Agent V3 Queen CoordinatorCAUTION

🌊 The original agent harness. Deploy intelligent multi-player swarms, coordinate autonomous workflows, and build conversational AI systems. Features adaptive memory, self-learning intelligence, federation, vector RAG integration, and native Claude Code / Codex / Hermes and many more Integrated

The instruction file, verbatim from the audited commit — this is the text the model reads, and the surface the audit's instruction layer examines. Quoted here so you can judge it without cloning anything.

---
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

CAUTIONgrade B · trust 89/100 Install with care. The audit found things worth knowing before you trust its output.

What the source does

Filesystem

none-observed

Network

none-observed

Shell

none-observed

Dependencies

pinned

Secrets in source

none-found

Findings (4)

crates

plugin/agents

plugin/commands

plugin/skills

Gates applied: no_behavioural_pass.