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agent-v3-queen-coordinator

agent-v3-queen-coordinator

github.com/ruvnetAgent workflows80.0k1.1k installsUpdated today
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About agent-v3-queen-coordinator

agent-v3-queen-coordinator is an orchestration skill for coordinating a 15-agent swarm around Claude-Flow v3 delivery. It organizes work across implementation phases and includes GitHub status checks and coordination-pattern storage.

Key features

  1. 01Coordinates a 15-agent swarm
  2. 02Organizes work across delivery phases
  3. 03Checks GitHub CLI availability and authentication
  4. 04Stores coordination patterns

Use cases

  1. 01Coordinating parallel work on ADR implementation
  2. 02Tracking phased v3 development tasks
  3. 03Checking GitHub access before coordination
  4. 04Recording completed coordination patterns

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/ruvnet/ruflo --skill agent-v3-queen-coordinator

After installing, type/agent-v3-queen-coordinatorin Claude Code to confirm it loaded.

Details

From the project README

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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