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Train and deploy neural networks in distributed E2B sandboxes with Flow Nexus
기본적으로 소스를 먼저 확인하는 Prompt가 선택됩니다. 직접 명령으로 전환하거나 로컬 사본을 다운로드할 수도 있습니다.
설치 여부를 결정하기 전에 SKILL.md와 SkillsMP에 표시된 보조 파일을 읽어 보세요.
SOC 직업 분류 기준
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
직접 명령은 검토 Prompt를 거치지 않습니다. 실행하기 전에 소스를 확인하세요.
npx skills add https://github.com/ruvnet/RuVector --skill flow-nexus-neural명령은 한 줄로 유지됩니다. 복사하기 전에 가로로 스크롤해 전체 내용을 확인하세요.
로컬 사본을 원하시나요? SkillsMP에서 현재 제공할 수 있는 파일을 다운로드하세요.
SKILL.md 표시 중
Evolve this harness with Darwin Mode — frozen model, evolving harness (real, sandboxed, safety-gated).
Create and run custom background analysis workers with composable phases. Use when you need automated code analysis, security scanning, pattern learning, or API documentation generation.
Quantum-resistant, self-learning version control for AI agents with ReasoningBank intelligence and multi-agent coordination
| 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.