuration usage for clarity.
-
04
Create small, reviewable refactor tasks instead of one big rewrite.
-
05
Share the context pack with Claude Code, Cursor, or Codex to guide cleanup.
What it is
A locally-generated context pack with architecture map, module summaries, areas to inspect, and cleanup priorities — designed to be shared with AI tools or human reviewers.
What it's not
- — It does not replace a professional security audit.
- — It does not automatically fix every issue in your codebase.
- — It does not guarantee production readiness.
FAQ
What is an AI code audit?
An AI code audit is a structured review of AI-generated or vibe-coded projects to identify architecture issues, missing documentation, areas to inspect, and cleanup priorities — before you start making changes. It is a form of AI codebase audit focused on understanding and preparing the code.
Is this the same as a security audit?
No. LegacyDoc AI generates a context pack and cleanup checklist. It does not perform penetration testing, vulnerability scanning, or formal security certification.
Can this clean up my vibe-coded app automatically?
No. It generates an audit-ready report so you (or an AI tool) can make informed cleanup decisions. It does not rewrite or modify your code.
How is this different from hiring a vibe code cleanup specialist?
A specialist does the cleanup work. LegacyDoc AI prepares the context pack that makes that work faster and more focused — whether you do it yourself or hire someone. If you are looking for a vibe code cleanup specialist, generating a context pack first lets the handoff go much faster.
Can I use the report with Claude Code, Cursor, Codex, or Copilot?
Yes. The generated context files are designed to be shared with AI coding tools so they can understand your project without you having to explain everything from scratch.
How does this relate to AI-generated code review?
The context pack is the foundation for AI-generated code review. With architecture maps, module summaries, and an AI code verification checklist, you (or an AI tool) can review code with full project context instead of guessing.
Does LegacyDoc AI upload my code?
No. LegacyDoc AI runs inside VS Code. Your code is sent directly to the AI provider you configure, not to RomantiCode servers. You bring your own API key (BYOK).
What kinds of projects work best?
Any local codebase in VS Code — especially AI-generated apps, vibe-coded prototypes, inherited projects, or legacy codebases that lack documentation.
Related resources
Example
Example Report
See a sample AI code audit report for an AI-generated app.
Use case
Vibe Code Cleanup Readiness
Prepare your app for cleanup before hiring a specialist or refactoring with AI.
Resource
AI Code Audit Checklist
A practical checklist to review architecture, docs, and cleanup priorities.
Product
LegacyDoc AI
The VS Code extension that generates the audit-ready context pack.
Audit your vibe-coded app
Runs inside VS Code. BYOK. No code storage or proxying by RomantiCode.