flow-nexus-swarm

What this skill does

# 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_spaw