How to install agent-v3-queen-coordinator
npx skills add https://github.com/ruvnet/ruflo --skill agent-v3-queen-coordinatorFull instructions (SKILL.md)
Source of truth, from ruvnet/ruflo.
name: agent-v3-queen-coordinator description: Agent skill for v3-queen-coordinator - invoke with $agent-v3-queen-coordinator
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
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Agent skill for workflow - invoke with $agent-workflow

agent-workflow-automation
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