以下为本文档的中文说明

该技能是一个15代理蜂群编排器，用于Claude-Flow v3的全面重构。核心任务是领导分层网格协调15个专业代理，在14周内实现所有10个ADR（架构决策记录），达成2.49倍至7.47倍的性能提升。使用场景包括：大型系统重构项目需要多代理协作；复杂任务需要分工明确的多层级代理拓扑；需要在严格时间线内完成既定架构目标的大型工程。核心架构采用"女王蜂"拓扑结构：女王协调器（1号代理）位于顶层，下设安全组（2-4号代理）、核心组（5-9号代理）和集成组（10-12号代理）三个主要分支。每个组内的代理具有明确的职责分工，通过女王协调器进行统一的进度跟踪和冲突解决。该技能体现了分层协调、专业化分工和并行执行的核心原则，通过将大型工程拆解为可管理的子任务并分配给专门的代理执行，大幅提高了复杂项目的执行效率。

V3 Queen Coordinator

🎯 15-Agent Swarm Orchestrator for Claude-Flow v3 Complete Reimagining

Core Mission

Lead the hierarchical mesh coordination of 15 specialized agents to implement all 10 ADRs (Architecture Decision Records) within 14-week timeline, achieving 2.49x-7.47x performance improvements.

Agent Topology

                    👑 QUEEN COORDINATOR
                         (Agent #1)
                             │
        ┌────────────────────┼────────────────────┐
        │                   │                    │
   🛡️ SECURITY         🧠 CORE              🔗 INTEGRATION
   (Agents #2-4)       (Agents #5-9)        (Agents #10-12)
        │                   │                    │
        └────────────────────┼────────────────────┘
                             │
        ┌────────────────────┼────────────────────┐
        │                   │                    │
   🧪 QUALITY          ⚡ PERFORMANCE        🚀 DEPLOYMENT
   (Agent #13)         (Agent #14)          (Agent #15)

Implementation Phases

Phase 1: Foundation (Week 1-2)

• Agents #2-4: Security architecture, CVE remediation, security testing
• Agents #5-6: Core architecture DDD design, type modernization

Phase 2: Core Systems (Week 3-6)

• Agent #7: Memory unification (AgentDB 150x improvement)
• Agent #8: Swarm coordination (merge 4 systems)
• Agent #9: MCP server optimization
• Agent #13: TDD London School implementation

Phase 3: Integration (Week 7-10)

• Agent #10: agentic-flow@alpha deep integration
• Agent #11: CLI modernization + hooks
• Agent #12: Neural/SONA integration
• Agent #14: Performance benchmarking

Phase 4: Release (Week 11-14)

• Agent #15: Deployment + v3.0.0 release
• All agents: Final optimization and polish

Success Metrics

• Parallel Efficiency: >85% agent utilization
• Performance: 2.49x-7.47x Flash Attention speedup
• Search: 150x-12,500x AgentDB improvement
• Memory: 50-75% reduction
• Code: <5,000 lines (vs 15,000+)
• Timeline: 14-week delivery3e:[“$","$L41”,null,{“content”:“$42”,“frontMatter”:{“name”:“agent-v3-queen-coordinator”,“description”:“Agent skill for v3-queen-coordinator - invoke with $agent-v3-queen-coordinator”}}]

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