Rosetta

Engineering governance and context for AI coding agents — shared instructions, architecture, standards, workflows, and guardrails in every session.

What is Rosetta

https://github.com/user-attachments/assets/6df6e217-3e5c-4691-84ed-7440701a87de

AI coding agents are great — until you try to use them across a real team. Everyone builds their own prompts and instructions, knowledge stays in silos, and the agent — not knowing your architecture or constraints — guesses from a few open files and confidently does the wrong thing.

That's why we built Rosetta — open-source engineering governance and context for AI coding agents. It's not another proprietary agent; it works with the tools you already use (Claude Code, Cursor, Copilot, Codex, and other MCP-compatible agents) and loads your team's shared engineering instructions into every session. Everything is versioned in Git and can run inside your perimeter.

Teach agents how to think, not what to do. The model already knows Python and React; what it lacks is your engineering discipline. That's what Rosetta encodes.

[!NOTE] If you are effectively using your current setup, writing your own skills, and managing AI using your own processes, you probably don't need Rosetta.

User Guide

Rosetta-guided work follows five phases — Prepare → Research → Plan → Act → Validate — with approval gates at the key decision points.

Read more in the User Guide.

Quick Start

Overall Process: Install (plugin preferred) → Initialize workspace → Configure workspace ecosystem (tools, MCPs) → Proceed with Coding, Modernization, Analysis, etc.

Please follow Quick Start Guide which will walk you through the process.

Very detailed information on installation and configuration in all available modes is in Installation Guide.

Skills

Reusable units of work the AI loads on demand. Workflows pick them for you, and most can also be called by name.

Let Rosetta decide

1. rosetta: You want Rosetta to handle it itself. Classifies your request, picks the matching workflow, and runs it end to end.

Understand what exists

1. load-project-context: Gives the AI your project's business context, architecture, and past decisions before any work starts.
2. codemap: Gives the AI a structural map of the repo to navigate by. Uses your LSP or code-graph tooling when available.
3. discovery: Evidence-backed picture of what exists today, including whether this was already attempted and done wrong.
4. reverse-engineering: Recovers domain rules and intent from code into business specs.
5. research: Researches any question grounded in your project, with real sources. Compares options and validates its own conclusions.

Decide what to build