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/ruv-FANNWrote 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/ruv-fann/neural-train)<a href="https://agentmods.dev/commands/ruvnet/ruv-fann/neural-train"><img src="https://agentmods.dev/badge/commands/ruvnet/ruv-fann/neural-train/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/ruv-fann/neural-train"><img src="https://agentmods.dev/badge/commands/ruvnet/ruv-fann/neural-train.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 | $0.00000 | $0.00133 |
| Opus 5 | $0.00000 | $0.00067 |
| Sonnet 5 | $0.00000 | $0.00027 |
| Haiku 4.5 | $0.00000 | $0.00013 |
Grade A, and why
neural-train 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 5d 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
7 near-identical copies found in the catalogue:
- neural-train — 100% identical, 0 lines differ
- neural-train — 100% identical, 3 lines differ
- neural-train — 100% identical, 0 lines differ
- neural-train — 100% identical, 50 lines differ
- neural-train — 100% identical, 0 lines differ
- neural-train — 100% identical, 0 lines differ
- neural-train — 100% identical, 0 lines differ
What it actually says
neural-train
Train neural patterns from operations.
Usage
npx claude-flow training neural-train [options]
Options
--data <source>- Training data source--model <name>- Target model--epochs <n>- Training epochs
Examples
# Train from recent ops
npx claude-flow training neural-train --data recent
# Specific model
npx claude-flow training neural-train --model task-predictor
# Custom epochs
npx claude-flow training neural-train --epochs 100
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.
- 5d ago First seen · 26 lines · 0 tokens per session scan A 3fa49fcd1ea6
neural-train is a command published in the GitHub repository ruvnet/ruv-FANN (380 stars, last pushed 1mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 133 tokens. 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
laravel-ai-sdk
Build AI features with the first-party Laravel AI SDK (Laravel 13+); use the laravel:ai-sdk skill exactly as written.
audit-prompt
Evaluate an existing prompt for clarity, effectiveness, and edge cases.
develop-image-prompt.eval
Generates a detailed image generation prompt from a document or content description. Good output: a prompt that is specific, visual, non-abstract, includes style/composition/lighting guidance, and is calibrated to the specified dimensions and style options.
dare-llm-integration
Integração segura e eficiente com LLMs (Gemini, Claude, OpenAI, Ollama) em projetos DARE.
prompt-create
Create a new prompt following ground rules.
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.