Set up and use 1Password CLI (op). Use when installing the CLI, enabling desktop app integration, signing in, and reading/injecting secrets for commands.
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。
15-agent hierarchical mesh coordination for v3 implementation. Orchestrates parallel execution across security, core, and integration domains following 10 ADRs with 14-week timeline.
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。
Orchestrates the complete 15-agent hierarchical mesh swarm for claude-flow v3 implementation, coordinating parallel execution across domains while maintaining dependencies and timeline adherence.
# 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
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")
]);
};
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")
]);
};
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")
]);
};
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 ${agent.id} final optimization`, agent.name)
)
]);
};
class DependencyCoordination {
private dependencies = new Map([
// Security first (no dependencies)
[2, []], [3, [2]], [4, [2, 3]],
// Core depends on security foundation
[5, [2]], [6, [5]], [7, [5]], [8, [5, 7]], [9, [5]],
// Integration depends on core systems
[10, [5, 7, 8]], [11, [5, 10]], [12, [7, 10]],
// Quality and performance cross-cutting
[13, [2, 5]], [14, [5, 7, 8, 10]], [15, [13, 14]]
]);
async coordinateExecution(): Promise<void> {
const completed = new Set<number>();
while (completed.size < 15) {
const ready = this.getReadyAgents(completed);
if (ready.length === 0) {
throw new Error('Deadlock detected in dependency chain');
}
// Execute ready agents in parallel
await Promise.all(ready.map(agentId => this.executeAgent(agentId)));
ready.forEach(id => completed.add(id));
}
}
}
class GitHubCoordination {
async initializeV3Milestone(): Promise<void> {
await gh.createMilestone({
title: 'Claude-Flow v3.0.0 Implementation',
description: '15-agent swarm implementation of 10 ADRs',
dueDate: this.calculate14WeekDeadline()
});
}
async createEpicIssues(): Promise<void> {
const epics = [
{ title: 'Security Overhaul (CVE-1,2,3)', agents: [2, 3, 4] },
{ title: 'Memory Unification (AgentDB)', agents: [7] },
{ title: 'agentic-flow Integration', agents: [10] },
{ title: 'Performance Optimization', agents: [14] },
{ title: 'DDD Architecture', agents: [5, 6] }
];
for (const epic of epics) {
await gh.createIssue({
title: epic.title,
labels: ['epic', 'v3', ...epic.agents.map(id => `agent-${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
}
}
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'
});
}
}
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()
};
}
}
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);
}
}
}
v3-security-overhaul - Security domain coordinationv3-memory-unification - Memory system coordinationv3-integration-deep - Integration domain coordinationv3-performance-optimization - Performance domain coordination# Queen Coordinator initializes full swarm
Task("V3 swarm initialization",
"Initialize 15-agent hierarchical mesh for complete v3 implementation",
"v3-queen-coordinator")
# 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
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。
概要と使いどころ
Set up and use 1Password CLI (op). Use when installing the CLI, enabling desktop app integration, signing in, and reading/injecting secrets for commands.
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。
Use this skill when the user requests to review, analyze, critique, or summarize academic papers, research articles, preprints, or scientific publications. Supports comprehensive structured reviews covering methodology assessment, contribution evaluation, literature positioning, and constructive feedback generation. Trigger on queries involving paper URLs, uploaded PDFs, arXiv links, or requests like "review this paper", "analyze this research", "summarize this study", or "write a peer review".
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。
Add descriptions for new models from the HuggingFace router to chat-ui configuration. Use when new models are released on the router and need descriptions added to prod.yaml and dev.yaml. Triggers on requests like "add new model descriptions", "update models from router", "sync models", or when explicitly invoking /add-model-descriptions.
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。
Automates browser interactions for web testing, form filling, screenshots, and data extraction. Use when the user needs to navigate websites, interact with web pages, fill forms, take screenshots, test web applications, or extract information from web pages.
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。
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。
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。