by griddynamics
An instruction layer for AI coding agent
# Add to your Claude Code skills
git clone https://github.com/griddynamics/rosettaLast scanned: 6/2/2026
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"scannedAt": "2026-06-02T08:39:38.982Z",
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}rosetta is an open-source ai agents skill for AI coding assistants such as Claude Code, Codex CLI, and ChatGPT, built by griddynamics. An instruction layer for AI coding agent. It has 341 GitHub stars.
Yes. rosetta passed SkillsLLM's automated security scan — a dependency vulnerability audit plus prompt-injection heuristics — with no high-severity issues. You can read the full report in the Security Report section on this page.
Clone the repository with "git clone https://github.com/griddynamics/rosetta" and add it to your Claude Code skills directory (see the Installation section above).
rosetta is primarily written in TypeScript. It is open-source under griddynamics on GitHub, so you can review or fork the full source.
Yes. SkillsLLM lists many other AI Agents skills you can browse and compare side by side. Open the AI Agents category from the badge at the top of this page, or use the Related Skills and comparison links further down to weigh rosetta against similar tools.
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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.
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.
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.
coding-flow: AI creates features, fixes defects, and performs refactoring, everything end-to-end. AI performs discovery, design, specs and a plan, user review, then AI implements and runs separate review and validation passes (including running application). Most useful for medium to large coding tasks, and for controlled component-by-component migration/modernization work.requirements-authoring-flow: AI works with user and raw artifacts to define entire-application requirements. AI discovers context and existing constraints, captures intent, drafts atomic requirement units, validates them, and finalizes traceability artifacts. This is the most efficient use of coding agents. Requirements then Coding.security-flow: AI runs an authorized, evidence-preserving security review through mandatory specialist subagents. It gates secret-bearing files before source ingestion, bounds active testing to approved pre-production targets, independently reviews evidence, and prepares concise inputs for a later coding flow without starting remediation.testgen-flow, api-aqa-flow, ui-aqa-flow: AI handles QA-related work such as generating test cases and creating API or UI automation tests. AI first collects project context, requirements, and existing QA assets, clarifies gaps, and only after that generates test cases or automation tests.code-analysis-flow: AI creates grounded analysis documents based on the codebase. AI first loads project context, asks clarification questions, then produces either one focused analysis document or parallel module analyses plus a summary.help-flow: AI explains available Rosetta workflows, skills, and agents. Most useful when the user is unsure which Rosetta capability to use.init-workspace-flow: AI sets up a repository for AI use in both brownfield and greenfield projects. AI first analyzes the workspace, builds baseline docs, asks gap-filling questions, and verifies the result. Use it once per repository as its purpose is to build context for subsequent sessions.If you prefer more vibe-coding, check the guardrails and useful skills below.
planning, tech-specs: Turn a request into a clear plan and actionable specs.orchestration: Coordinate an efficient team of subagents for large tasks (also request "team manager" capability for full experience).questioning, hitl: AI to work with human, not over or behind, to be more human-oriented.research, reverse-engineering: Repository grounded research and logical reverse engineering (business logic extraction).coding, debugging, testing: Implementation, debugging with root-cause analysis, and validation.security: Run authorized security reviews with secret-first safety gates, bounded testing, lossless evidence, independent review, and remediation-task preparation.reasoning: Requires AI to decompose and recompose the problem, boundaries, actors, roles, gaps, contradictions, and perform recursive tree-of-thoughts reasoning.solr-*: AI will help to build SOLR search-related artifacts.| Without Rosetta | With Rosetta |
|---|---|
| Each developer writes their own prompts and instructions | One versioned, shared source of truth |
| The agent guesses from a few open files | It reads your architecture and conventions first |
| Starts coding immediately | Prepare → research → plan → act → validate |
| Reviews its own work in the same context | A fresh-context reviewer subagent checks it |
| "Generate and hope" | Validation with real execution evidence |
| Knowledge stuck in senior engineers' heads | Captured once, reused everywhere |
| Without Rosetta | With Rosetta |
|---|---|
| Jumps straight into editing the handler | Reads ARCHITECTURE.md and your existing conventions first |
| Misses the shared middleware pattern; duplicates the Redis client | Reuses the shared rate-limiter and Redis layer |
| No plan, no checkpoint | Proposes a plan and asks for approval |
| Ships without running tests | Runs the integration tests, then a fresh-context reviewer validates |
Rosetta layers your instructions at runtime — core, then organization, then project, each building on the one above — and adapts the result to whatever agent you use:
flowchart LR
A["<b>Core</b><br/>best practices & workflows"] --> B["<b>Organization</b><br/>standards, policies & guardrails"]
B --> C["<b>Project</b><br/>architecture, context & constraints"]
C --> R["<b>Rosetta Runtime</b><br/>context engineering + instruction orchestration"]
R --> AD["<b>Agent Adapters</b>"]
AD --> G["<b>Guided Execution</b><br/>workflows • guardrails • validation"]
G --> T["Claude Code · Cursor · Co