Borrowing it
Nothing to install: this file belongs to EarthmanWeb/claude-flow-plugin. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/EarthmanWeb/claude-flow-plugin/main/.claude/commands/flow-nexus/neural-network.mdgit clone --depth 1 https://github.com/EarthmanWeb/claude-flow-pluginWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/commands/earthmanweb/claude-flow-plugin/neural-network)<a href="https://agentmods.dev/commands/earthmanweb/claude-flow-plugin/neural-network"><img src="https://agentmods.dev/badge/commands/earthmanweb/claude-flow-plugin/neural-network/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/commands/earthmanweb/claude-flow-plugin/neural-network"><img src="https://agentmods.dev/badge/commands/earthmanweb/claude-flow-plugin/neural-network.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00015 | $0.00781 |
| Opus 5.5 | $0.00006 | $0.00312 |
| Sonnet 5.5 | $0.00003 | $0.00156 |
| Haiku 4.5 | $0.00002 | $0.00078 |
Grade A, and why
flow-nexus-neural scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
100% identical to flow-nexus-neural — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 134 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Flow Nexus Neural Networks
Train custom neural networks with distributed computing.
Train Model
mcp__flow-nexus__neural_train({
config: {
architecture: {
type: "feedforward", // lstm, gan, autoencoder, transformer
layers: [
{ type: "dense", units: 128, activation: "relu" },
{ type: "dropout", rate: 0.2 },
{ type: "dense", units: 10, activation: "softmax" }
]
},
training: {
epochs: 100,
batch_size: 32,
learning_rate: 0.001,
optimizer: "adam"
}
},
tier: "small" // nano, mini, small, medium, large
})
Run Inference
mcp__flow-nexus__neural_predict({
model_id: "model_id",
input: [[0.5, 0.3, 0.2], [0.1, 0.8, 0.1]],
user_id: "your_id"
})
Use Templates
// List templates
mcp__flow-nexus__neural_list_templates({
category: "classification", // regression, nlp, vision, anomaly
tier: "free",
limit: 20
})
// Deploy template
mcp__flow-nexus__neural_deploy_template({
template_id: "sentiment-analysis",
custom_config: {
training: { epochs: 50 }
}
})
Distributed Training
// Initialize cluster
mcp__flow-nexus__neural_cluster_init({
name: "training-cluster",
architecture: "transformer",
topology: "mesh",
consensus: "proof-of-learning",
wasmOptimization: true
})
// Deploy nodes
mcp__flow-nexus__neural_node_deploy({
cluster_id: "cluster_id",
node_type: "worker", // parameter_server, aggregator
model: "large",
capabilities: ["training", "inference"]
})
// Start training
mcp__flow-nexus__neural_train_distributed({
cluster_id: "cluster_id",
dataset: "mnist",
epochs: 100,
federated: true // Enable federated learning
})
Model Management
// List your models
mcp__flow-nexus__neural_list_models({
user_id: "your_id",
include_public: true
})
// Benchmark performance
mcp__flow-nexus__neural_performance_benchmark({
model_id: "model_id",
benchmark_type: "comprehensive"
})
// Publish as template
mcp__flow-nexus__neural_publish_template({
model_id: "model_id",
name: "My Custom Model",
description: "Highly accurate classifier",
category: "classification",
price: 0 // Free template
})
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 134 lines · 15 tokens per session scan A 4d59772eee01
flow-nexus-neural is a command published in the GitHub repository EarthmanWeb/claude-flow-plugin (2 stars, last pushed 8mo ago), licensed MIT. It adds 15 tokens to every session and 781 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to flow-nexus-neural, differing in 0 lines, and is treated as a copy.
Other commands, from other repositories
boot
Start a new PromptKit prompt assembly session (alias for /bootstrap).
music-suno-prompt
Grounded Suno prompt synthesis from local knowledge corpus + persona canon + label canon. No vibes-prompting.
audit-prompt
Evaluate an existing prompt for clarity, effectiveness, and edge cases.
laravel-ai-sdk
Build AI features with the first-party Laravel AI SDK (Laravel 13+); use the laravel:ai-sdk skill exactly as written.
vlm-ocr-evaluation
Run the vlm-ocr skill in its evaluate phase: compare candidate OCR systems against a stratified human-transcribed ground-truth sample and pick a model on measured CER/WER before committing to a bulk run.
dare-llm-integration
Integração segura e eficiente com LLMs (Gemini, Claude, OpenAI, Ollama) em projetos DARE.