Meta-prompting, context engineering, and centralized instructions management for AI coding agents

Guides AI systems to operate with a deep understanding of system architecture, domain constraints, and engineering standards.
Gives every agent the same context, standards, and guardrails — across any IDE, any team.

Get Started See it in action GitHub

Why AI Agents Need Rosetta

AI coding agents are everywhere. Consistency and context are not.

System Status: Unmanaged AI Agents
MISSED CONVENTIONS
Agents lack your architecture, business rules, and compliance requirements. They produce code that violates conventions, causing expensive rework and high rejection rates.
INSTRUCTIONS DON'T SCALE
Every engineer writes their own prompts — or none at all. Crafting effective instructions takes hours of work per task, they go stale fast, and there's no way to version, review, or govern them.
NO CROSS-IDE REUSE
Prompt libraries are IDE-specific and fragile. Switch from Cursor to Claude Code? Rewrite everything. New model drops? Start over. The same problem gets solved differently on every team.
SILOED KNOWLEDGE
Patterns proven in one project never reach others. Senior expertise stays in people's heads. Breaking changes cascade undetected across services because agents see one repo, not the system.
Rosetta solves this. See how ↓

What Rosetta Adds to AI Coding Agents

Coding agent system prompts handle tool calls and output formatting. They contain no engineering process, no guardrails, no project awareness. They can't — the system prompt doesn't know if you're building a PoC or enterprise software with regulated data. Rosetta fills that gap with instructions that guide the agent through everything it would otherwise skip.

1
Project context before every task. Without Rosetta, agents read a few lines around the problem and guess the rest. With Rosetta, the agent reverse-engineers your architecture, tech stack, and business context during initialization — and reads it before every task. No more blind guessing.
2
Guardrails that actually enforce. Agents don't assess risk, don't protect sensitive data, don't question dangerous actions. Rosetta instructions require the agent to assess risk, mask sensitive data, detect dangerous operations, and follow behavior boundaries — loaded at startup, always active.
3
Human-in-the-loop at decision points. Agents trust user input unconditionally and never stop once started. Rosetta workflows define approval gates after specs, after plans, and before risky actions. The agent stops, asks targeted questions, and waits — instead of getting carried away.
4
Request classification and source of truth. Agents treat every request the same. Rosetta auto-classifies each request into one of twelve workflow types — coding, testing, research, requirements, modernization, and others — loading entirely different instructions for each. The agent maintains requirements traceability instead of mixing everything together.
5
Analysis before execution. Most agents rush straight to code. Rosetta workflows define preparation, research, planning, and approval phases before a single line is written. Plans and specs are separate artifacts. The process scales by task size.
6
Review and validation by separate agents. Self-review doesn't work — the model rubber-stamps its own decisions. Rosetta instructs the agent to delegate review and validation to separate subagents with fresh context windows. They've never seen the implementation struggles. They catch what the implementer can't.
7
Workflows built from real failure modes. Ask any AI to design a coding workflow from scratch — it produces 2-3 steps and forgets everything else. Rosetta contains workflows created by humans who observed every category of AI failure and encoded the solutions. The agent stops skipping the steps that matter.
8
Self-learning and crash recovery. Agents don't learn from mistakes and don't survive session loss. Rosetta instructs the agent to maintain a memory of errors and lessons, and to write execution state to disk. If a session fails, the next one resumes from the last checkpoint.
9
Security by design. Rosetta never sees your code. Instruction delivery is deterministic — the agent requests by tag, not by sending source code. No semantic search over your codebase. Air-gap capable. Runs inside your perimeter.
10
One investment, every AI tool. Works across Cursor, Claude Code, VS Code, JetBrains, Codex, Windsurf, and more. Write instructions once. Three layers merge at runtime — core, organization, project — so teams customize without forking. Version-controlled with instant rollback.

A typical coding task drops from ~75 min to ~25 min. Repository onboarding drops from weeks to minutes. Production teams report 3x–5x productivity gains.

Try Rosetta

Pick a scenario and see how Rosetta handles it step by step.

Rosetta analyzing your request…
Rosetta

Pick a scenario on the left to start

Get Started

Pick the supported installation path, verify Rosetta is active, initialize the repository, then start using workflows.

1

Install Rosetta

Use plugins where they are supported. Use MCP only for agents without a Rosetta plugin path.

Rosetta is designed to never use or see your data or IP.
 ‼️ You must receive a prior approval from your manager and company to use it. ‼️ 

Install the Rosetta plugin from the Claude Code marketplace. Deep links: Plugins, Installation.

$ claude plugin marketplace add griddynamics/rosetta
$ claude plugin install rosetta@rosetta

Use a Cursor team marketplace when available. If not, extract the standalone package into the repository. Deep links: Plugins, Installation.

# Team marketplace repository
https://github.com/griddynamics/rosetta

# Standalone fallback
Download core-cursor-standalone-*.zip from the latest release and extract it into the repository.

For VS Code, add Rosetta as a Copilot plugin marketplace and install rosetta from agent customizations. For JetBrains Copilot, use the standalone package. Deep links: Plugins, Installation.

# VS Code setting: chat.plugins.marketplaces
https://github.com/griddynamics/rosetta

# JetBrains Copilot
Download core-copilot-standalone-*.zip from the latest release and extract it into the repository.

Extract the standalone Codex plugin package into the repository, then enable hooks. Deep links: Plugins, Installation.

Download core-codex-*.zip from the latest release and extract it into the repository.

$ codex features enable hooks

Add Rosetta MCP to Antigravity, then add the bootstrap rule. Deep links: MCPs Installation, Installation.

{
  "mcpServers": {
    "Rosetta": {
      "serverUrl": "https://mcp.rosetta.griddynamics.net/mcp"
    }
  }
}

Bootstrap rule destination:
.agent/rules/bootstrap.md

Add Rosetta MCP to Windsurf, then add the bootstrap rule. Deep links: MCPs Installation, Installation.

{
  "mcpServers": {
    "Rosetta": {
      "url": "https://mcp.rosetta.griddynamics.net/mcp"
    }
  }
}

Bootstrap rule destination:
.windsurf/rules/bootstrap.md

Add Rosetta MCP to OpenCode, then add the bootstrap rule. Deep links: MCPs Installation, Installation.

{
  "mcp": {
    "Rosetta": {
      "type": "http",
      "url": "https://mcp.rosetta.griddynamics.net/mcp",
      "enabled": true
    }
  }
}

Bootstrap rule destination:
AGENTS.md

Add Rosetta MCP in Junie settings, then add the bootstrap rule. Deep links: MCPs Installation, Installation.

{
  "mcpServers": {
    "Rosetta": {
      "url": "https://mcp.rosetta.griddynamics.net/mcp"
    }
  }
}

Bootstrap rule destination:
.junie/guidelines.md

Use the Rosetta HTTP MCP endpoint in any MCP-compatible IDE or coding agent without a Rosetta plugin path. Add the bootstrap rule to that tool's project instruction file. Deep links: MCPs Installation, Installation.

https://mcp.rosetta.griddynamics.net/mcp

Bootstrap rule:
Download bootstrap.md and place it in your IDE or agent's project instruction file.
2

Verify Rosetta

Restart the IDE if you changed files or settings. Then ask the agent this exact question:

What can you do, Rosetta?

A working plugin setup follows Rosetta prompts, shows available workflows, and runs self-help-flow instead of giving a generic model answer.

A working MCP setup uses Rosetta MCP to retrieve agents, guardrails, and instructions before answering.

3

Initialize The Repository

Run once per repository and commit the generated workspace files.

Initialize this repository using Rosetta

The agent analyzes the tech stack, creates workspace docs such as TECHSTACK.md, CODEMAP.md, DEPENDENCIES.md, ARCHITECTURE.md, and CONTEXT.md, and asks clarifying questions. Deep links: Quick Start, Init Workspace Flow.

4

Configure The Workspace

After initialization, set up the workspace context so agents have the right business and technical grounding: business context, architecture details, reference source, reusable patterns, useful CLIs and MCPs, and modernization-specific setup when relevant. Deep link: Configuration.

5

Start With A Workflow

Ask for the outcome you need. Rosetta routes the task through the matching workflow. Deep link: Usage Guide workflows.

/coding-flow Implement side bar on the home page, ...
/requirements-authoring-flow extract detailed requirements from ...
/modernization-flow Perform modernization phase 1 to reuse library refsrc/...
✓
You're set. Rosetta is active. Use the workflows directly or talk naturally after repository initialization.
See all workflows →
Agent-agnostic by design. Use frontier-class models (Claude Sonnet 4.6, GPT-5.4-medium, Gemini 3.1 Pro) or better for best results.

Without Rosetta vs With Rosetta

Real enterprise cases. Same task — different results.

Without Rosetta
"Implement data changes from an Excel spec across two interconnected databases."
  • Started writing queries without understanding the full picture
  • Missed cross-schema relationships and dependent services
  • Ignored stored procedures, triggers, and legacy integrations
  • Produced partial SQL that would break downstream on deploy
Broken deploy. Hours of manual debugging.
With Rosetta
"Implement data changes from an Excel spec across two interconnected databases."
  • Loaded both database schemas and mapped every relationship
  • Identified all services and APIs depending on affected tables
  • Found missing fields and web services that needed updates
  • Discovered additional connections hidden in legacy COBOL code
  • Assembled full end-to-end implementation with all dependencies resolved
Complete implementation. Nothing missed — even legacy connections.
Without Rosetta
"Modernize this C++ service with Windows components into a new architecture."
  • Scanned a few files and suggested a generic rewrite
  • Missed Windows-specific dependencies and COM components
  • Couldn't determine what to reuse vs replace
  • Produced a vague spec — team still had to reverse-engineer the original
Spec full of gaps. Team starts over manually.
With Rosetta
"Modernize this C++ service with Windows components into a new architecture."
  • Systematically analyzed every class, method, and dependency
  • Mapped Windows components, libraries, and service boundaries
  • Determined what to reuse, what to replace, and what to drop
  • Created a target spec with interfaces, edge cases, and architecture decisions
  • Spec so precise that developers could ask AI follow-ups and get exact answers
Production-ready spec. Team moved straight to implementation.
Without Rosetta
"Add discount code support to checkout — requires changes across API gateway, pricing service, and order service."
  • Agent modified the pricing service without seeing the API gateway contract
  • Broke the order service integration — different payload format expected
  • No awareness of shared validation rules or error handling conventions
  • Each service fixed separately, introducing new inconsistencies
Three services out of sync. Integration tests failing for days.
With Rosetta
"Add discount code support to checkout — requires changes across API gateway, pricing service, and order service."
  • Rosetta loaded architecture, API contracts, and shared schemas across all three services
  • Agent produced a coordinated spec covering gateway routing, pricing logic, and order updates
  • Validation rules and error handling followed existing patterns automatically
  • Changes reviewed as a single coherent plan before any code was written
All three services updated in sync. Integration tests green on first run.