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agent-v3-queen-coordinator

Agent skill for v3-queen-coordinator - invoke with $agent-v3-queen-coordinator

Quellinformationen

Repository
ruvnet/ruflo
Letzte Quellaktivität
7. Februar 2026 um 17:36
Erkannte Sprache von SKILL.md
Englisch
Sterne
75.283
Forks
8.944

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SKILL.md
Quellanweisungen · Schreibgeschützte Vorschau
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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