Agent skill
agent-v3-queen-coordinator
Agent skill for v3-queen-coordinator - invoke with $agent-v3-queen-coordinator
Install this agent skill to your Project
npx add-skill https://github.com/ruvnet/ruflo/tree/main/.agents/skills/agent-v3-queen-coordinator
SKILL.md
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 "๐ 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 "๐ง RuVector: $(cat $tmp$v3-intel.json | jq -r '.initialized // false')"
# GitHub integration check
if command -v gh &> $dev$null; then
echo "๐ GitHub CLI available"
gh auth status &>$dev$null && echo "โ
Authenticated" || echo "โ ๏ธ Auth needed"
fi
# Initialize v3 coordination
echo "๐ฏ Mission: ADR-001 to ADR-010 implementation"
echo "๐ Targets: 2.49x-7.47x performance, 150x search, 50-75% memory reduction"
post_execution: | echo "๐ 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
๐ฏ 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
๐ QUEEN COORDINATOR
(Agent #1)
โ
โโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโ
โ โ โ
๐ก๏ธ SECURITY ๐ง CORE ๐ INTEGRATION
(Agents #2-4) (Agents #5-9) (Agents #10-12)
โ โ โ
โโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโ
โ
โโโโโโโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโ
โ โ โ
๐งช QUALITY โก PERFORMANCE ๐ 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
Recommended Agent Skills
Expand your agent's capabilities with these related and highly-rated skills.
add-model-descriptions
Add descriptions for new models from the HuggingFace router to chat-ui configuration. Use when new models are released on the router and need descriptions added to prod.yaml and dev.yaml. Triggers on requests like "add new model descriptions", "update models from router", "sync models", or when explicitly invoking /add-model-descriptions.
agent-swarm-pr
Agent skill for swarm-pr - invoke with $agent-swarm-pr
agent-neural-network
Agent skill for neural-network - invoke with $agent-neural-network
agent-performance-analyzer
Agent skill for performance-analyzer - invoke with $agent-performance-analyzer
agent-researcher
Agent skill for researcher - invoke with $agent-researcher
V3 Memory Unification
Unify 6+ memory systems into AgentDB with HNSW indexing for 150x-12,500x search improvements. Implements ADR-006 (Unified Memory Service) and ADR-009 (Hybrid Memory Backend).
Didn't find tool you were looking for?