Flow Nexus Swarm

作者 ruvnet60de638630ab无许可证74K 个星标收录于 2026年10月8日更新于 2026年10月8日仓库今天更新

Cloud-based AI swarm deployment and event-driven workflow automation with Flow Nexus platform

仅含说明AI & Agents DevOps & Cloud

AI 生成的概览

在 Flow Nexus 平台上部署和编排云端 AI 智能体集群与事件驱动工作流。

功能: 该技能说明如何使用 Flow Nexus MCP 工具初始化具有层级、网状、环形和星形等拓扑的 AI 智能体集群，并生成专用智能体。它涵盖创建和执行事件驱动工作流，包括依赖管理、并行步骤、触发器和重试策略，以及监控、扩缩容和销毁集群。它还提供用于全栈开发、研究、CI/CD 和数据处理流水线的可复用模式与模板。
适用场景: 适用于在云平台上协调多个 AI 智能体的场景，例如并行研究、多仓库开发或自动化部署流水线。也适合需要事件驱动工作流自动化、消息队列处理或可扩展智能体编排的项目。
运行要求: 需要 Flow Nexus MCP 服务器（通过 npm 安装并注册账户），以及对 Flow Nexus 云平台的网络访问。该技能不包含脚本，仅提供说明和 MCP 工具调用示例。

Flow Nexus Swarm & Workflow Orchestration

Deploy and manage cloud-based AI agent swarms with event-driven workflow automation, message queue processing, and intelligent agent coordination.

Overview

Flow Nexus provides cloud-based orchestration for AI agent swarms with:

Multi-topology Support: Hierarchical, mesh, ring, and star architectures
Event-driven Workflows: Message queue processing with async execution
Template Library: Pre-built swarm configurations for common use cases
Intelligent Agent Assignment: Vector similarity matching for optimal agent selection
Real-time Monitoring: Comprehensive metrics and audit trails
Scalable Infrastructure: Cloud-based execution with auto-scaling

Swarm Management

Initialize Swarm

Create a new swarm with specified topology and configuration:

javascript

mcp__flow-nexus__swarm_init({  topology: "hierarchical", // Options: mesh, ring, star, hierarchical  maxAgents: 8,  strategy: "balanced" // Options: balanced, specialized, adaptive})

mcp__flow-nexus__swarm_init({  topology: "hierarchical", // Options: mesh, ring, star, hierarchical  maxAgents: 8,  strategy: "balanced" // Options: balanced, specialized, adaptive})

Topology Guide:

Hierarchical: Tree structure with coordinator nodes (best for complex projects)
Mesh: Peer-to-peer collaboration (best for research and analysis)
Ring: Circular coordination (best for sequential workflows)
Star: Centralized hub (best for simple delegation)

Strategy Guide:

Balanced: Equal distribution of workload across agents
Specialized: Agents focus on specific expertise areas
Adaptive: Dynamic adjustment based on task complexity

Spawn Agents

Add specialized agents to the swarm:

javascript

mcp__flow-nexus__agent_spawn({  type: "researcher", // Options: researcher, coder, analyst, optimizer, coordinator  name: "Lead Researcher",  capabilities: ["web_search", "analysis", "summarization"]})

mcp__flow-nexus__agent_spawn({  type: "researcher", // Options: researcher, coder, analyst, optimizer, coordinator  name: "Lead Researcher",  capabilities: ["web_search", "analysis", "summarization"]})

Agent Types:

Researcher: Information gathering, web search, analysis
Coder: Code generation, refactoring, implementation
Analyst: Data analysis, pattern recognition, insights
Optimizer: Performance tuning, resource optimization
Coordinator: Task delegation, progress tracking, integration

Orchestrate Tasks

Distribute tasks across the swarm:

javascript

mcp__flow-nexus__task_orchestrate({  task: "Build a REST API with authentication and database integration",  strategy: "parallel", // Options: parallel, sequential, adaptive  maxAgents: 5,  priority: "high" // Options: low, medium, high, critical})

mcp__flow-nexus__task_orchestrate({  task: "Build a REST API with authentication and database integration",  strategy: "parallel", // Options: parallel, sequential, adaptive  maxAgents: 5,  priority: "high" // Options: low, medium, high, critical})

Execution Strategies:

Parallel: Maximum concurrency for independent subtasks
Sequential: Step-by-step execution with dependencies
Adaptive: AI-powered strategy selection based on task analysis

Monitor & Scale Swarms

javascript

// Get detailed swarm statusmcp__flow-nexus__swarm_status({  swarm_id: "optional-id" // Uses active swarm if not provided})
// List all active swarmsmcp__flow-nexus__swarm_list({  status: "active" // Options: active, destroyed, all})
// Scale swarm up or downmcp__flow-nexus__swarm_scale({  target_agents: 10,  swarm_id: "optional-id"})
// Gracefully destroy swarmmcp__flow-nexus__swarm_destroy({  swarm_id: "optional-id"})

// Get detailed swarm statusmcp__flow-nexus__swarm_status({  swarm_id: "optional-id" // Uses active swarm if not provided})
// List all active swarmsmcp__flow-nexus__swarm_list({  status: "active" // Options: active, destroyed, all})
// Scale swarm up or downmcp__flow-nexus__swarm_scale({  target_agents: 10,  swarm_id: "optional-id"})
// Gracefully destroy swarmmcp__flow-nexus__swarm_destroy({  swarm_id: "optional-id"})

Workflow Automation

Create Workflow

Define event-driven workflows with message queue processing:

javascript

mcp__flow-nexus__workflow_create({  name: "CI/CD Pipeline",  description: "Automated testing, building, and deployment",  steps: [    {      id: "test",      action: "run_tests",      agent: "tester",      parallel: true    },    {      id: "build",      action: "build_app",      agent: "builder",      depends_on: ["test"]    },    {      id: "deploy",      action: "deploy_prod",      agent: "deployer",      depends_on: ["build"]    }  ],  triggers: ["push_to_main", "manual_trigger"],  metadata: {    priority: 10,    retry_policy: "exponential_backoff"  }})

mcp__flow-nexus__workflow_create({  name: "CI/CD Pipeline",  description: "Automated testing, building, and deployment",  steps: [    {      id: "test",      action: "run_tests",      agent: "tester",      parallel: true    },    {      id: "build",      action: "build_app",      agent: "builder",      depends_on: ["test"]    },    {      id: "deploy",      action: "deploy_prod",      agent: "deployer",      depends_on: ["build"]    }  ],  triggers: ["push_to_main", "manual_trigger"],  metadata: {    priority: 10,    retry_policy: "exponential_backoff"  }})

Workflow Features:

Dependency Management: Define step dependencies with depends_on
Parallel Execution: Set parallel: true for concurrent steps
Event Triggers: GitHub events, schedules, manual triggers
Retry Policies: Automatic retry on transient failures
Priority Queuing: High-priority workflows execute first

Execute Workflow

Run workflows synchronously or asynchronously:

javascript

mcp__flow-nexus__workflow_execute({  workflow_id: "workflow_id",  input_data: {    branch: "main",    commit: "abc123",    environment: "production"  },  async: true // Queue-based execution for long-running workflows})

mcp__flow-nexus__workflow_execute({  workflow_id: "workflow_id",  input_data: {    branch: "main",    commit: "abc123",    environment: "production"  },  async: true // Queue-based execution for long-running workflows})

Execution Modes:

Sync (async: false): Immediate execution, wait for completion
Async (async: true): Message queue processing, non-blocking

Monitor Workflows

javascript

// Get workflow status and metricsmcp__flow-nexus__workflow_status({  workflow_id: "id",  execution_id: "specific-run-id", // Optional  include_metrics: true})
// List workflows with filtersmcp__flow-nexus__workflow_list({  status: "running", // Options: running, completed, failed, pending  limit: 10,  offset: 0})
// Get complete audit trailmcp__flow-nexus__workflow_audit_trail({  workflow_id: "id",  limit: 50,  start_time: "2025-01-01T00:00:00Z"})

// Get workflow status and metricsmcp__flow-nexus__workflow_status({  workflow_id: "id",  execution_id: "specific-run-id", // Optional  include_metrics: true})
// List workflows with filtersmcp__flow-nexus__workflow_list({  status: "running", // Options: running, completed, failed, pending  limit: 10,  offset: 0})
// Get complete audit trailmcp__flow-nexus__workflow_audit_trail({  workflow_id: "id",  limit: 50,  start_time: "2025-01-01T00:00:00Z"})

Agent Assignment

Intelligently assign agents to workflow tasks:

javascript

mcp__flow-nexus__workflow_agent_assign({  task_id: "task_id",  agent_type: "coder", // Preferred agent type  use_vector_similarity: true // AI-powered capability matching})

mcp__flow-nexus__workflow_agent_assign({  task_id: "task_id",  agent_type: "coder", // Preferred agent type  use_vector_similarity: true // AI-powered capability matching})

Vector Similarity Matching:

Analyzes task requirements and agent capabilities
Finds optimal agent based on past performance
Considers workload and availability

Queue Management

Monitor and manage message queues:

javascript

mcp__flow-nexus__workflow_queue_status({  queue_name: "optional-specific-queue",  include_messages: true // Show pending messages})

mcp__flow-nexus__workflow_queue_status({  queue_name: "optional-specific-queue",  include_messages: true // Show pending messages})

Agent Orchestration

Full-Stack Development Pattern

javascript

// 1. Initialize swarm with hierarchical topologymcp__flow-nexus__swarm_init({  topology: "hierarchical",  maxAgents: 8,  strategy: "specialized"})
// 2. Spawn specialized agentsmcp__flow-nexus__agent_spawn({ type: "coordinator", name: "Project Manager" })mcp__flow-nexus__agent_spawn({ type: "coder", name: "Backend Developer" })mcp__flow-nexus__agent_spawn({ type: "coder", name: "Frontend Developer" })mcp__flow-nexus__agent_spawn({ type: "coder", name: "Database Architect" })mcp__flow-nexus__agent_spawn({ type: "analyst", name: "QA Engineer" })
// 3. Create development workflowmcp__flow-nexus__workflow_create({  name: "Full-Stack Development",  steps: [    { id: "requirements", action: "analyze_requirements", agent: "coordinator" },    { id: "db_design", action: "design_schema", agent: "Database Architect" },    { id: "backend", action: "build_api", agent: "Backend Developer", depends_on: ["db_design"] },    { id: "frontend", action: "build_ui", agent: "Frontend Developer", depends_on: ["requirements"] },    { id: "integration", action: "integrate", agent: "Backend Developer", depends_on: ["backend", "frontend"] },    { id: "testing", action: "qa_testing", agent: "QA Engineer", depends_on: ["integration"] }  ]})
// 4. Execute workflowmcp__flow-nexus__workflow_execute({  workflow_id: "workflow_id",  input_data: {    project: "E-commerce Platform",    tech_stack: ["Node.js", "React", "PostgreSQL"]  }})

// 1. Initialize swarm with hierarchical topologymcp__flow-nexus__swarm_init({  topology: "hierarchical",  maxAgents: 8,  strategy: "specialized"})
// 2. Spawn specialized agentsmcp__flow-nexus__agent_spawn({ type: "coordinator", name: "Project Manager" })mcp__flow-nexus__agent_spawn({ type: "coder", name: "Backend Developer" })mcp__flow-nexus__agent_spawn({ type: "coder", name: "Frontend Developer" })mcp__flow-nexus__agent_spawn({ type: "coder", name: "Database Architect" })mcp__flow-nexus__agent_spawn({ type: "analyst", name: "QA Engineer" })
// 3. Create development workflowmcp__flow-nexus__workflow_create({  name: "Full-Stack Development",  steps: [    { id: "requirements", action: "analyze_requirements", agent: "coordinator" },    { id: "db_design", action: "design_schema", agent: "Database Architect" },    { id: "backend", action: "build_api", agent: "Backend Developer", depends_on: ["db_design"] },    { id: "frontend", action: "build_ui", agent: "Frontend Developer", depends_on: ["requirements"] },    { id: "integration", action: "integrate", agent: "Backend Developer", depends_on: ["backend", "frontend"] },    { id: "testing", action: "qa_testing", agent: "QA Engineer", depends_on: ["integration"] }  ]})
// 4. Execute workflowmcp__flow-nexus__workflow_execute({  workflow_id: "workflow_id",  input_data: {    project: "E-commerce Platform",    tech_stack: ["Node.js", "React", "PostgreSQL"]  }})

Research & Analysis Pattern

javascript

// 1. Initialize mesh topology for collaborative researchmcp__flow-nexus__swarm_init({  topology: "mesh",  maxAgents: 5,  strategy: "balanced"})
// 2. Spawn research agentsmcp__flow-nexus__agent_spawn({ type: "researcher", name: "Primary Researcher" })mcp__flow-nexus__agent_spawn({ type: "researcher", name: "Secondary Researcher" })mcp__flow-nexus__agent_spawn({ type: "analyst", name: "Data Analyst" })mcp__flow-nexus__agent_spawn({ type: "analyst", name: "Insights Analyst" })
// 3. Orchestrate research taskmcp__flow-nexus__task_orchestrate({  task: "Research machine learning trends for 2025 and analyze market opportunities",  strategy: "parallel",  maxAgents: 4,  priority: "high"})

// 1. Initialize mesh topology for collaborative researchmcp__flow-nexus__swarm_init({  topology: "mesh",  maxAgents: 5,  strategy: "balanced"})
// 2. Spawn research agentsmcp__flow-nexus__agent_spawn({ type: "researcher", name: "Primary Researcher" })mcp__flow-nexus__agent_spawn({ type: "researcher", name: "Secondary Researcher" })mcp__flow-nexus__agent_spawn({ type: "analyst", name: "Data Analyst" })mcp__flow-nexus__agent_spawn({ type: "analyst", name: "Insights Analyst" })
// 3. Orchestrate research taskmcp__flow-nexus__task_orchestrate({  task: "Research machine learning trends for 2025 and analyze market opportunities",  strategy: "parallel",  maxAgents: 4,  priority: "high"})

CI/CD Pipeline Pattern

javascript

mcp__flow-nexus__workflow_create({  name: "Deployment Pipeline",  description: "Automated testing, building, and multi-environment deployment",  steps: [    { id: "lint", action: "lint_code", agent: "code_quality", parallel: true },    { id: "unit_test", action: "unit_tests", agent: "test_runner", parallel: true },    { id: "integration_test", action: "integration_tests", agent: "test_runner", parallel: true },    { id: "build", action: "build_artifacts", agent: "builder", depends_on: ["lint", "unit_test", "integration_test"] },    { id: "security_scan", action: "security_scan", agent: "security", depends_on: ["build"] },    { id: "deploy_staging", action: "deploy", agent: "deployer", depends_on: ["security_scan"] },    { id: "smoke_test", action: "smoke_tests", agent: "test_runner", depends_on: ["deploy_staging"] },    { id: "deploy_prod", action: "deploy", agent: "deployer", depends_on: ["smoke_test"] }  ],  triggers: ["github_push", "github_pr_merged"],  metadata: {    priority: 10,    auto_rollback: true  }})

mcp__flow-nexus__workflow_create({  name: "Deployment Pipeline",  description: "Automated testing, building, and multi-environment deployment",  steps: [    { id: "lint", action: "lint_code", agent: "code_quality", parallel: true },    { id: "unit_test", action: "unit_tests", agent: "test_runner", parallel: true },    { id: "integration_test", action: "integration_tests", agent: "test_runner", parallel: true },    { id: "build", action: "build_artifacts", agent: "builder", depends_on: ["lint", "unit_test", "integration_test"] },    { id: "security_scan", action: "security_scan", agent: "security", depends_on: ["build"] },    { id: "deploy_staging", action: "deploy", agent: "deployer", depends_on: ["security_scan"] },    { id: "smoke_test", action: "smoke_tests", agent: "test_runner", depends_on: ["deploy_staging"] },    { id: "deploy_prod", action: "deploy", agent: "deployer", depends_on: ["smoke_test"] }  ],  triggers: ["github_push", "github_pr_merged"],  metadata: {    priority: 10,    auto_rollback: true  }})

Data Processing Pipeline Pattern

javascript

mcp__flow-nexus__workflow_create({  name: "ETL Pipeline",  description: "Extract, Transform, Load data processing",  steps: [    { id: "extract", action: "extract_data", agent: "data_extractor" },    { id: "validate_raw", action: "validate_data", agent: "validator", depends_on: ["extract"] },    { id: "transform", action: "transform_data", agent: "transformer", depends_on: ["validate_raw"] },    { id: "enrich", action: "enrich_data", agent: "enricher", depends_on: ["transform"] },    { id: "load", action: "load_data", agent: "loader", depends_on: ["enrich"] },    { id: "validate_final", action: "validate_data", agent: "validator", depends_on: ["load"] }  ],  triggers: ["schedule:0 2 * * *"], // Daily at 2 AM  metadata: {    retry_policy: "exponential_backoff",    max_retries: 3  }})

mcp__flow-nexus__workflow_create({  name: "ETL Pipeline",  description: "Extract, Transform, Load data processing",  steps: [    { id: "extract", action: "extract_data", agent: "data_extractor" },    { id: "validate_raw", action: "validate_data", agent: "validator", depends_on: ["extract"] },    { id: "transform", action: "transform_data", agent: "transformer", depends_on: ["validate_raw"] },    { id: "enrich", action: "enrich_data", agent: "enricher", depends_on: ["transform"] },    { id: "load", action: "load_data", agent: "loader", depends_on: ["enrich"] },    { id: "validate_final", action: "validate_data", agent: "validator", depends_on: ["load"] }  ],  triggers: ["schedule:0 2 * * *"], // Daily at 2 AM  metadata: {    retry_policy: "exponential_backoff",    max_retries: 3  }})

Templates & Patterns

Use Pre-built Templates

javascript

// Create swarm from templatemcp__flow-nexus__swarm_create_from_template({  template_name: "full-stack-dev",  overrides: {    maxAgents: 6,    strategy: "specialized"  }})
// List available templatesmcp__flow-nexus__swarm_templates_list({  category: "quickstart", // Options: quickstart, specialized, enterprise, custom, all  includeStore: true})

// Create swarm from templatemcp__flow-nexus__swarm_create_from_template({  template_name: "full-stack-dev",  overrides: {    maxAgents: 6,    strategy: "specialized"  }})
// List available templatesmcp__flow-nexus__swarm_templates_list({  category: "quickstart", // Options: quickstart, specialized, enterprise, custom, all  includeStore: true})

Available Template Categories:

Quickstart Templates:

full-stack-dev: Complete web development swarm
research-team: Research and analysis swarm
code-review: Automated code review swarm
data-pipeline: ETL and data processing

Specialized Templates:

ml-development: Machine learning project swarm
mobile-dev: Mobile app development
devops-automation: Infrastructure and deployment
security-audit: Security analysis and testing

Enterprise Templates:

enterprise-migration: Large-scale system migration
multi-repo-sync: Multi-repository coordination
compliance-review: Regulatory compliance workflows
incident-response: Automated incident management

Custom Template Creation

Save successful swarm configurations as reusable templates for future projects.

Advanced Features

Real-time Monitoring

javascript

// Subscribe to execution streamsmcp__flow-nexus__execution_stream_subscribe({  stream_type: "claude-flow-swarm",  deployment_id: "deployment_id"})
// Get execution statusmcp__flow-nexus__execution_stream_status({  stream_id: "stream_id"})
// List files created during executionmcp__flow-nexus__execution_files_list({  stream_id: "stream_id",  created_by: "claude-flow"})

// Subscribe to execution streamsmcp__flow-nexus__execution_stream_subscribe({  stream_type: "claude-flow-swarm",  deployment_id: "deployment_id"})
// Get execution statusmcp__flow-nexus__execution_stream_status({  stream_id: "stream_id"})
// List files created during executionmcp__flow-nexus__execution_files_list({  stream_id: "stream_id",  created_by: "claude-flow"})

Swarm Metrics & Analytics

javascript

// Get swarm performance metricsmcp__flow-nexus__swarm_status({  swarm_id: "id"})
// Analyze workflow efficiencymcp__flow-nexus__workflow_status({  workflow_id: "id",  include_metrics: true})

// Get swarm performance metricsmcp__flow-nexus__swarm_status({  swarm_id: "id"})
// Analyze workflow efficiencymcp__flow-nexus__workflow_status({  workflow_id: "id",  include_metrics: true})

Multi-Swarm Coordination

Coordinate multiple swarms for complex, multi-phase projects:

javascript

// Phase 1: Research swarmconst researchSwarm = await mcp__flow-nexus__swarm_init({  topology: "mesh",  maxAgents: 4})
// Phase 2: Development swarmconst devSwarm = await mcp__flow-nexus__swarm_init({  topology: "hierarchical",  maxAgents: 8})
// Phase 3: Testing swarmconst testSwarm = await mcp__flow-nexus__swarm_init({  topology: "star",  maxAgents: 5})

// Phase 1: Research swarmconst researchSwarm = await mcp__flow-nexus__swarm_init({  topology: "mesh",  maxAgents: 4})
// Phase 2: Development swarmconst devSwarm = await mcp__flow-nexus__swarm_init({  topology: "hierarchical",  maxAgents: 8})
// Phase 3: Testing swarmconst testSwarm = await mcp__flow-nexus__swarm_init({  topology: "star",  maxAgents: 5})

Best Practices

1. Choose the Right Topology

javascript

// Simple projects: Starmcp__flow-nexus__swarm_init({ topology: "star", maxAgents: 3 })
// Collaborative work: Meshmcp__flow-nexus__swarm_init({ topology: "mesh", maxAgents: 5 })
// Complex projects: Hierarchicalmcp__flow-nexus__swarm_init({ topology: "hierarchical", maxAgents: 10 })
// Sequential workflows: Ringmcp__flow-nexus__swarm_init({ topology: "ring", maxAgents: 4 })

// Simple projects: Starmcp__flow-nexus__swarm_init({ topology: "star", maxAgents: 3 })
// Collaborative work: Meshmcp__flow-nexus__swarm_init({ topology: "mesh", maxAgents: 5 })
// Complex projects: Hierarchicalmcp__flow-nexus__swarm_init({ topology: "hierarchical", maxAgents: 10 })
// Sequential workflows: Ringmcp__flow-nexus__swarm_init({ topology: "ring", maxAgents: 4 })

2. Optimize Agent Assignment

javascript

// Use vector similarity for optimal matchingmcp__flow-nexus__workflow_agent_assign({  task_id: "complex-task",  use_vector_similarity: true})

// Use vector similarity for optimal matchingmcp__flow-nexus__workflow_agent_assign({  task_id: "complex-task",  use_vector_similarity: true})

3. Implement Proper Error Handling

javascript

mcp__flow-nexus__workflow_create({  name: "Resilient Workflow",  steps: [...],  metadata: {    retry_policy: "exponential_backoff",    max_retries: 3,    timeout: 300000, // 5 minutes    on_failure: "notify_and_rollback"  }})

mcp__flow-nexus__workflow_create({  name: "Resilient Workflow",  steps: [...],  metadata: {    retry_policy: "exponential_backoff",    max_retries: 3,    timeout: 300000, // 5 minutes    on_failure: "notify_and_rollback"  }})

4. Monitor and Scale

javascript

// Regular monitoringconst status = await mcp__flow-nexus__swarm_status()
// Scale based on workloadif (status.workload > 0.8) {  await mcp__flow-nexus__swarm_scale({ target_agents: status.agents + 2 })}

// Regular monitoringconst status = await mcp__flow-nexus__swarm_status()
// Scale based on workloadif (status.workload > 0.8) {  await mcp__flow-nexus__swarm_scale({ target_agents: status.agents + 2 })}

5. Use Async Execution for Long-Running Workflows

javascript

// Long-running workflows should use message queuesmcp__flow-nexus__workflow_execute({  workflow_id: "data-pipeline",  async: true // Non-blocking execution})
// Monitor progressmcp__flow-nexus__workflow_queue_status({ include_messages: true })

// Long-running workflows should use message queuesmcp__flow-nexus__workflow_execute({  workflow_id: "data-pipeline",  async: true // Non-blocking execution})
// Monitor progressmcp__flow-nexus__workflow_queue_status({ include_messages: true })

6. Clean Up Resources

7. Leverage Templates

javascript

// Use proven templates instead of building from scratchmcp__flow-nexus__swarm_create_from_template({  template_name: "code-review",  overrides: { maxAgents: 4 }})

// Use proven templates instead of building from scratchmcp__flow-nexus__swarm_create_from_template({  template_name: "code-review",  overrides: { maxAgents: 4 }})

Integration with Claude Flow

Flow Nexus swarms integrate seamlessly with Claude Flow hooks:

bash

# Pre-task coordination setupnpx claude-flow@alpha hooks pre-task --description "Initialize swarm"
# Post-task metrics exportnpx claude-flow@alpha hooks post-task --task-id "swarm-execution"

# Pre-task coordination setupnpx claude-flow@alpha hooks pre-task --description "Initialize swarm"
# Post-task metrics exportnpx claude-flow@alpha hooks post-task --task-id "swarm-execution"

Common Use Cases

1. Multi-Repo Development

Coordinate development across multiple repositories
Synchronized testing and deployment
Cross-repo dependency management

2. Research Projects

Distributed information gathering
Parallel analysis of different data sources
Collaborative synthesis and reporting

3. DevOps Automation

Infrastructure as Code deployment
Multi-environment testing
Automated rollback and recovery

4. Code Quality Workflows

Automated code review
Security scanning
Performance benchmarking

5. Data Processing

Large-scale ETL pipelines
Real-time data transformation
Data validation and quality checks

Authentication & Setup

bash

# Install Flow Nexusnpm install -g flow-nexus@latest
# Register accountnpx flow-nexus@latest register
# Loginnpx flow-nexus@latest login
# Add MCP server to Claude Codeclaude mcp add flow-nexus npx flow-nexus@latest mcp start

# Install Flow Nexusnpm install -g flow-nexus@latest
# Register accountnpx flow-nexus@latest register
# Loginnpx flow-nexus@latest login
# Add MCP server to Claude Codeclaude mcp add flow-nexus npx flow-nexus@latest mcp start

Support & Resources

Platform: https://flow-nexus.ruv.io
Documentation: https://github.com/ruvnet/flow-nexus
Issues: https://github.com/ruvnet/flow-nexus/issues

Remember: Flow Nexus provides cloud-based orchestration infrastructure. For local execution and coordination, use the core claude-flow MCP server alongside Flow Nexus for maximum flexibility.

来源与署名

来源：ruvnet/ruflo位于.claude/skills/flow-nexus-swarm提交60de638

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Flow Nexus Swarm

Flow Nexus Swarm & Workflow Orchestration

📋 Table of Contents

Overview

Swarm Management

Initialize Swarm

Spawn Agents

Orchestrate Tasks

Monitor & Scale Swarms

Workflow Automation

Create Workflow

Execute Workflow

Monitor Workflows

Agent Assignment

Queue Management

Agent Orchestration

Full-Stack Development Pattern

Research & Analysis Pattern

CI/CD Pipeline Pattern

Data Processing Pipeline Pattern

Templates & Patterns

Use Pre-built Templates

Custom Template Creation

Advanced Features

Real-time Monitoring

Swarm Metrics & Analytics

Multi-Swarm Coordination

Best Practices

1. Choose the Right Topology

2. Optimize Agent Assignment

3. Implement Proper Error Handling

4. Monitor and Scale

5. Use Async Execution for Long-Running Workflows

6. Clean Up Resources

7. Leverage Templates

Integration with Claude Flow

Common Use Cases

1. Multi-Repo Development

2. Research Projects

3. DevOps Automation

4. Code Quality Workflows

5. Data Processing

Authentication & Setup

Support & Resources

来源与署名

更多来自 ruvnet/ruflo 的技能

Cron Schedule

Autopilot Predict

Autopilot Loop

Worker Integration

Worker Benchmarks

Swarm Advanced

更多AI & Agents技能

Ppt Template Creator

Skill Development

Plugin Structure

Command Development

Claude Md Improver

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