القائمة
Train and deploy neural networks in distributed E2B sandboxes with Flow Nexus
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اقرأ SKILL.md وأي ملفات مرافقة يعرضها SkillsMP قبل أن تقرر التثبيت.
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npx skills add https://github.com/ruvnet/RuVector --skill flow-nexus-neuralيبقى الأمر في سطر واحد. مرّر أفقيًا لمراجعته كاملًا قبل النسخ.
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عرض 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 |
| hooks | {"pre":"echo \"🧠 Flow Nexus Neural activated\"\nif [ -d \"/workspaces/ruvector/.claude/intelligence\" ]; then\n cd /workspaces/ruvector/.claude/intelligence\n INTELLIGENCE_MODE=treatment node cli.js pre-edit \"$FILE\" 2>/dev/null || true\nfi\n","post":"echo \"✅ Flow Nexus Neural complete\"\nif [ -d \"/workspaces/ruvector/.claude/intelligence\" ]; then\n cd /workspaces/ruvector/.claude/intelligence\n INTELLIGENCE_MODE=treatment node cli.js post-edit \"$FILE\" \"true\" 2>/dev/null || true\nfi\n"} |
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.
Integrates with RuVector's Q-learning and vector memory for improved performance.
CLI: node .claude/intelligence/cli.js stats
# 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
Train neural networks with custom architectures and configurations.
Available Architectures:
feedforward - Standard fully-connected networkslstm - Long Short-Term Memory for sequencesgan - Generative Adversarial Networksautoencoder - Dimensionality reductiontransformer - Attention-based modelsTraining Tiers:
nano - Minimal resources (fast, limited)mini - Small modelssmall - Standard modelsmedium - Complex modelslarge - Large-scale trainingmcp__flow-nexus__neural_train({
config: {
architecture: {
type: "feedforward",
layers: [
{ type: "dense", units: 256, activation: "relu" },
{ type: "dropout", rate: 0.3 },
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]
},
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})
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"
})
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"
})
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"
}
Browse and deploy pre-trained models from the marketplace.
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"
}
]
}
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"
})
Train large models across multiple E2B sandboxes with distributed computing.
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 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"]
})
mcp__flow-nexus__neural_cluster_connect({
cluster_id: "cluster_xyz789",
topology: "mesh" // Override default if needed
})
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
})
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
}
}
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
})
mcp__flow-nexus__neural_cluster_terminate({
cluster_id: "cluster_xyz789"
})
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"
}
]
}
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"
}
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"
}
mcp__flow-nexus__neural_validation_workflow({
model_id: "model_abc123",
user_id: "your_user_id",
validation_type: "comprehensive" // performance, accuracy, robustness, comprehensive
})
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"
})
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"
})
// 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"
})
// 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."]
})
// 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
})
// 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
})
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" }
]
}
Best for: Time series, sequences, forecasting
{
type: "lstm",
layers: [
{ type: "lstm", units: 128, return_sequences: true },
{ type: "lstm", units: 64 },
{ type: "dense", units: 1 }
]
}
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" }
]
}
Best for: Generative tasks, image synthesis
{
type: "gan",
generator_layers: [...],
discriminator_layers: [...]
}
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" }
]
}
nano or mini tiers for experimentation// 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"
})
flow-nexus-sandbox - E2B sandbox managementflow-nexus-swarm - AI swarm orchestrationflow-nexus-workflow - Workflow automationNote: Distributed training requires authentication. Register at https://flow-nexus.ruv.io or use npx flow-nexus@latest register.