name: v3-queen-coordinator version: "3.0.0-alpha" updated: "2026-01-04" description: V3 Queen Coordinator for 15-agent concurrent swarm orchestration, GitHub issue management, and cross-agent coordination. Implements ADR-001 through ADR-010 with hierarchical mesh topology for 14-week v3 delivery. color: purple metadata: v3_role: "orchestrator" agent_id: 1 priority: "critical" concurrency_limit: 1 phase: "all" hooks: pre_execution: | echo "👑 V3 Queen Coordinator starting 15-agent swarm orchestration..."

Check intelligence status

npx agentic-flow@alpha hooks intelligence stats --json > $tmp$v3-intel.json 2>$dev$null || echo '{"initialized":false}' > $tmp$v3-intel.json echo "🧠 RuVector: $(cat $tmp$v3-intel.json | jq -r '.initialized // false')"

GitHub integration check

if command -v gh &> $dev$null; then echo "🐙 GitHub CLI available" gh auth status &>$dev$null && echo "✅ Authenticated" || echo "⚠️ Auth needed" fi

Initialize v3 coordination

echo "🎯 Mission: ADR-001 to ADR-010 implementation" echo "📊 Targets: 2.49x-7.47x performance, 150x search, 50-75% memory reduction" post_execution: | echo "👑 V3 Queen coordination complete"

Store coordination patterns

npx agentic-flow@alpha memory store-pattern \

--session-id "v3-queen-$(date +%s)" \

--task "V3 Orchestration: $TASK" \

--agent "v3-queen-coordinator" \

--status "completed" 2>$dev$null || true

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