Ruflo is an execution and coordination layer for Claude Code and Codex that equips AI coding agents with tools, memory, control loops, sandboxes, and collaboration mechanisms. Developers use it to organize specialized agents into swarms, coordinate workflows, retain knowledge across sessions, and communicate across machines. The catalogue entries are Ruflo’s skills, commands, agents, hooks, and plugin components.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
git clone --depth 1 https://github.com/ruvnet/rufloWrote 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/ruvnet/ruflo/neural-network)<a href="https://agentmods.dev/commands/ruvnet/ruflo/neural-network"><img src="https://agentmods.dev/badge/commands/ruvnet/ruflo/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/ruvnet/ruflo/neural-network"><img src="https://agentmods.dev/badge/commands/ruvnet/ruflo/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.
Copies of this mod
8 near-identical copies found in the catalogue:
- flow-nexus-neural — 100% identical, 0 lines differ
- flow-nexus-neural — 100% identical, 0 lines differ
- flow-nexus-neural — 100% identical, 0 lines differ
- flow-nexus-neural — 100% identical, 0 lines differ
- flow-nexus-neural — 100% identical, 0 lines differ
- flow-nexus-neural — 100% identical, 0 lines differ
- flow-nexus-neural — 100% identical, 0 lines differ
- flow-nexus-neural — 100% identical, 187 lines differ
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 ruvnet/ruflo (76,506 stars, last pushed yesterday), 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. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other commands, from other repositories
dataset
Manage dataset manifests for data that is too large or unsuitable to store directly in the wiki. The wiki becomes the index/interface; the data stays external.
briefing
Structured pre-execution briefing session -- collects case context through specialist panel, builds execution plan, supports resume and depth control.
legal-5step
Execute the BetterCallClaude 5-step Swiss legal framework: intake → research → strategy → adversarial → draft. A complete end-to-end pipeline for any Swiss legal matter, from document analysis through final legal output.
legal-goal
Define a checkable legal success condition for /legal-loop. Accepts a named profile (citations-clean, draft-passes-gate, adversarial-converge, nda-batch-clean, reg-watch, timeline-sourced) or free-text objective. Produces a persisted Goal Record — never starts work itself.
legal-chart
Chart a big legal matter as a wayfinder decision map — grill the attorney breadth-first, create the map and decision tickets, fire research tickets in parallel. Planning only: charting resolves no decisions itself.
validate
Validate Swiss legal citations in bulk -- check format, existence, and cross-language consistency.