# Train Distributed Neural Models with Flow Nexus

Neural training across sandboxes is hard to coordinate. This skill gives Claude, Codex, and Claude Code workflows for Flow Nexus training, inference, templates, and clusters.

## Install

```bash
npx skillstore add dnyoussef/flow-nexus-neural
```

## Metadata

- Status: approved
- Slug: dnyoussef-flow-nexus-neural
- Version: 1.0.0
- Author version: 1.0.0
- Skillstore revision: r1
- Version status: valid
- Tree hash: 3c948a3eae6b93b2f5c9c8b33f9cbe2229cabc0266b9a032361d044b5c35203b
- Author: DNYoussef
- GitHub username: DNYoussef
- License: MIT
- Repository: https://github.com/DNYoussef/ai-chrome-extension/tree/main/.claude/skills/flow-nexus-neural
- Ref: c579efd02184f1128d6741d16e6c8d951d7d4208
- Supported tools: Claude, Codex, Claude Code
- Audit status: complete
- Agent install advisory: allowed
- Manual install advisory: allowed
- Artifact signature: available
- Audit attestation: unavailable
- Human verification: not\_verified
- Risk factors: external\_commands, network
- Quality score: 71
- Public page: https://skillstore.pages.dev/skills/dnyoussef-flow-nexus-neural
- Manifest: https://skillstore.pages.dev/api/skills/dnyoussef-flow-nexus-neural/manifest

## Capabilities

- Shows Flow Nexus MCP setup and authentication commands for the user.
- Provides neural training examples for feedforward, LSTM, transformer, GAN, and autoencoder models.
- Demonstrates model inference, benchmarking, validation, publishing, rating, and template marketplace workflows.
- Provides distributed training cluster examples with node deployment, topology connection, status monitoring, and termination.
- Includes troubleshooting guidance for stalled training, low accuracy, and memory pressure.

## Use Cases

- Prototype Neural Models: Create small training configurations before investing in larger model runs.
- Reuse Marketplace Templates: Find, deploy, test, and rate Flow Nexus templates for common model tasks.
- Coordinate Distributed Training: Plan clusters, worker nodes, monitoring, and termination for large model jobs.

## Prompt Templates

### Start a Small Training Run

```
Use flow-nexus-neural to prepare a small feedforward training plan. Include tier, layers, training settings, and fields I must replace.
```

### Deploy and Test a Template

```
Use flow-nexus-neural to select a marketplace template for my task, deploy it, run inference, and summarize validation steps.
```

### Build a Distributed Cluster

```
Use flow-nexus-neural to design a distributed training cluster with topology, worker roles, monitoring, and a safe termination plan.
```

### Plan Federated Training Operations

```
Use flow-nexus-neural to plan a federated learning workflow. Include node assumptions, aggregation rounds, privacy checks, and validation milestones.
```

## Limitations

- Requires a Flow Nexus account and a configured Flow Nexus MCP server.
- Examples must be adapted to real datasets, model IDs, user IDs, and compute quotas.
- The skill does not validate datasets, credentials, costs, or model ownership.
- Distributed training depends on Flow Nexus and E2B service availability.

## Best Practices

- Start with nano or mini tiers before moving to distributed clusters.
- Pin and verify the Flow Nexus package before adding the MCP server.
- Monitor job status, benchmark results, and validation output before publishing a model.

## Anti Patterns

- Do not run unpinned setup commands in sensitive environments without reviewing package provenance.
- Do not train with private or regulated data until authentication, retention, and isolation are confirmed.
- Do not publish templates without validation, benchmark, and ownership checks.

## Security Audit

- Audited at: 2026-07-05T12:49:13.35\+00:00
- Summary: Most static findings are false positives from Markdown code fences, inline backtick formatting, and Flow Nexus MCP examples. One prerequisite block and one final note instruct users to run unpinned npx flow-nexus@latest commands, which creates a real external-command and supply-chain risk. The hardcoded URLs are documentation or authentication references, and the system reconnaissance hits are neural training fields.

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