flow-nexus-swarm

Files (1)

• SKILL.md

SKILL.md (first lines)

# 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

From aegntic/cldcde, MIT.

More from aegntic/cldcde