
V3 Swarm Coordination
FreeEfficiently orchestrate a 15-agent swarm for v3 implementations.
Free Β· Opens the source repo
What V3 Swarm Coordination does
V3 Swarm Coordination is designed for developers and teams looking to implement complex systems using a 15-agent hierarchical mesh architecture. This skill coordinates parallel execution across multiple domainsβsecurity, core systems, and integrationβensuring that dependencies are managed effectively while adhering to a strict timeline. The skill is particularly useful for projects that require a structured approach to multi-agent orchestration, allowing for simultaneous task execution without compromising on quality or security.
The skill's architecture is based on a clear hierarchical mesh topology, where each agent has a designated role and responsibility. The Queen Coordinator oversees the entire operation, ensuring that tasks are distributed appropriately among the agents. This setup allows for a streamlined workflow, where security measures can be implemented in tandem with core system enhancements and integration efforts. The focus on parallel execution helps teams meet tight deadlines, making it ideal for projects with a 14-week timeline.
Utilizing this skill involves initializing the swarm and defining tasks across various phases, from foundational security architecture to final deployment. The provided code snippets illustrate how to set up tasks for each agent, facilitating a clear understanding of how to leverage the skill effectively. By following the structured phases of implementation, users can ensure that all aspects of the project are covered, from security to performance validation.
V3 Swarm Coordination is particularly suited for teams working on complex software projects that require a high level of coordination and security. It is an essential tool for developers and project managers who need to maintain oversight of multiple agents while ensuring that all tasks are executed efficiently and on schedule.
When to use it
Use this skill when implementing complex systems that require parallel execution across security, core, and integration domains.
When not to use it
This skill may not be suitable for simpler projects that do not require a multi-agent approach or when the timeline is flexible.
What you can build with it
Security Architecture Design
Utilize the skill to design a comprehensive threat model and security boundaries in the initial phases of your project.
Core System Implementation
Leverage parallel execution to implement core systems efficiently, ensuring high performance and reliability.
Final Release Preparation
Coordinate all agents for the final optimization and release of your software, ensuring all tasks are completed on schedule.
How to install V3 Swarm Coordination
View source1. Install with the skills CLI
npx skills add ruvnet/ruflo/v3-swarm-coordination --agent claude-code2. Or install it manually
Download the skill folder and drop it into ~/.claude/skills/ for all projects, or .claude/skills/ to scope it to one repo. Restart Claude Code so it picks up the new skill.
Anthropic's agentic coding CLI, and the reference implementation of Agent Skills. Drop a skill folder into ~/.claude/skills and Claude Code loads it automatically whenever a task matches the skill's description. Claude Code docs
Inside SKILL.md
Written by ruvnetV3 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
π QUEEN COORDINATOR
(Agent #1)
β
ββββββββββββββββββββββΌβββββββββββββββββββββ
β β β
π‘οΈ SECURITY π§ CORE π INTEGRATION
(Agents #2-4) (Agents #5-9) (Agents #10-12)
β β β
ββββββββββββββββββββββΌβββββββββββββββββββββ
β
ββββββββββββββββββββββΌβββββββββββββββββββββ
β β β
π§ͺ QUALITY β‘ PERFORMANCE π 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
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 coordinationv3-memory-unification- Memory system coordinationv3-integration-deep- Integration domain coordinationv3-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
Frequently asked questions about V3 Swarm Coordination
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