Agent V3 Queen Coordinator
Orchestrate a 15-agent concurrent swarm with GitHub issue coordination and ADR-driven delivery targets when building large multi-agent systems.
Overview
Agent-v3-queen-coordinator is a journey-wide agent skill that runs 15-agent swarm orchestration and GitHub-backed coordination—usable whenever a solo builder needs a queen orchestrator before committing multi-agent deliv
Install
npx skills add https://github.com/ruvnet/ruflo --skill agent-v3-queen-coordinatorWhat is this skill?
- 15-agent concurrent swarm orchestration with queen role and concurrency limit of 1
- GitHub CLI integration for issue management and authenticated coordination checks
- Pre-execution hooks for RuVector intelligence stats and post-execution memory pattern storage via agentic-flow
- Targets ADR-001 through ADR-010 with stated performance goals (2.49x–7.47x, 150x search, 50–75% memory reduction)
- Hierarchical mesh topology framed for 14-week v3 delivery
- concurrency_limit: 1 for queen role
Adoption & trust: 638 installs on skills.sh; 58.5k GitHub stars; 0/3 security scanners passed (skills.sh audits).
What problem does it solve?
You have many specialized agents and ADRs to implement but no centralized queen process to sequence hooks, auth checks, and cross-agent GitHub work.
Who is it for?
Builders shipping ruflo or agentic-flow v3 stacks who must coordinate ~15 agents with GitHub issues and documented ADR milestones.
Skip if: Single-agent coding sessions, projects without agentic-flow or gh CLI, or teams that only need lightweight task routing without swarm topology.
When should I use this skill?
Invoke with $agent-v3-queen-coordinator when starting v3 queen coordination, 15-agent swarm orchestration, or ADR-aligned multi-agent delivery.
What do I get? / Deliverables
A queen-coordinated swarm session completes with intelligence and GitHub readiness logged, coordination patterns stored in agentic-flow memory, and ADR-aligned targets explicit for the mesh.
- Completed pre/post coordination hook run
- Stored coordination memory pattern session
- Logged intelligence and GitHub readiness status
Recommended Skills
Journey fit
Useful at every journey phase - explore requirements and options before committing to a direction.
Where it fits
Start a v3 swarm with intelligence stats and GitHub auth verified before agents pick up ADR tasks.
Coordinate cross-agent work that depends on gh issue state and shared memory patterns.
Align final integration agents against open GitHub issues before release week.
Post-run store coordination patterns to improve the next 14-week delivery loop.
Route escalations through queen hooks when user-facing agents need Seraphina-level platform guidance.
How it compares
Use as a procedural orchestrator skill, not a generic MCP tool or a one-shot code generator.
Common Questions / FAQ
Who is agent-v3-queen-coordinator for?
Indie builders and tech leads running large agentic-flow swarms who need one queen agent to own pre/post hooks, GitHub coordination, and ADR-scoped delivery.
When should I use agent-v3-queen-coordinator?
During build/agent-tooling when standing up mesh orchestration; during ship/review when syncing GitHub issues across agents; during operate/iterate when storing coordination patterns and checking intelligence stats before the next swarm cycle.
Is agent-v3-queen-coordinator safe to install?
It runs shell hooks, may call npx agentic-flow and gh with network access—review the Security Audits panel on this Prism page and scope credentials before enabling in production repos.
SKILL.md
READMESKILL.md - 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