Agent-Skills.md

Agent Skills: V3 Queen Coordinator

Agent skill for v3-queen-coordinator - invoke with $agent-v3-queen-coordinator

UncategorizedID: ruvnet/claude-flow/agent-v3-queen-coordinator

Repository

ruvnet/claude-flow
ruvnetLicense: MIT
28,0463,058

Install this agent skill to your local

pnpm dlx add-skill https://github.com/ruvnet/ruflo/tree/HEAD/.agents/skills/agent-v3-queen-coordinator

Skill Files

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.agents/skills/agent-v3-queen-coordinator/SKILL.md

Skill Metadata

Name
agent-v3-queen-coordinator
Description
Agent skill for v3-queen-coordinator - invoke with $agent-v3-queen-coordinator

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 delivery