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+---
+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
+hooks:
+  pre: |
+    echo "🧠 Flow Nexus Neural activated"
+    if [ -d "/workspaces/ruvector/.claude/intelligence" ]; then
+      cd /workspaces/ruvector/.claude/intelligence
+      INTELLIGENCE_MODE=treatment node cli.js pre-edit "$FILE" 2>/dev/null || true
+    fi
+  post: |
+    echo "✅ Flow Nexus Neural complete"
+    if [ -d "/workspaces/ruvector/.claude/intelligence" ]; then
+      cd /workspaces/ruvector/.claude/intelligence
+      INTELLIGENCE_MODE=treatment node cli.js post-edit "$FILE" "true" 2>/dev/null || true
+    fi
+---
+
+# 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.
+
+## Self-Learning Intelligence
+
+Integrates with RuVector's Q-learning and vector memory for improved performance.
+CLI: `node .claude/intelligence/cli.js stats`
+
+## 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`.