home / skills / proffesor-for-testing / agentic-qe / v3-swarm-coordination

This skill orchestrates a 15-agent hierarchical swarm for Claude-flow v3, coordinating cross-domain tasks while preserving dependencies and timelines.

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---
name: "V3 Swarm Coordination"
description: "15-agent hierarchical mesh coordination for v3 implementation. Orchestrates parallel execution across security, core, and integration domains following 10 ADRs with 14-week timeline."
---

# V3 Swarm Coordination

## What This Skill Does

Orchestrates the complete 15-agent hierarchical mesh swarm for claude-flow v3 implementation, coordinating parallel execution across domains while maintaining dependencies and timeline adherence.

## Quick Start

```bash
# Initialize 15-agent v3 swarm
Task("Swarm initialization", "Initialize hierarchical mesh for v3 implementation", "v3-queen-coordinator")

# Security domain (Phase 1 - Critical priority)
Task("Security architecture", "Design v3 threat model and security boundaries", "v3-security-architect")
Task("CVE remediation", "Fix CVE-1, CVE-2, CVE-3 vulnerabilities", "security-auditor")
Task("Security testing", "Implement TDD security framework", "test-architect")

# Core domain (Phase 2 - Parallel execution)
Task("Memory unification", "Implement AgentDB 150x improvement", "v3-memory-specialist")
Task("Integration architecture", "Deep agentic-flow@alpha integration", "v3-integration-architect")
Task("Performance validation", "Validate 2.49x-7.47x targets", "v3-performance-engineer")
```

## 15-Agent Swarm Architecture

### Hierarchical Mesh Topology
```
                    ๐Ÿ‘‘ QUEEN COORDINATOR
                         (Agent #1)
                             โ”‚
        โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”
        โ”‚                   โ”‚                    โ”‚
   ๐Ÿ›ก๏ธ SECURITY         ๐Ÿง  CORE              ๐Ÿ”— INTEGRATION
   (Agents #2-4)       (Agents #5-9)        (Agents #10-12)
        โ”‚                   โ”‚                    โ”‚
        โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜
                             โ”‚
        โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”
        โ”‚                   โ”‚                    โ”‚
   ๐Ÿงช QUALITY          โšก PERFORMANCE        ๐Ÿš€ DEPLOYMENT
   (Agent #13)         (Agent #14)          (Agent #15)
```

### Agent Roster
| ID | Agent | Domain | Phase | Responsibility |
|----|-------|--------|-------|----------------|
| 1 | Queen Coordinator | Orchestration | All | GitHub issues, dependencies, timeline |
| 2 | Security Architect | Security | Foundation | Threat modeling, CVE planning |
| 3 | Security Implementer | Security | Foundation | CVE fixes, secure patterns |
| 4 | Security Tester | Security | Foundation | TDD security testing |
| 5 | Core Architect | Core | Systems | DDD architecture, coordination |
| 6 | Core Implementer | Core | Systems | Core module implementation |
| 7 | Memory Specialist | Core | Systems | AgentDB unification |
| 8 | Swarm Specialist | Core | Systems | Unified coordination engine |
| 9 | MCP Specialist | Core | Systems | MCP server optimization |
| 10 | Integration Architect | Integration | Integration | agentic-flow@alpha deep integration |
| 11 | CLI/Hooks Developer | Integration | Integration | CLI modernization |
| 12 | Neural/Learning Dev | Integration | Integration | SONA integration |
| 13 | TDD Test Engineer | Quality | All | London School TDD |
| 14 | Performance Engineer | Performance | Optimization | Benchmarking validation |
| 15 | Release Engineer | Deployment | Release | CI/CD and v3.0.0 release |

## Implementation Phases

### Phase 1: Foundation (Week 1-2)
**Active Agents**: #1, #2-4, #5-6
```typescript
const phase1 = async () => {
  // Parallel security and architecture foundation
  await Promise.all([
    // Security domain (critical priority)
    Task("Security architecture", "Complete threat model and security boundaries", "v3-security-architect"),
    Task("CVE-1 fix", "Update vulnerable dependencies", "security-implementer"),
    Task("CVE-2 fix", "Replace weak password hashing", "security-implementer"),
    Task("CVE-3 fix", "Remove hardcoded credentials", "security-implementer"),
    Task("Security testing", "TDD London School security framework", "test-architect"),

    // Core architecture foundation
    Task("DDD architecture", "Design domain boundaries and structure", "core-architect"),
    Task("Type modernization", "Update type system for v3", "core-implementer")
  ]);
};
```

### Phase 2: Core Systems (Week 3-6)
**Active Agents**: #1, #5-9, #13
```typescript
const phase2 = async () => {
  // Parallel core system implementation
  await Promise.all([
    Task("Memory unification", "Implement AgentDB with 150x-12,500x improvement", "v3-memory-specialist"),
    Task("Swarm coordination", "Merge 4 coordination systems into unified engine", "swarm-specialist"),
    Task("MCP optimization", "Optimize MCP server performance", "mcp-specialist"),
    Task("Core implementation", "Implement DDD modular architecture", "core-implementer"),
    Task("TDD core tests", "Comprehensive test coverage for core systems", "test-architect")
  ]);
};
```

### Phase 3: Integration (Week 7-10)
**Active Agents**: #1, #10-12, #13-14
```typescript
const phase3 = async () => {
  // Parallel integration and optimization
  await Promise.all([
    Task("agentic-flow integration", "Eliminate 10,000+ duplicate lines", "v3-integration-architect"),
    Task("CLI modernization", "Enhance CLI with hooks system", "cli-hooks-developer"),
    Task("SONA integration", "Implement <0.05ms learning adaptation", "neural-learning-developer"),
    Task("Performance benchmarking", "Validate 2.49x-7.47x targets", "v3-performance-engineer"),
    Task("Integration testing", "End-to-end system validation", "test-architect")
  ]);
};
```

### Phase 4: Release (Week 11-14)
**Active Agents**: All 15
```typescript
const phase4 = async () => {
  // Full swarm final optimization
  await Promise.all([
    Task("Performance optimization", "Final optimization pass", "v3-performance-engineer"),
    Task("Release preparation", "CI/CD pipeline and v3.0.0 release", "release-engineer"),
    Task("Final testing", "Complete test coverage validation", "test-architect"),

    // All agents: Final polish and optimization
    ...agents.map(agent =>
      Task("Final polish", `Agent ${agent.id} final optimization`, agent.name)
    )
  ]);
};
```

## Coordination Patterns

### Dependency Management
```typescript
class DependencyCoordination {
  private dependencies = new Map([
    // Security first (no dependencies)
    [2, []], [3, [2]], [4, [2, 3]],

    // Core depends on security foundation
    [5, [2]], [6, [5]], [7, [5]], [8, [5, 7]], [9, [5]],

    // Integration depends on core systems
    [10, [5, 7, 8]], [11, [5, 10]], [12, [7, 10]],

    // Quality and performance cross-cutting
    [13, [2, 5]], [14, [5, 7, 8, 10]], [15, [13, 14]]
  ]);

  async coordinateExecution(): Promise<void> {
    const completed = new Set<number>();

    while (completed.size < 15) {
      const ready = this.getReadyAgents(completed);

      if (ready.length === 0) {
        throw new Error('Deadlock detected in dependency chain');
      }

      // Execute ready agents in parallel
      await Promise.all(ready.map(agentId => this.executeAgent(agentId)));

      ready.forEach(id => completed.add(id));
    }
  }
}
```

### GitHub Integration
```typescript
class GitHubCoordination {
  async initializeV3Milestone(): Promise<void> {
    await gh.createMilestone({
      title: 'Claude-Flow v3.0.0 Implementation',
      description: '15-agent swarm implementation of 10 ADRs',
      dueDate: this.calculate14WeekDeadline()
    });
  }

  async createEpicIssues(): Promise<void> {
    const epics = [
      { title: 'Security Overhaul (CVE-1,2,3)', agents: [2, 3, 4] },
      { title: 'Memory Unification (AgentDB)', agents: [7] },
      { title: 'agentic-flow Integration', agents: [10] },
      { title: 'Performance Optimization', agents: [14] },
      { title: 'DDD Architecture', agents: [5, 6] }
    ];

    for (const epic of epics) {
      await gh.createIssue({
        title: epic.title,
        labels: ['epic', 'v3', ...epic.agents.map(id => `agent-${id}`)],
        assignees: epic.agents.map(id => this.getAgentGithubUser(id))
      });
    }
  }

  async trackProgress(): Promise<void> {
    // Hourly progress updates from each agent
    setInterval(async () => {
      for (const agent of this.agents) {
        await this.postAgentProgress(agent);
      }
    }, 3600000); // 1 hour
  }
}
```

### Communication Bus
```typescript
class SwarmCommunication {
  private bus = new QuicSwarmBus({
    maxAgents: 15,
    messageTimeout: 30000,
    retryAttempts: 3
  });

  async broadcastToSecurityDomain(message: SwarmMessage): Promise<void> {
    await this.bus.broadcast(message, {
      targetAgents: [2, 3, 4],
      priority: 'critical'
    });
  }

  async coordinateCoreSystems(message: SwarmMessage): Promise<void> {
    await this.bus.broadcast(message, {
      targetAgents: [5, 6, 7, 8, 9],
      priority: 'high'
    });
  }

  async notifyIntegrationTeam(message: SwarmMessage): Promise<void> {
    await this.bus.broadcast(message, {
      targetAgents: [10, 11, 12],
      priority: 'medium'
    });
  }
}
```

## Performance Coordination

### Parallel Efficiency Monitoring
```typescript
class EfficiencyMonitor {
  async measureParallelEfficiency(): Promise<EfficiencyReport> {
    const agentUtilization = await this.measureAgentUtilization();
    const coordinationOverhead = await this.measureCoordinationCost();

    return {
      totalEfficiency: agentUtilization.average,
      target: 0.85, // >85% utilization
      achieved: agentUtilization.average > 0.85,
      bottlenecks: this.identifyBottlenecks(agentUtilization),
      recommendations: this.generateOptimizations()
    };
  }
}
```

### Load Balancing
```typescript
class SwarmLoadBalancer {
  async balanceWorkload(): Promise<void> {
    const workloads = await this.analyzeAgentWorkloads();

    for (const [agentId, load] of workloads.entries()) {
      if (load > this.getCapacityThreshold(agentId)) {
        await this.redistributeWork(agentId);
      }
    }
  }

  async redistributeWork(overloadedAgent: number): Promise<void> {
    const availableAgents = this.getAvailableAgents();
    const tasks = await this.getAgentTasks(overloadedAgent);

    // Redistribute tasks to available agents
    for (const task of tasks) {
      const bestAgent = this.selectOptimalAgent(task, availableAgents);
      await this.reassignTask(task, bestAgent);
    }
  }
}
```

## Success Metrics

### Swarm Coordination
- [ ] **Parallel Efficiency**: >85% agent utilization time
- [ ] **Dependency Resolution**: Zero deadlocks or blocking issues
- [ ] **Communication Latency**: <100ms inter-agent messaging
- [ ] **Timeline Adherence**: 14-week delivery maintained
- [ ] **GitHub Integration**: <4h automated issue response

### Implementation Targets
- [ ] **ADR Coverage**: All 10 ADRs implemented successfully
- [ ] **Performance**: 2.49x-7.47x Flash Attention achieved
- [ ] **Search**: 150x-12,500x AgentDB improvement validated
- [ ] **Code Reduction**: <5,000 lines (vs 15,000+)
- [ ] **Security**: 90/100 security score achieved

## Related V3 Skills

- `v3-security-overhaul` - Security domain coordination
- `v3-memory-unification` - Memory system coordination
- `v3-integration-deep` - Integration domain coordination
- `v3-performance-optimization` - Performance domain coordination

## Usage Examples

### Initialize Complete V3 Swarm
```bash
# Queen Coordinator initializes full swarm
Task("V3 swarm initialization",
     "Initialize 15-agent hierarchical mesh for complete v3 implementation",
     "v3-queen-coordinator")
```

### Phase-based Execution
```bash
# Phase 1: Security-first foundation
npm run v3:phase1:security

# Phase 2: Core systems parallel
npm run v3:phase2:core-systems

# Phase 3: Integration and optimization
npm run v3:phase3:integration

# Phase 4: Release preparation
npm run v3:phase4:release
```

Overview

This skill orchestrates a 15-agent hierarchical mesh to implement Claude-Flow v3 across security, core, integration, quality, performance, and release domains. It enforces a 14-week timeline and dependency-driven execution while enabling high-parallelism and measurable efficiency targets. The design follows 10 ADRs and provides GitHub milestone and issue automation for traceable delivery.

How this skill works

The Queen Coordinator manages agent dependencies and phases, launching domain-specific agents in parallel where dependencies allow. A Quic-based communication bus handles prioritized broadcasts, while load balancers and efficiency monitors measure utilization, detect bottlenecks, and redistribute work. GitHub coordination creates milestones, epic issues, and hourly progress updates to track timeline adherence and automated responses.

When to use it

  • Implementing a coordinated, multi-domain v3 rollout with strict timeline constraints
  • Orchestrating parallel work across security, core, integration and release teams
  • Automating milestone and epic issue creation for a complex delivery
  • Validating parallel efficiency and identifying coordination bottlenecks
  • Preparing a staged release with integrated CI/CD and final swarm polish

Best practices

  • Run Phase 1 security foundation first to eliminate upstream blockers before parallel core work
  • Keep agent responsibilities small and idempotent to simplify retries and redistribution
  • Use hourly automated progress updates to detect drift early and re-balance load
  • Fail fast on dependency deadlocks with clear diagnostics and escalation paths
  • Measure utilization and coordination overhead continuously; target >85% parallel efficiency

Example use cases

  • Full v3 implementation across 14 weeks with automated GitHub milestone and epics
  • Parallel core system refactor (AgentDB unification and DDD architecture) with TDD coverage
  • Deep integration of agentic-flow@alpha and CLI modernization while validating performance targets
  • Security-first remediation sprint fixing prioritized CVEs and implementing TDD security tests
  • Final release sprint coordinating all 15 agents for CI/CD, benchmarking, and final polish

FAQ

How are agent dependencies enforced?

A dependency map drives the coordinator loop; ready agents run in parallel and the system throws an explicit error if no ready agents exist to prevent silent deadlocks.

What metrics define success?

Key metrics include >85% parallel utilization, zero dependency deadlocks, <100ms messaging latency, 14-week timeline adherence, and automated GitHub response times under 4 hours.