关于 V3 Swarm Coordination
用于 claude-flow v3 实施的协调技能,规划由 15 个 Agent 组成的分层网格,按领域并行执行任务并管理依赖与阶段。
主要功能
- 01协调 15-Agent 分层网格
- 02按领域并行分配任务
- 03管理任务依赖与阶段
- 04协调 GitHub 里程碑
- 05监测负载与并行效率
适用场景
- 01启动 claude-flow v3 实施团队
- 02协调安全与核心系统工作
- 03按阶段安排集成和发布
MCP 相关
技能架构包含 MCP 专员,负责优化 MCP 服务;实施计划也包括 MCP 优化任务。
安装
复制下面的命令,在终端中运行。
$
npx skills add https:/ / github.com/ ruvnet/ ruflo --skill v3-swarm-coordination安装后在 Claude Code 中输入/v3-swarm-coordination确认已加载。
详细介绍
V3 Swarm 协调
此技能的作用
协调 claude-flow v3 实施所需的完整 15-Agent 分层网状 Swarm,在各领域并行执行的同时,确保依赖关系和进度安排得到遵循。
快速开始
# 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 架构
分层网状拓扑
👑 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 名单
| ID | Agent | 领域 | 阶段 | 职责 |
|---|---|---|---|---|
| 1 | Queen 协调器 | 编排 | 全部 | GitHub 问题、依赖关系、进度安排 |
| 2 | 安全架构师 | 安全 | 基础 | 威胁建模、CVE 规划 |
| 3 | 安全实施者 | 安全 | 基础 | 修复 CVE、安全模式 |
| 4 | 安全测试员 | 安全 | 基础 | TDD 安全测试 |
| 5 | 核心架构师 | 核心 | 系统 | DDD 架构、协调 |
| 6 | 核心实施者 | 核心 | 系统 | 核心模块实施 |
| 7 | 内存专家 | 核心 | 系统 | AgentDB 统一 |
| 8 | Swarm 专家 | 核心 | 系统 | 统一协调引擎 |
| 9 | MCP 专家 | 核心 | 系统 | MCP 服务优化 |
| 10 | 集成架构师 | 集成 | 集成 | agentic-flow@alpha 深度集成 |
| 11 | CLI/Hooks 开发者 | 集成 | 集成 | CLI 现代化 |
| 12 | 神经网络/学习开发者 | 集成 | 集成 | SONA 集成 |
| 13 | TDD 测试工程师 | 质量 | 全部 | London School TDD |
| 14 | 性能工程师 | 性能 | 优化 | 基准测试验证 |
| 15 | 发布工程师 | 部署 | 发布 | CI/CD 和 v3.0.0 发布 |
实施阶段
阶段 1:基础(第 1-2 周)
活跃 Agent:#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")
]);
};
阶段 2:核心系统(第 3-6 周)
活跃 Agent:#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")
]);
};
阶段 3:集成(第 7-10 周)
活跃 Agent:#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")
]);
};
阶段 4:发布(第 11-14 周)
活跃 Agent:全部 15 个
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 ${agent.id} final optimization`, agent.name)
)
]);
};
协调模式
依赖管理
class DependencyCoordination {
private dependencies = new Map([
// Security first (no dependencies)
[2, []], [3, [2]], [4, [2, 3]],
// Core depends on security foundation
[5, [2]], [6, [5]], [7, [5]], [8, [5, 7]], [9, [5]],
// Integration depends on core systems
[10, [5, 7, 8]], [11, [5, 10]], [12, [7, 10]],
// Quality and performance cross-cutting
[13, [2, 5]], [14, [5, 7, 8, 10]], [15, [13, 14]]
]);
async coordinateExecution(): Promise<void> {
const completed = new Set<number>();
while (completed.size < 15) {
const ready = this.getReadyAgents(completed);
if (ready.length === 0) {
throw new Error('Deadlock detected in dependency chain');
}
// Execute ready agents in parallel
await Promise.all(ready.map(agentId => this.executeAgent(agentId)));
ready.forEach(id => completed.add(id));
}
}
}
GitHub 集成
class GitHubCoordination {
async initializeV3Milestone(): Promise<void> {
await gh.createMilestone({
title: 'Claude-Flow v3.0.0 Implementation',
description: '15-agent swarm implementation of 10 ADRs',
dueDate: this.calculate14WeekDeadline()
});
}
async createEpicIssues(): Promise<void> {
const epics = [
{ title: 'Security Overhaul (CVE-1,2,3)', agents: [2, 3, 4] },
{ title: 'Memory Unification (AgentDB)', agents: [7] },
{ title: 'agentic-flow Integration', agents: [10] },
{ title: 'Performance Optimization', agents: [14] },
{ title: 'DDD Architecture', agents: [5, 6] }
];
for (const epic of epics) {
await gh.createIssue({
title: epic.title,
labels: ['epic', 'v3', ...epic.agents.map(id => `agent-${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
}
}
通信总线
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'
});
}
}
性能协调
并行效率监控
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()
};
}
}
负载均衡
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);
}
}
}
成功指标
Swarm 协调
- 并行效率:Agent 利用时间 >85%
- 依赖关系解决:不存在死锁或阻塞问题
- 通信延迟:Agent 间消息传递 <100ms
- 进度安排遵循情况:保持 14 周交付计划
- GitHub 集成:自动响应问题 <4 小时
实施目标
- ADR 覆盖率:成功实施全部 10 个 ADR
- 性能:实现 2.49x-7.47x Flash Attention
- 搜索:验证 AgentDB 提升 150x-12,500x
- 代码精简:<5,000 行(原为 15,000+ 行)
- 安全性:达到 90/100 安全评分
相关 V3 技能
v3-security-overhaul- 安全领域协调v3-memory-unification- 内存系统协调v3-integration-deep- 集成领域协调v3-performance-optimization- 性能领域协调
使用示例
初始化完整 V3 Swarm
# Queen Coordinator initializes full swarm
Task("V3 swarm initialization",
"Initialize 15-agent hierarchical mesh for complete v3 implementation",
"v3-queen-coordinator")
按阶段执行
# 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
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