Give AI coding tools the codebase context they need before they change anything.
Claude Code, Cursor, Codex, and other AI coding tools work better when they understand the project first. LegacyDoc AI generates a compact context pack from your local VS Code workspace: architecture, modules, setup notes, areas to inspect, and handoff-ready project facts.
Runs inside VS Code · BYOK · No code storage by RomantiCode
If you searched
AI codebase context
You probably need a compact project map before asking an AI coding assistant to modify your app.
If you use
Claude Code, Cursor, or Codex
Give the agent project facts, architecture notes, and constraints instead of a vague prompt.
If you need
a PROJECT.md template
Start with purpose, stack, entry points, module summaries, boundaries, and cleanup priorities.
What an AI-ready codebase context pack should include
Useful context is specific enough to guide changes, but small enough to fit into a real AI workflow.
Project overview
Purpose, stack, entry points, run commands, and important environment assumptions.
Architecture map
A Mermaid diagram showing routes, modules, services, data flow, and external dependencies.
Module summaries
Plain-language descriptions of what each folder and important file is responsible for.
Change boundaries
Notes about fragile areas, files to avoid editing blindly, and constraints the agent should respect.
Areas to inspect
Large files, duplicated logic, missing docs, unclear config, and risky surfaces that deserve review.
Handoff notes
A prompt-ready summary that helps a human or AI assistant understand the next task quickly.
Turn project context into a reusable AI coding brief
A good PROJECT.md gives coding agents the same project map a human reviewer would want:
purpose, stack, entry points, architecture notes, module ownership, constraints, and cleanup priorities.
LegacyDoc AI helps you generate that context from the workspace first, then review it before sharing it with an AI tool.
Prompt-ready
Paste the brief into Claude Code, Cursor, Codex, or a review handoff.
Reviewable
Keep the document small enough for a human to check before the next change.
Boundary-aware
Make risky areas and do-not-touch files visible before edits begin.
Reusable
Refresh the context when architecture or ownership changes.
Example PROJECT.md structure
Template# PROJECT.md - AI Coding Context
## Project purpose
This project is a ...
## Stack and runtime
- Framework:
- Language:
- Package manager:
- Database:
- Deployment:
## Entry points
- App entry:
- API routes:
- Background jobs:
- Config:
## Architecture map
See docs/architecture.md or the generated Mermaid diagram.
## Key modules
- src/features/tasks:
- src/lib/auth:
- src/lib/db:
## Rules for AI coding tools
- Read this file before suggesting changes.
- Keep changes small and reviewable.
- Do not rewrite unrelated modules.
- Ask before changing data models, auth, billing, or deployment config.
## Areas to inspect before cleanup
- Large files:
- Missing tests:
- Risky integrations:
- Unclear ownership:
## Cleanup priorities
1. ...
2. ...
3. ...A practical context-first AI coding workflow
Do not start with “refactor this project.” Start by making the project legible.
-
01
Generate context
Open the project in VS Code and generate docs, diagrams, and a project context pack.
-
02
Review the facts
Check the generated overview against the real code before sharing it with an AI tool.
-
03
Ask for a plan
Give the context pack to Claude Code, Cursor, or Codex and ask for a small change plan.
-
04
Change in slices
Apply one reviewable change at a time, using the context pack as the shared project map.
Raw prompts vs codebase context
Without context
- — The AI sees isolated files instead of the project shape.
- — It may miss setup rules, module boundaries, or hidden dependencies.
- — Refactor suggestions become broad, risky, and hard to review.
- — Every new session starts with the same explanation work.
With a context pack
- — The AI starts from a project overview and architecture map.
- — Constraints, risky areas, and important files are visible up front.
- — Cleanup plans can be split into smaller reviewable tasks.
- — Humans and AI tools share the same project brief.
Clear boundaries
This is a documentation and context layer, not a black-box coding agent.
Related resources
Product
LegacyDoc AI
Generate docs, diagrams, and context packs inside VS Code.
Use case
Document Legacy Code
Create documentation for existing or inherited codebases first.
Use case
Vibe Code Cleanup
Prepare AI-generated apps before cleanup or handoff.
Tool
AI Code Audit Report
Turn codebase context into an audit-ready project report.
FAQ
What is AI codebase context?
AI codebase context is a compact project brief that explains the app structure, important modules, architecture, setup assumptions, and areas to inspect before you ask an AI coding tool to make changes.
What should a PROJECT.md for AI coding include?
A useful PROJECT.md should include project purpose, stack, entry points, architecture notes, key modules, change boundaries, areas to inspect, and cleanup priorities. The goal is to help AI coding tools understand the project before making edits.
Can I use this context with Claude Code, Cursor, or Codex?
Yes. LegacyDoc AI can generate AI-readable project context that you can paste or attach when working with Claude Code, Cursor, Codex, or another AI coding assistant.
Does LegacyDoc AI connect to MCP or provide persistent agent memory?
No. This page is about generating static documentation and context files from your local VS Code workspace. It does not claim MCP integration, persistent memory, or dynamic code search.
Will this automatically refactor my code?
No. LegacyDoc AI generates documentation, diagrams, and context. You still decide what to change, review AI suggestions, and apply code edits yourself.
Does RomantiCode store my code?
No. LegacyDoc AI runs inside VS Code. Your code is sent directly to the AI provider you configure with your own API key, not to RomantiCode servers.
Generate codebase context before the next AI coding session
Install LegacyDoc AI in VS Code and create a reviewed project brief from your local workspace.