本文へ移動
cccskills
無料GitHub で公開

V3 Swarm Coordination

15-agent hierarchical mesh coordination for v3 implementation. Orchestrates parallel execution across security, core, and integration domains following 10 ADRs with 14-week timeline.

日本語の概要は準備中です。原文の説明を表示しています。

proffesor-for-testing/agentic-qeスター 515(GitHub 515 + cccskills 0)

実行スコア 未検証

インストール方法を見る

含まれるファイル(1)

  • SKILL.md11.9 KB

SKILL.md(原文)

インストールする前に、エージェントに与えられる指示の中身を確認できます。

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

# 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

                    👑 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)

Agent Roster

IDAgentDomainPhaseResponsibility
1Queen CoordinatorOrchestrationAllGitHub issues, dependencies, timeline
2Security ArchitectSecurityFoundationThreat modeling, CVE planning
3Security ImplementerSecurityFoundationCVE fixes, secure patterns
4Security TesterSecurityFoundationTDD security testing
5Core ArchitectCoreSystemsDDD architecture, coordination
6Core ImplementerCoreSystemsCore module implementation
7Memory SpecialistCoreSystemsAgentDB unification
8Swarm SpecialistCoreSystemsUnified coordination engine
9MCP SpecialistCoreSystemsMCP server optimization
10Integration ArchitectIntegrationIntegrationagentic-flow@alpha deep integration
11CLI/Hooks DeveloperIntegrationIntegrationCLI modernization
12Neural/Learning DevIntegrationIntegrationSONA integration
13TDD Test EngineerQualityAllLondon School TDD
14Performance EngineerPerformanceOptimizationBenchmarking validation
15Release EngineerDeploymentReleaseCI/CD and v3.0.0 release

Implementation Phases

Phase 1: Foundation (Week 1-2)

Active Agents: #1, #2-4, #5-6

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

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

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

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

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

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

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

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

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

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

Phase-based Execution

# 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

レビュー

まだレビューはありません。使ってみた感想をお寄せください。

コメント

ログインなしで書き込めます。使ってみた感想、質問、動かなかった環境などを気軽にどうぞ。

まだコメントはありません。最初のコメントを書いてみませんか。

送信するとすぐに公開されます。メールアドレスや電話番号などの個人情報は書かないでください。URL は 2 つまでです。

0 / 1000 文字

このフォームは reCAPTCHA で保護されており、Google のプライバシーポリシーと利用規約が適用されます。

同じリポジトリのスキル

a11y-ally

無料

Use when running comprehensive WCAG accessibility audits with axe-core + pa11y + Lighthouse, generating context-aware remediation, or testing video accessibility. Supports 3-tier browser cascade with graceful degradation.

日本語の概要は準備中です。原文の説明を表示しています。

proffesor-for-testing/agentic-qe5152026年10月26日 更新

WCAG 2.2 compliance testing, screen reader validation, and inclusive design verification. Use when ensuring legal compliance (ADA, Section 508), testing for disabilities, or building accessible applications for 1 billion disabled users globally.

日本語の概要は準備中です。原文の説明を表示しています。

proffesor-for-testing/agentic-qe5152026年10月26日 更新

Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems integration. Use when building distributed AI systems, multi-agent coordination, or advanced vector search applications.

日本語の概要は準備中です。原文の説明を表示しています。

proffesor-for-testing/agentic-qe5152026年10月26日 更新

Create and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms. Includes Decision Transformer, Q-Learning, SARSA, Actor-Critic, and more. Use when building self-learning agents, implementing RL, or optimizing agent behavior through experience.

日本語の概要は準備中です。原文の説明を表示しています。

proffesor-for-testing/agentic-qe5152026年10月26日 更新

Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intelligent assistants.

日本語の概要は準備中です。原文の説明を表示しています。

proffesor-for-testing/agentic-qe5152026年10月26日 更新

Optimize AgentDB performance with quantization (4-32x memory reduction), HNSW indexing (150x faster search), caching, and batch operations. Use when optimizing memory usage, improving search speed, or scaling to millions of vectors.

日本語の概要は準備中です。原文の説明を表示しています。

proffesor-for-testing/agentic-qe5152026年10月26日 更新

proffesor-for-testing のスキルをすべて見る

このスキルの問題を報告する