chess/.claude/skills/flow-nexus-neural/SKILL.md

---
name: flow-nexus-neural
description: Train and deploy neural networks in distributed E2B sandboxes with Flow Nexus
version: 1.0.0
category: ai-ml
tags:
  - neural-networks
  - distributed-training
  - machine-learning
  - deep-learning
  - flow-nexus
  - e2b-sandboxes
requires_auth: true
mcp_server: flow-nexus
---

# 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

```bash
# 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

```javascript
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

```javascript
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

```javascript
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.

```javascript
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:**
```json
{
  "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

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

**Response:**
```json
{
  "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

```javascript
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

```javascript
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:**
```json
{
  "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

```javascript
// 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

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

#### Start Distributed Training

```javascript
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:**
```javascript
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

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

**Response:**
```json
{
  "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

```javascript
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

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

### 5. Model Management

#### List Your Models

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

**Response:**
```json
{
  "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

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

**Response:**
```json
{
  "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

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

**Response:**
```json
{
  "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

```javascript
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

```javascript
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

```javascript
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

```javascript
// 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

```javascript
// 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

```javascript
// 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

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

### Autoencoders
Best for: Dimensionality reduction, anomaly detection
```javascript
{
  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

1. **Start Small**: Begin with `nano` or `mini` tiers for experimentation
2. **Use Templates**: Leverage marketplace templates for common tasks
3. **Monitor Training**: Check status regularly to catch issues early
4. **Benchmark Models**: Always benchmark before production deployment
5. **Distributed Training**: Use clusters for large models (>1B parameters)
6. **Federated Learning**: Use for privacy-sensitive data
7. **Version Models**: Publish successful models as templates for reuse
8. **Validate Thoroughly**: Use validation workflows before deployment

## Troubleshooting

### Training Stalled
```javascript
// 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`.