V3 DDD Architecture

Analyze claude-flow v3 to extract a DDD-aligned architecture with bounded contexts.

Updated Aug 23, 2026
One-click install
npx skills add https://github.com/JLMA-Agentic-Ai/ruv_downloads --skill v3-ddd-architecture-jlma-agentic-ai
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: V3 DDD Architecture
Source: https://github.com/JLMA-Agentic-Ai/ruv_downloads/tree/main/.claude/skills/v3-ddd-architecture
Command: npx skills add https://github.com/JLMA-Agentic-Ai/ruv_downloads --skill v3-ddd-architecture-jlma-agentic-ai

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Designs and implements a Domain-Driven Design (DDD) architecture to transform claude-flow v3 from a god-object orchestrator into modular bounded contexts with a clean, microkernel-based structure.

Core Features & Use Cases

  • Bounded context decomposition with core domains (task-management, session-management, health-monitoring)
  • Microkernel pattern enabling plugin-based extension and domain collaboration
  • Clear module configuration and domain events for scalable, testable code

Quick Start

Initialize the first bounded contexts and core kernel scaffolding to begin the DDD-driven architecture for claude-flow v3.

Frequently Asked Questions about V3 DDD Architecture

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I refactor a god-object orchestrator into bounded contexts using domain-driven design?

Domain-driven design decomposition splits a monolithic orchestrator into modular bounded contexts like task-management and session-management, isolating core domains to achieve scalable and testable code segmentation.

What's the best way to implement a microkernel plugin system for AI workflow orchestration?

A microkernel pattern enables plugin-based extension by establishing a core kernel for base operations and supporting domain events, allowing plugins to extend functionality without modifying the core orchestrator.

How does event-driven communication work between domain modules in a DDD architecture?

Event-driven communication in a DDD architecture uses domain events to coordinate actions across separated bounded contexts, ensuring modules remain decoupled while reacting to state changes within the system.

When do I need bounded contexts for an AI workflow orchestrator?

Bounded contexts are needed when an AI workflow orchestrator grows too complex, requiring strict domain separation for core areas like health-monitoring and task-management to maintain code clarity and scalability.

Can I use a microkernel architecture to support domain separation in complex orchestrators?

Yes, a microkernel architecture supports domain separation by providing a minimal core kernel that coordinates plugin-based extensions, ensuring each domain module operates independently while sharing essential services.

What are the limitations of refactoring a monolithic orchestrator into a microkernel and plugin system?

Limitations include the overhead of defining clear module configurations, establishing event-driven communication channels, and ensuring the microkernel core remains lightweight while supporting all required domain extensions.