euaziel/-wifi-csi-human-pose-detection-v2/flow-nexus-swarm

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

Install

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SKILL.md

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.

Self-Learning Intelligence

Integrates with RuVector's Q-learning and vector memory for improved performance. CLI: node .claude/intelligence/cli.js stats

📋 Table of Contents

  1. Overview
  2. Swarm Management
  3. Workflow Automation
  4. Agent Orchestration
  5. Templates & Patterns
  6. Advanced Features
  7. 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

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Popularity

Repository stars
131
Repository forks
33
7-day trend score
24

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