对照HuggingFace router的模型列表与chat-ui配置,研究尚未收录的模型,编写简短英文说明,并同步添加到生产和开发配置。
- 添加路由中新上线的模型
- 用简短说明概括模型特点
- 同步更新生产和开发环境的模型信息
将claude-flow v3实现工作分配给15个智能体,协调安全、基础系统、集成、质量检查及发布,并管理任务依赖与14周的实施进度。
15-agent hierarchical mesh coordination for v3 implementation. Orchestrates parallel execution across security, core, and integration domains following 10 ADRs with 14-week timeline.
V3 Swarm Coordination将claude-flow v3的实现工作分配给15个智能体,管理任务顺序和进度。由总协调者统筹安全、基础系统、集成、质量、性能及发布负责人。整个计划依据10份架构决策记录(ADR),分为4个阶段,在14周内从基础建设推进至发布准备。它还涵盖依赖满足后的任务并行执行、负责人之间的沟通,以及工作负载的重新分配。
适用于claude-flow v3这类需要同时推进多个领域变更的开发工作。需要优先处理安全问题、等待基础系统完成后再开展集成,或希望通过GitHub的Issue和里程碑跟踪进度时,可以使用此技能整理安排。
此配置面向claude-flow v3,要求能够通过Task调用智能体,并具备相应的专业智能体及分阶段命令。内容也包含GitHub集成示例。14周期限、性能提升比例和可用率等均为目标值,并非已实现的结果。
本介绍由 AI(OpenAI Codex)根据公开的 SKILL.md 撰写。准确的规格请查看下方原文。
AI 仅根据 SKILL.md 的内容,从 4 个方面进行了评估。星标数和热度不计入评估。
总分按每项 A=25 分、B=15 分、C=5 分相加。 由 AI(OpenAI Codex)于 2026年10月21日 评估。仅供参考,安装前请同时查看原文。
安装前可以查看交给 AI 代理的指令内容。
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
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对照HuggingFace router的模型列表与chat-ui配置,研究尚未收录的模型,编写简短英文说明,并同步添加到生产和开发配置。
为技术选型和设计决策创建ADR文档,沿用现有文件的连续编号,生成模板并登记到AgentDB,同时为找到的相关历史决策添加链接和依赖关系。
读取记录架构决策及其原因的ADR,创建并更新搜索索引和依赖关系图。支持两种文档格式,并汇总引用与状态的不一致情况。
删除ADR设计决策记录或关系信息后,清空并重建索引,移除残留的旧条目,再核对当前文件数与索引记录数是否一致;也可预览重建内容而不作更改。
将代码差异与记录已采纳设计决策的ADR进行核对,整理违反决策、引用过时记录及缺少相关记录的变更,并提出代码修正或决策调整建议。
检查已保存的架构决策记录(ADR),报告失效引用、替代关系中的循环和状态不一致问题,适合导入后的校验与发布前的数据检查。