flow-nexus-neural

SKILL.md

Flow Nexus Neural Networks

Deploy, train, and manage neural networks in distributed E2B sandbox environments. Train custom models with multiple architectures (feedforward, LSTM, GAN, transformer) or use pre-built templates from the marketplace.

Prerequisites

# Add Flow Nexus MCP server
claude mcp add flow-nexus npx flow-nexus@latest mcp start

# Register and login
npx flow-nexus@latest register
npx flow-nexus@latest login

Core Capabilities

1. Single-Node Neural Training
Train neural networks with custom architectures and configurations.

Available Architectures:

feedforward - Standard fully-connected networks

lstm - Long Short-Term Memory for sequences

gan - Generative Adversarial Networks

autoencoder - Dimensionality reduction

transformer - Attention-based models

Training Tiers:

nano - Minimal resources (fast, limited)

mini - Small models

small - Standard models

medium - Complex models

large - Large-scale training

Example: Train Custom Classifier

mcp__flow-nexus__neural_train({
  config: {
    architecture: {
      type: "feedforward",
      layers: [
        { type: "dense", units: 256, activation: "relu" },
        { type: "dropout", rate: 0.3 },
        { type: "dense", units: 128, activation: "relu" },
        { type: "dropout", rate: 0.2 },
        { type: "dense", units: 64, activation: "relu" },
        { type: "dense", units: 10, activation: "softmax" }
      ]
    },
    training: {
      epochs: 100,
      batch_size: 32,
      learning_rate: 0.001,
      optimizer: "adam"
    },
    divergent: {
      enabled: true,
      pattern: "lateral", // quantum, chaotic, associative, evolutionary
      factor: 0.5
    }
  },
  tier: "small",
  user_id: "your_user_id"
})

Example: LSTM for Time Series

mcp__flow-nexus__neural_train({
  config: {
    architecture: {
      type: "lstm",
      layers: [
        { type: "lstm", units: 128, return_sequences: true },
        { type: "dropout", rate: 0.2 },
        { type: "lstm", units: 64 },
        { type: "dense", units: 1, activation: "linear" }
      ]
    },
    training: {
      epochs: 150,
      batch_size: 64,
      learning_rate: 0.01,
      optimizer: "adam"
    }
  },
  tier: "medium"
})

Example: Transformer Architecture

mcp__flow-nexus__neural_train({
  config: {
    architecture: {
      type: "transformer",
      layers: [
        { type: "embedding", vocab_size: 10000, embedding_dim: 512 },
        { type: "transformer_encoder", num_heads: 8, ff_dim: 2048 },
        { type: "global_average_pooling" },
        { type: "dense", units: 128, activation: "relu" },
        { type: "dense", units: 2, activation: "softmax" }
      ]
    },
    training: {
      epochs: 50,
      batch_size: 16,
      learning_rate: 0.0001,
      optimizer: "adam"
    }
  },
  tier: "large"
})

2. Model Inference

Run predictions on trained models.

mcp__flow-nexus__neural_predict({
  model_id: "model_abc123",
  input: [
    [0.5, 0.3, 0.2, 0.1],
    [0.8, 0.1, 0.05, 0.05],
    [0.2, 0.6, 0.15, 0.05]
  ],
  user_id: "your_user_id"
})

Response:

{
  "predictions": [
    [0.12, 0.85, 0.03],
    [0.89, 0.08, 0.03],
    [0.05, 0.92, 0.03]
  ],
  "inference_time_ms": 45,
  "model_version": "1.0.0"
}

3. Template Marketplace

Browse and deploy pre-trained models from the marketplace.

List Available Templates

mcp__flow-nexus__neural_list_templates({
  category: "classification", // timeseries, regression, nlp, vision, anomaly, generative
  tier: "free", // or "paid"
  search: "sentiment",
  limit: 20
})

Response:

{
  "templates": [
    {
      "id": "sentiment-analysis-v2",
      "name": "Sentiment Analysis Classifier",
      "description": "Pre-trained BERT model for sentiment analysis",
      "category": "nlp",
      "accuracy": 0.94,
      "downloads": 1523,
      "tier": "free"
    },
    {
      "id": "image-classifier-resnet",
      "name": "ResNet Image Classifier",
      "description": "ResNet-50 for image classification",
      "category": "vision",
      "accuracy": 0.96,
      "downloads": 2341,
      "tier": "paid"
    }
  ]
}

Deploy Template

mcp__flow-nexus__neural_deploy_template({
  template_id: "sentiment-analysis-v2",
  custom_config: {
    training: {
      epochs: 50,
      learning_rate: 0.0001
    }
  },
  user_id: "your_user_id"
})

4. Distributed Training Clusters

Train large models across multiple E2B sandboxes with distributed computing.

Initialize Cluster

mcp__flow-nexus__neural_cluster_init({
  name: "large-model-cluster",
  architecture: "transformer", // transformer, cnn, rnn, gnn, hybrid
  topology: "mesh", // mesh, ring, star, hierarchical
  consensus: "proof-of-learning", // byzantine, raft, gossip
  daaEnabled: true, // Decentralized Autonomous Agents
  wasmOptimization: true
})

Response:

{
  "cluster_id": "cluster_xyz789",
  "name": "large-model-cluster",
  "status": "initializing",
  "topology": "mesh",
  "max_nodes": 100,
  "created_at": "2025-10-19T10:30:00Z"
}

Deploy Worker Nodes

// Deploy parameter server
mcp__flow-nexus__neural_node_deploy({
  cluster_id: "cluster_xyz789",
  node_type: "parameter_server",
  model: "large",
  template: "nodejs",
  capabilities: ["parameter_management", "gradient_aggregation"],
  autonomy: 0.8
})

// Deploy worker nodes
mcp__flow-nexus__neural_node_deploy({
  cluster_id: "cluster_xyz789",
  node_type: "worker",
  model: "xl",
  role: "worker",
  capabilities: ["training", "inference"],
  layers: [
    { type: "transformer_encoder", num_heads: 16 },
    { type: "feed_forward", units: 4096 }
  ],
  autonomy: 0.9
})

// Deploy aggregator
mcp__flow-nexus__neural_node_deploy({
  cluster_id: "cluster_xyz789",
  node_type: "aggregator",
  model: "large",
  capabilities: ["gradient_aggregation", "model_synchronization"]
})

Connect Cluster Topology

mcp__flow-nexus__neural_cluster_connect({
  cluster_id: "cluster_xyz789",
  topology: "mesh" // Override default if needed
})

Start Distributed Training

mcp__flow-nexus__neural_train_distributed({
  cluster_id: "cluster_xyz789",
  dataset: "imagenet", // or custom dataset identifier
  epochs: 100,
  batch_size: 128,
  learning_rate: 0.001,
  optimizer: "adam", // sgd, rmsprop, adagrad
  federated: true // Enable federated learning
})

Federated Learning Example:

mcp__flow-nexus__neural_train_distributed({
  cluster_id: "cluster_xyz789",
  dataset: "medical_images_distributed",
  epochs: 200,
  batch_size: 64,
  learning_rate: 0.0001,
  optimizer: "adam",
  federated: true, // Data stays on local nodes
  aggregation_rounds: 50,
  min_nodes_per_round: 5
})

Monitor Cluster Status

mcp__flow-nexus__neural_cluster_status({
  cluster_id: "cluster_xyz789"
})

Response:

{
  "cluster_id": "cluster_xyz789",
  "status": "training",
  "nodes": [
    {
      "node_id": "node_001",
      "type": "parameter_server",
      "status": "active",
      "cpu_usage": 0.75,
      "memory_usage": 0.82
    },
    {
      "node_id": "node_002",
      "type": "worker",
      "status": "active",
      "training_progress": 0.45
    }
  ],
  "training_metrics": {
    "current_epoch": 45,
    "total_epochs": 100,
    "loss": 0.234,
    "accuracy": 0.891
  }
}

Run Distributed Inference

mcp__flow-nexus__neural_predict_distributed({
  cluster_id: "cluster_xyz789",
  input_data: JSON.stringify([
    [0.1, 0.2, 0.3],
    [0.4, 0.5, 0.6]
  ]),
  aggregation: "ensemble" // mean, majority, weighted, ensemble
})

Terminate Cluster

mcp__flow-nexus__neural_cluster_terminate({
  cluster_id: "cluster_xyz789"
})

5. Model Management

List Your Models

mcp__flow-nexus__neural_list_models({
  user_id: "your_user_id",
  include_public: true
})

Response:

{
  "models": [
    {
      "model_id": "model_abc123",
      "name": "Custom Classifier v1",
      "architecture": "feedforward",
      "accuracy": 0.92,
      "created_at": "2025-10-15T14:20:00Z",
      "status": "trained"
    },
    {
      "model_id": "model_def456",
      "name": "LSTM Forecaster",
      "architecture": "lstm",
      "mse": 0.0045,
      "created_at": "2025-10-18T09:15:00Z",
      "status": "training"
    }
  ]
}

Check Training Status

mcp__flow-nexus__neural_training_status({
  job_id: "job_training_xyz"
})

Response:

{
  "job_id": "job_training_xyz",
  "status": "training",
  "progress": 0.67,
  "current_epoch": 67,
  "total_epochs": 100,
  "current_loss": 0.234,
  "estimated_completion": "2025-10-19T12:45:00Z"
}

Performance Benchmarking

mcp__flow-nexus__neural_performance_benchmark({
  model_id: "model_abc123",
  benchmark_type: "comprehensive" // inference, throughput, memory, comprehensive
})

Response:

{
  "model_id": "model_abc123",
  "benchmarks": {
    "inference_latency_ms": 12.5,
    "throughput_qps": 8000,
    "memory_usage_mb": 245,
    "gpu_utilization": 0.78,
    "accuracy": 0.92,
    "f1_score": 0.89
  },
  "timestamp": "2025-10-19T11:00:00Z"
}

Create Validation Workflow

mcp__flow-nexus__neural_validation_workflow({
  model_id: "model_abc123",
  user_id: "your_user_id",
  validation_type: "comprehensive" // performance, accuracy, robustness, comprehensive
})

6. Publishing and Marketplace

Publish Model as Template

mcp__flow-nexus__neural_publish_template({
  model_id: "model_abc123",
  name: "High-Accuracy Sentiment Classifier",
  description: "Fine-tuned BERT model for sentiment analysis with 94% accuracy",
  category: "nlp",
  price: 0, // 0 for free, or credits amount
  user_id: "your_user_id"
})

Rate a Template

mcp__flow-nexus__neural_rate_template({
  template_id: "sentiment-analysis-v2",
  rating: 5,
  review: "Excellent model! Achieved 95% accuracy on my dataset.",
  user_id: "your_user_id"
})

Common Use Cases

Image Classification with CNN

// Initialize cluster for large-scale image training
const cluster = await mcp__flow-nexus__neural_cluster_init({
  name: "image-classification-cluster",
  architecture: "cnn",
  topology: "hierarchical",
  wasmOptimization: true
})

// Deploy worker nodes
await mcp__flow-nexus__neural_node_deploy({
  cluster_id: cluster.cluster_id,
  node_type: "worker",
  model: "large",
  capabilities: ["training", "data_augmentation"]
})

// Start training
await mcp__flow-nexus__neural_train_distributed({
  cluster_id: cluster.cluster_id,
  dataset: "custom_images",
  epochs: 100,
  batch_size: 64,
  learning_rate: 0.001,
  optimizer: "adam"
})

NLP Sentiment Analysis

// Use pre-built template
const deployment = await mcp__flow-nexus__neural_deploy_template({
  template_id: "sentiment-analysis-v2",
  custom_config: {
    training: {
      epochs: 30,
      batch_size: 16
    }
  }
})

// Run inference
const result = await mcp__flow-nexus__neural_predict({
  model_id: deployment.model_id,
  input: ["This product is amazing!", "Terrible experience."]
})

Time Series Forecasting

// Train LSTM model
const training = await mcp__flow-nexus__neural_train({
  config: {
    architecture: {
      type: "lstm",
      layers: [
        { type: "lstm", units: 128, return_sequences: true },
        { type: "dropout", rate: 0.2 },
        { type: "lstm", units: 64 },
        { type: "dense", units: 1 }
      ]
    },
    training: {
      epochs: 150,
      batch_size: 64,
      learning_rate: 0.01,
      optimizer: "adam"
    }
  },
  tier: "medium"
})

// Monitor progress
const status = await mcp__flow-nexus__neural_training_status({
  job_id: training.job_id
})

Federated Learning for Privacy

// Initialize federated cluster
const cluster = await mcp__flow-nexus__neural_cluster_init({
  name: "federated-medical-cluster",
  architecture: "transformer",
  topology: "mesh",
  consensus: "proof-of-learning",
  daaEnabled: true
})

// Deploy nodes across different locations
for (let i = 0; i < 5; i++) {
  await mcp__flow-nexus__neural_node_deploy({
    cluster_id: cluster.cluster_id,
    node_type: "worker",
    model: "large",
    autonomy: 0.9
  })
}

// Train with federated learning (data never leaves nodes)
await mcp__flow-nexus__neural_train_distributed({
  cluster_id: cluster.cluster_id,
  dataset: "medical_records_distributed",
  epochs: 200,
  federated: true,
  aggregation_rounds: 100
})

Architecture Patterns

Feedforward Networks

Best for: Classification, regression, simple pattern recognition

{
  type: "feedforward",
  layers: [
    { type: "dense", units: 256, activation: "relu" },
    { type: "dropout", rate: 0.3 },
    { type: "dense", units: 128, activation: "relu" },
    { type: "dense", units: 10, activation: "softmax" }
  ]
}

LSTM Networks

Best for: Time series, sequences, forecasting

{
  type: "lstm",
  layers: [
    { type: "lstm", units: 128, return_sequences: true },
    { type: "lstm", units: 64 },
    { type: "dense", units: 1 }
  ]
}

Transformers

Best for: NLP, attention mechanisms, large-scale text

{
  type: "transformer",
  layers: [
    { type: "embedding", vocab_size: 10000, embedding_dim: 512 },
    { type: "transformer_encoder", num_heads: 8, ff_dim: 2048 },
    { type: "global_average_pooling" },
    { type: "dense", units: 2, activation: "softmax" }
  ]
}

GANs

Best for: Generative tasks, image synthesis

{
  type: "gan",
  generator_layers: [...],
  discriminator_layers: [...]
}

Autoencoders

Best for: Dimensionality reduction, anomaly detection

{
  type: "autoencoder",
  encoder_layers: [
    { type: "dense", units: 128, activation: "relu" },
    { type: "dense", units: 64, activation: "relu" }
  ],
  decoder_layers: [
    { type: "dense", units: 128, activation: "relu" },
    { type: "dense", units: input_dim, activation: "sigmoid" }
  ]
}

Best Practices

Start Small: Begin with nano or mini tiers for experimentation

Use Templates: Leverage marketplace templates for common tasks

Monitor Training: Check status regularly to catch issues early

Benchmark Models: Always benchmark before production deployment

Distributed Training: Use clusters for large models (>1B parameters)

Federated Learning: Use for privacy-sensitive data

Version Models: Publish successful models as templates for reuse

Validate Thoroughly: Use validation workflows before deployment

Troubleshooting

Training Stalled

// Check cluster status
const status = await mcp__flow-nexus__neural_cluster_status({
  cluster_id: "cluster_id"
})

// Terminate and restart if needed
await mcp__flow-nexus__neural_cluster_terminate({
  cluster_id: "cluster_id"
})

Low Accuracy

Increase epochs

Adjust learning rate

Add regularization (dropout)

Try different optimizer

Use data augmentation

Out of Memory

Reduce batch size

Use smaller model tier

Enable gradient accumulation

Use distributed training

Related Skills

flow-nexus-sandbox - E2B sandbox management

flow-nexus-swarm - AI swarm orchestration

flow-nexus-workflow - Workflow automation

Resources

Flow Nexus Docs: https:/$flow-nexus.ruv.io$docs

Neural Network Guide: https:/$flow-nexus.ruv.io$docs$neural

Template Marketplace: https:/$flow-nexus.ruv.io$templates

API Reference: https:/$flow-nexus.ruv.io$api

Note: Distributed training requires authentication. Register at https:/$flow-nexus.ruv.io or use npx flow-nexus@latest register.