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RomantiCode · LegacyDoc AI

Audit and verify AI-generated code in VS Code before cleanup, refactor, or launch.

This is the public profile for RomantiCode and our flagship product, LegacyDoc AI — a VS Code extension that turns real-world codebases (legacy projects, AI-generated MVPs, vibe-coded prototypes, inherited apps) into documented, audit-ready projects.

Not an open-source repository. This repository contains no product source code. Source code for the commercial VS Code extension is not published here. This is a public product profile and documentation entry point — it exists to give Google, users, and developer-tool directories a stable, citable URL for the product. The extension is distributed only via the VS Code Marketplace.


What we do

LegacyDoc AI generates, from your local codebase inside VS Code:

  • Inline JSDoc, comments, and explanatory docstrings
  • Standalone Markdown documentation files
  • Mermaid architecture diagrams
  • Module and folder summaries
  • Areas to inspect and cleanup priorities
  • AI code verification scope for architecture, risky flows, generated assumptions, and review evidence
  • An AI-ready context pack for Claude Code, Cursor, or Codex

It is built for the workflow developers actually face in 2026:

The AI-generated app runs. Now what do you clean up, what's safe to refactor, and what context do you hand to the next developer or AI tool?


Links

Product

Tools and examples

Use cases and resources

Current SEO target pages

  • Vibe coding cleanup services — prepare an AI-generated app with an audit report, architecture map, cleanup priorities, and a quote-ready handoff before hiring cleanup help.
  • Vibe code cleanup — prepare the architecture map, risk notes, cleanup priorities, and quote pack before refactoring AI-generated code.
  • Vibe code cleanup checklist — run a cleanup checklist before refactor, AI coding agent work, or a specialist quote.
  • Vibe code cleanup brief template — copy a quote-ready brief for product context, architecture notes, risk areas, cleanup priorities, and verification.
  • Code graph for coding agents — estimate token, tool-call, and context prep savings before adopting graph-based agent workflows.
  • LLM cost regression checker — estimate whether a pull request, model switch, reviewer agent, or tool-output change could raise recurring AI coding cost.
  • AI quota tracker — plan Claude, Codex, Gemini, Copilot, and coding-agent quota usage before a team runs out mid-task.
  • MCP security scanner — review MCP server tools, file access, secrets, network egress, prompt injection risk, and agent trust before connecting AI coding workflows.
  • npm scope compromise checker — review lockfiles, lifecycle scripts, registry sources, and scoped-package changes before AI agents or CI trust a dependency update.
  • Audit AI code — generate an audit-ready context pack before cleanup, refactor, review, or launch.
  • AI code audit cost calculator — estimate discovery hours, report scope, and review budget before asking for an AI-generated code audit quote.
  • AI code audit report template — copy the report structure for architecture context, inspection areas, cleanup priorities, and AI-ready handoff notes.
  • AI codebase understanding — create a codebase map and PROJECT.md-style brief for Claude Code, Cursor, Codex, and other AI coding tools.
  • AI codebase map example — study a sample codebase map with entry points, module ownership, data flow, risk areas, and review boundaries.
  • Codebase to LLM file packer — choose files, map repo context, remove secrets, and build a clean LLM-ready bundle before pasting code into Claude, ChatGPT, Gemini, Cursor, or Codex.
  • How to audit Bolt app code — review routes, Supabase policies, secrets, payments, failure paths, and cleanup handoff before launching a Bolt-built app.
  • How to audit Lovable app code — review routes, Supabase policies, secrets, payments, failure paths, and cleanup handoff before launching a Lovable-built app.
  • AI generated code architecture audit — map entry points, ownership, data flow, generated assumptions, and cleanup risk before AI agents or developers refactor an AI-built app.
  • PROJECT.md template for AI coding agents — copy a project context template for Codex, Claude Code, Cursor, Copilot, and other AI coding agents.
  • Claude Code context file checklist — prepare CLAUDE.md, PROJECT.md, AGENTS.md, repo maps, commands, boundaries, and verification notes before a Claude Code session.
  • Cursor rules vs AGENTS.md — decide which instructions belong in Cursor rules, AGENTS.md, PROJECT.md, and generated audit reports.
  • VS Code AI extension permissions checklist — review workspace access, provider routing, terminals, secrets, VS Code profiles, and approval evidence before installing AI coding extensions.
  • Vibe coding cleanup specialist — understand cost, scope, and handoff prep before hiring a cleanup specialist.
  • AI app launch audit — request launch-readiness review before real users, demos, cleanup services, or production handoff.
  • AI app launch audit request template — copy a request brief before asking for launch-readiness review, cleanup quote, investor demo check, or developer handoff.
  • Legacy code documentation — generate JSDoc, Markdown docs, architecture maps, module summaries, and AI-ready handoff context before cleanup.
  • AI code audit pricing — compare free and Pro options for audit-ready docs, cleanup handoff, and VS Code context packs.
  • VS Code extension privacy policy — review local-first code handling, BYOK provider keys, no RomantiCode code storage, and no extension telemetry.
  • VS Code extension terms of service — review license scope, activation, acceptable use, support boundaries, and AI output responsibility.
  • AI coding model comparison — compare new coding models with the same context pack, task boundary, and review gate.
  • Composer 2.5 — prepare a Cursor Composer 2.5 context pack before long-running coding tasks.
  • Gemini 3.5 Flash — plan Gemini 3.5 Flash coding-agent work with repo context and review evidence.
  • Gemini Omni — turn multimodal product inputs into a reviewable implementation brief.

External listings

Legal


How it works

  1. Install the extension from the VS Code Marketplace.
  2. Set your own AI provider API key (Anthropic, OpenAI, Google, xAI, or a custom OpenAI-compatible endpoint).
  3. Right-click a file or folder → generate docs, Markdown, or a full project analysis.
  4. Review the output in a diff preview before anything is written to your files.
  5. Share the generated context with Claude Code, Cursor, Codex, or with a human reviewer.

Pricing

  • Free — 5 generations per day. Inline JSDoc, Markdown, Mermaid diagrams.
  • Pro ($29 one-time) — Unlimited generations, project-level analysis, custom prompt templates, up to 3 devices.

Compare plans: https://www.romanticode.com/pricing/

Get Pro directly: https://www.creem.io/payment/prod_6A1crhhY1jAOyEPrIkJPuY


Privacy and boundaries

Runs inside VS Code + BYOK. No code storage or proxying by RomantiCode.

Your code is sent directly from your machine to the AI provider you configure (Anthropic, OpenAI, Google, xAI, or a custom endpoint). RomantiCode does not see, store, or proxy your code.

LegacyDoc AI does not:

  • Replace a professional security audit
  • Replace SAST, CI, dependency scanning, or compliance review
  • Automatically fix code
  • Guarantee production readiness
  • Modify your code without a diff preview

It helps you understand, document, prioritize, and prepare the codebase before cleanup, refactor, review, or handoff.


Support


More documentation

  • docs/links.md — Canonical URL list for submissions and sharing
  • docs/submission-copy.md — Ready-to-use copy for directory submissions, Marketplace descriptions, and short social posts

© RomantiCode. All rights reserved.

About

LegacyDoc AI — public product profile. VS Code extension for AI code audit reports, documentation, and architecture diagrams. Commercial product; source not published here. Install: https://marketplace.visualstudio.com/items?itemName=ruilegendoc.legacy-doc-ai

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