home / skills / microck / ordinary-claude-skills / flow-nexus-swarm

flow-nexus-swarm skill

/skills_all/flow-nexus-swarm

This skill helps you deploy and orchestrate cloud AI swarms with event-driven workflows and intelligent agent coordination.

This is most likely a fork of the flow-nexus-swarm skill from plurigrid
npx playbooks add skill microck/ordinary-claude-skills --skill flow-nexus-swarm

Review the files below or copy the command above to add this skill to your agents.

Files (2)
SKILL.md
16.4 KB
---
name: flow-nexus-swarm
description: Cloud-based AI swarm deployment and event-driven workflow automation with Flow Nexus platform
category: orchestration
tags: [swarm, workflow, cloud, agents, automation, message-queue]
version: 1.0.0
requires:
  - flow-nexus MCP server
  - Active Flow Nexus account (register at flow-nexus.ruv.io)
---

# 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.

## 📋 Table of Contents

1. [Overview](#overview)
2. [Swarm Management](#swarm-management)
3. [Workflow Automation](#workflow-automation)
4. [Agent Orchestration](#agent-orchestration)
5. [Templates & Patterns](#templates--patterns)
6. [Advanced Features](#advanced-features)
7. [Best Practices](#best-practices)

## 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
})
```

**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"]
})
```

**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
})
```

**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 status
mcp__flow-nexus__swarm_status({
  swarm_id: "optional-id" // Uses active swarm if not provided
})

// List all active swarms
mcp__flow-nexus__swarm_list({
  status: "active" // Options: active, destroyed, all
})

// Scale swarm up or down
mcp__flow-nexus__swarm_scale({
  target_agents: 10,
  swarm_id: "optional-id"
})

// Gracefully destroy swarm
mcp__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"
  }
})
```

**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
})
```

**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 metrics
mcp__flow-nexus__workflow_status({
  workflow_id: "id",
  execution_id: "specific-run-id", // Optional
  include_metrics: true
})

// List workflows with filters
mcp__flow-nexus__workflow_list({
  status: "running", // Options: running, completed, failed, pending
  limit: 10,
  offset: 0
})

// Get complete audit trail
mcp__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
})
```

**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
})
```

## Agent Orchestration

### Full-Stack Development Pattern

```javascript
// 1. Initialize swarm with hierarchical topology
mcp__flow-nexus__swarm_init({
  topology: "hierarchical",
  maxAgents: 8,
  strategy: "specialized"
})

// 2. Spawn specialized agents
mcp__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 workflow
mcp__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 workflow
mcp__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 research
mcp__flow-nexus__swarm_init({
  topology: "mesh",
  maxAgents: 5,
  strategy: "balanced"
})

// 2. Spawn research agents
mcp__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 task
mcp__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
  }
})
```

### 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
  }
})
```

## Templates & Patterns

### Use Pre-built Templates

```javascript
// Create swarm from template
mcp__flow-nexus__swarm_create_from_template({
  template_name: "full-stack-dev",
  overrides: {
    maxAgents: 6,
    strategy: "specialized"
  }
})

// List available templates
mcp__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 streams
mcp__flow-nexus__execution_stream_subscribe({
  stream_type: "claude-flow-swarm",
  deployment_id: "deployment_id"
})

// Get execution status
mcp__flow-nexus__execution_stream_status({
  stream_id: "stream_id"
})

// List files created during execution
mcp__flow-nexus__execution_files_list({
  stream_id: "stream_id",
  created_by: "claude-flow"
})
```

### Swarm Metrics & Analytics

```javascript
// Get swarm performance metrics
mcp__flow-nexus__swarm_status({
  swarm_id: "id"
})

// Analyze workflow efficiency
mcp__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 swarm
const researchSwarm = await mcp__flow-nexus__swarm_init({
  topology: "mesh",
  maxAgents: 4
})

// Phase 2: Development swarm
const devSwarm = await mcp__flow-nexus__swarm_init({
  topology: "hierarchical",
  maxAgents: 8
})

// Phase 3: Testing swarm
const testSwarm = await mcp__flow-nexus__swarm_init({
  topology: "star",
  maxAgents: 5
})
```

## Best Practices

### 1. Choose the Right Topology

```javascript
// Simple projects: Star
mcp__flow-nexus__swarm_init({ topology: "star", maxAgents: 3 })

// Collaborative work: Mesh
mcp__flow-nexus__swarm_init({ topology: "mesh", maxAgents: 5 })

// Complex projects: Hierarchical
mcp__flow-nexus__swarm_init({ topology: "hierarchical", maxAgents: 10 })

// Sequential workflows: Ring
mcp__flow-nexus__swarm_init({ topology: "ring", maxAgents: 4 })
```

### 2. Optimize Agent Assignment

```javascript
// Use vector similarity for optimal matching
mcp__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"
  }
})
```

### 4. Monitor and Scale

```javascript
// Regular monitoring
const status = await mcp__flow-nexus__swarm_status()

// Scale based on workload
if (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 queues
mcp__flow-nexus__workflow_execute({
  workflow_id: "data-pipeline",
  async: true // Non-blocking execution
})

// Monitor progress
mcp__flow-nexus__workflow_queue_status({ include_messages: true })
```

### 6. Clean Up Resources

```javascript
// Destroy swarm when complete
mcp__flow-nexus__swarm_destroy({ swarm_id: "id" })
```

### 7. Leverage Templates

```javascript
// Use proven templates instead of building from scratch
mcp__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 setup
npx claude-flow@alpha hooks pre-task --description "Initialize swarm"

# Post-task metrics export
npx 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 Nexus
npm install -g flow-nexus@latest

# Register account
npx flow-nexus@latest register

# Login
npx flow-nexus@latest login

# Add MCP server to Claude Code
claude 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.

Overview

This skill enables cloud-based deployment and orchestration of AI agent swarms and event-driven workflows using the Flow Nexus platform. It provides topology-driven swarm management, message-queue workflow automation, intelligent agent assignment, real-time monitoring, and scalable execution. Use it to deploy coordinated multi-agent systems for development, data pipelines, CI/CD, research, and enterprise automation.

How this skill works

The skill exposes API-like primitives to initialize swarms, spawn specialized agents, define event-driven workflows, and orchestrate tasks across agents. Workflows run sync or async via message queues, support dependencies, parallel steps, retry policies, and priority queuing. Agent assignment uses vector-similarity matching and historic performance to pick the best available agents, while monitoring and audit endpoints surface metrics and execution streams.

When to use it

  • Automate multi-step processes that benefit from parallel or dependent task execution
  • Coordinate specialized agents for full-stack development, data ETL, CI/CD, or research
  • Scale temporary or long-running workloads with cloud auto-scaling and queue-driven execution
  • Create reusable swarm templates for repeatable project patterns
  • Implement resilience with retries, priorities, and audit trails

Best practices

  • Choose topology to match collaboration needs (star for simple hubs, mesh for research, hierarchical for complex projects, ring for sequential flows)
  • Use vector-similarity agent assignment to match expertise and reduce manual routing
  • Design workflows with clear dependencies and parallel flags to maximize concurrency where safe
  • Set retry policies, timeouts, and on_failure handlers to make workflows resilient
  • Save validated swarm configurations as templates to speed future deployments

Example use cases

  • Full-stack development pipeline: spawn coordinators and coders, create a development workflow, and execute CI/CD steps
  • ETL data pipeline: schedule nightly extract/transform/load workflows with retry and validation steps
  • Research & analysis: initialize a mesh swarm for collaborative literature review and market analysis
  • CI/CD automation: run linting, tests, security scans, and staged deployments with auto-rollback on failure
  • Multi-phase projects: coordinate separate swarms for research, development, and testing and share artifacts

FAQ

Can workflows run asynchronously?

Yes. Set async:true to queue workflows for non-blocking, message-driven execution with retries and priority queuing.

How does agent assignment pick the best agent?

Agent assignment can enable vector similarity to match task requirements to agent capabilities and consider workload and past performance.