---
name: flow-nexus-neural-e9d5946f4e09
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`.


---
Source attribution: Activer007
Original project: https://github.com/Activer007/ordinary-claude-skills/blob/e3e1b3d55f9a132a68d242040d23048a7ba8f85c/skills_all/flow-nexus-neural/SKILL.md
License: MIT
License information: https://opensource.org/license/mit
Original upstream instructions; downloaded from Mega AI Hub.

## Upstream license notice

MIT License

Copyright (c) 2025 Microck

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

i did not write most of these. i just collected them.

*   **anthropic skills:** mit license (mostly)
*   **community skills:** check the `LICENSE` file in each specific folder.

credits go to https://github.com/anthropics, https://github.com/obra, https://github.com/ComposioHQ, and the other people listed in the source tables. if you own one of these and want me to take it down, just open an issue and i will nuke it.



# Mega AI Hub resource metadata

Creator / publisher: Activer007
Original project: https://github.com/Activer007/ordinary-claude-skills/blob/e3e1b3d55f9a132a68d242040d23048a7ba8f85c/skills_all/flow-nexus-neural/SKILL.md
Attribution basis: Source repository record

Fields of work: Data & Analytics, Development & Engineering
Roles: Backend Developer, Data Scientist, ML / AI Engineer
Specialties: API, Backend
Tools: Flow Nexus
Country applicability: Country not specified
Classification basis: Source catalog labels
Instruction SHA-256: d46b975bce9fa57eec82321e65a8bdb28d641918c66d7885f6283d399dfacb33