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Get Started Free →15-agent hierarchical mesh coordination for v3 implementation. Orchestrates parallel execution across security, core, and integration domains following 10 ADRs with 14-week timeline.
.claude/skills/ruvnet-v3-swarm-coordination/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | 97% | 0% |
| case-03 | ✗→✓ | ▲ Improved | 179% | 0% |
| case-09 | ✗→✓ | ▲ Improved | 100% | 0% |
| case-15 | ✗→✓ | ▲ Improved | 132% | 0% |
| case-02 | ✓→✓ | = Same ✓ | 90% | 0% |
Orchestrates the complete 15-agent hierarchical mesh swarm for claude-flow v3 implementation, coordinating parallel execution across domains while maintaining dependencies and timeline adherence.
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")
👑 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)| 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 |
Active Agents: #1, #2-4, #5-6
typescriptconst 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") ]); };
Active Agents: #1, #5-9, #13
typescriptconst 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") ]); };
Active Agents: #1, #10-12, #13-14
typescriptconst 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") ]); };
Active Agents: All 15
typescriptconst 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 ${agent.id} final optimization`, agent.name) ) ]); };
typescriptclass DependencyCoordination { private dependencies = new Map([ // Security first (no dependencies) [2, []], [3, [2]], [4, [2, 3]], // Core depends on security foundation [5, [2]], [6, [5]], [7, [5]], [8, [5, 7]], [9, [5]], // Integration depends on core systems [10, [5, 7, 8]], [11, [5, 10]], [12, [7, 10]], // Quality and performance cross-cutting [13, [2, 5]], [14, [5, 7, 8, 10]], [15, [13, 14]] ]); async coordinateExecution(): Promise<void> { const completed = new Set<number>(); while (completed.size < 15) { const ready = this.getReadyAgents(completed); if (ready.length === 0) { throw new Error('Deadlock detected in dependency chain'); } // Execute ready agents in parallel await Promise.all(ready.map(agentId => this.executeAgent(agentId))); ready.forEach(id => completed.add(id)); } } }
typescriptclass GitHubCoordination { async initializeV3Milestone(): Promise<void> { await gh.createMilestone({ title: 'Claude-Flow v3.0.0 Implementation', description: '15-agent swarm implementation of 10 ADRs', dueDate: this.calculate14WeekDeadline() }); } async createEpicIssues(): Promise<void> { const epics = [ { title: 'Security Overhaul (CVE-1,2,3)', agents: [2, 3, 4] }, { title: 'Memory Unification (AgentDB)', agents: [7] }, { title: 'agentic-flow Integration', agents: [10] }, { title: 'Performance Optimization', agents: [14] }, { title: 'DDD Architecture', agents: [5, 6] } ]; for (const epic of epics) { await gh.createIssue({ title: epic.title, labels: ['epic', 'v3', ...epic.agents.map(id => `agent-${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 } }
typescriptclass 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' }); } }
typescriptclass 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() }; } }
typescriptclass 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); } } }
v3-security-overhaul - Security domain coordinationv3-memory-unification - Memory system coordinationv3-integration-deep - Integration domain coordinationv3-performance-optimization - Performance domain coordinationbash# Queen Coordinator initializes full swarm Task("V3 swarm initialization", "Initialize 15-agent hierarchical mesh for complete v3 implementation", "v3-queen-coordinator")
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
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→pass | 17,243 | 14,152 | -18% | 1 | 1 | 0% | 3,197 | 6,310 | +97% | 0 | 0 | — |
case-02 | pass→pass | 26,059 | 29,019 | +11% | 1 | 1 | 0% | 4,607 | 8,773 | +90% | 0 | 0 | — |
case-03 | fail→pass | 19,020 | 30,118 | +58% | 1 | 1 | 0% | 3,368 | 9,392 | +179% | 0 | 0 | — |
case-04 | fail→fail | 11,147 | 7,014 | -37% | 1 | 1 | 0% | 1,997 | 4,483 | +124% | 0 | 0 | — |
case-09 | fail→pass | 14,125 | 10,277 | -27% | 1 | 1 | 0% | 2,391 | 4,788 | +100% | 0 | 0 | — |
case-05 | pass→pass | 17,588 | 12,763 | -27% | 1 | 1 | 0% | 3,351 | 5,763 | +72% | 0 | 0 | — |
case-06 | pass→pass | 10,986 | 8,866 | -19% | 1 | 1 | 0% | 2,462 | 5,136 | +109% | 0 | 0 | — |
case-07 | pass→pass | 13,176 | 10,389 | -21% | 1 | 1 | 0% | 2,224 | 5,178 | +133% | 0 | 0 | — |
case-08 | pass→pass | 12,762 | 8,349 | -35% | 1 | 1 | 0% | 2,162 | 4,630 | +114% | 0 | 0 | — |
case-10 | pass→pass | 10,475 | 10,606 | +1% | 1 | 1 | 0% | 1,647 | 4,876 | +196% | 0 | 0 | — |
case-11 | pass→pass | 13,466 | 9,616 | -29% | 1 | 1 | 0% | 2,106 | 4,751 | +126% | 0 | 0 | — |
case-12 | pass→pass | 10,908 | 6,884 | -37% | 1 | 1 | 0% | 1,831 | 4,388 | +140% | 0 | 0 | — |
case-13 | pass→pass | 17,648 | 13,537 | -23% | 1 | 1 | 0% | 3,139 | 5,537 | +76% | 0 | 0 | — |
case-14 | pass→pass | 14,908 | 13,098 | -12% | 1 | 1 | 0% | 2,439 | 5,527 | +127% | 0 | 0 | — |
case-15 | fail→pass | 14,528 | 11,681 | -20% | 1 | 1 | 0% | 2,115 | 4,901 | +132% | 0 | 0 | — |
case-16 | pass→pass | 13,099 | 8,453 | -35% | 1 | 1 | 0% | 2,136 | 4,518 | +112% | 0 | 0 | — |
case-17 | pass→pass | 17,169 | 11,424 | -33% | 1 | 1 | 0% | 2,847 | 4,982 | +75% | 0 | 0 | — |
case-18 | pass→pass | 13,072 | 8,846 | -32% | 1 | 1 | 0% | 2,437 | 4,688 | +92% | 0 | 0 | — |
case-19 | pass→pass | 9,412 | 9,838 | +5% | 1 | 1 | 0% | 1,903 | 5,065 | +166% | 0 | 0 | — |
case-20 | pass→pass | 14,558 | 12,210 | -16% | 1 | 1 | 0% | 2,508 | 5,465 | +118% | 0 | 0 | — |
case-21 | pass→pass | 14,971 | 12,812 | -14% | 1 | 1 | 0% | 2,278 | 5,573 | +145% | 0 | 0 | — |
case-22 | pass→pass | 12,388 | 5,659 | -54% | 1 | 1 | 0% | 2,181 | 4,332 | +99% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 22 cases were attempted. The headline lift of +18 percentage points is the difference between those two pass rates over the 22 comparable cases.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.