Does it support distributed training?

Yes, it supports initializing clusters with various topologies like mesh and ring, enabling decentralized training and federated learning.

Does this skill require authentication?

Yes, you must register and log in to Flow Nexus via the MCP server to use the neural training and deployment features.

What is the Flow Nexus Neural skill?

It is a specialized capability for Claude Code that allows users to train, deploy, and manage neural networks in distributed E2B sandboxes using the Flow Nexus MCP server.

Which neural network architectures can I use?

The skill supports a wide range of architectures including feedforward, LSTM, GAN, autoencoders, and transformers.

Are there pre-built models available?

Yes, the skill includes a template marketplace where you can browse and deploy pre-trained models for NLP, vision, and regression tasks.

Flow Nexus Neural Networks

Name: Flow Nexus Neural Networks
Author: while-basic

bywhile-basic

Data Science & ML

Trains and deploys complex neural networks within distributed E2B sandboxes for scalable machine learning workflows.

About

Flow Nexus Neural is a powerful Claude Code skill designed for developers to architect, train, and manage neural networks in isolated E2B sandbox environments. It provides deep integration with the Flow Nexus MCP server, allowing for the deployment of diverse architectures including Transformers, LSTMs, and GANs. From single-node custom training to orchestrating massive distributed clusters with federated learning capabilities, this skill simplifies the transition from local experimentation to scalable, decentralized AI production. It also features a marketplace for pre-trained templates, comprehensive performance benchmarking, and autonomous agent-led training nodes.

Key Features

Distributed training clusters with mesh, ring, and hierarchical topologies
Template marketplace for rapid deployment of pre-trained vision and NLP models
Multi-architecture support including Transformers, LSTMs, GANs, and Autoencoders
Comprehensive benchmarking for inference latency, throughput, and accuracy
0 GitHub stars
Federated learning support for decentralized, privacy-preserving model training

Use Cases

Orchestrating large-scale distributed training for complex Transformer models across multiple E2B sandboxes.
Rapidly deploying pre-trained sentiment analysis or image classification models via the marketplace.
Implementing time-series forecasting using custom LSTM architectures with automated validation workflows.

About

Key Features

Distributed training clusters with mesh, ring, and hierarchical topologies
Template marketplace for rapid deployment of pre-trained vision and NLP models
Multi-architecture support including Transformers, LSTMs, GANs, and Autoencoders
Comprehensive benchmarking for inference latency, throughput, and accuracy
0 GitHub stars
Federated learning support for decentralized, privacy-preserving model training

Use Cases

Orchestrating large-scale distributed training for complex Transformer models across multiple E2B sandboxes.
Rapidly deploying pre-trained sentiment analysis or image classification models via the marketplace.
Implementing time-series forecasting using custom LSTM architectures with automated validation workflows.