Overview
- Rosetta provides shared prompts and rules that stay consistent across IDEs, coding agents, and models. It uses a classification-first and meta-prompting approach so teams can run project-specific workflows with predictable quality.
+ Rosetta is a control plane for AI coding agents that automates context setup, enforces consistent workflows, and manages engineering knowledge at the organization level — without sharing your source code. It solves the core problems teams face with AI-assisted development:
+
+ - Fragmented adoption and inconsistent execution across teams and tools
+ - Missing business and technical context in day-to-day AI-assisted development
+ - Weak governance, low visibility, and avoidable risk in AI-enabled SDLC
+ - Reinvented workflows and slow onboarding between projects
+
-## What Rosetta Solves
-
-Modern AI coding agents require externalized, persistent context to maintain stable behavior across sessions and tasks. Creating and maintaining this context is typically manual, error-prone, and slow. Rosetta automates the initialization and ongoing maintenance of AI coding agent context for new and existing codebases — providing agent rules, skills, workflows, sub-agents, and instructions as explicit, versioned artifacts, managed centrally via MCP so teams across the organization can share and evolve agent context consistently.
-
-- Fragmented adoption and inconsistent execution across teams and tools.
-- Missing business and technical context in day-to-day AI-assisted development.
-- Weak governance, low visibility, and avoidable risk in AI-enabled SDLC.
-- Reinvented workflows and slow onboarding between projects.
-
## Benefits By Role
@@ -53,19 +50,6 @@ Modern AI coding agents require externalized, persistent context to maintain sta
Release Driven
Evolve safely with release-based governance and rollback-friendly instruction management.
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Unified Knowledge Hub
Business context, architecture, requirements, and rules are organized in one retrievable system.
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RAGFlow Integration
Publishes instruction artifacts for semantic retrieval via MCP tools in coding sessions.
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Smart Metadata and Incremental Updates
Uses tags and hash-based change detection to publish only modified files.
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Built-in Guardrails
Includes approval gates, risk controls, and validation checkpoints for safer execution.
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Reference SDLC
Provides a complete lifecycle by default with opt-out flexibility and room for controlled process experiments.
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Adoption Visibility
Supports usage tracking by capability and helps identify promoters, blockers, and high-value rollout patterns.
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Single-Command Onboarding
Supports fast initialization, upgrades, and project-level customization.
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Community-Friendly
Open-source workflow with contribution paths for improvements to rules and guidance.
-