agentmods.dev

ruview-train

ruview-train is a command for coding agents from ruvnet/RuView. It costs 45 tokens per session (528 once invoked), scanned A, original, MIT.

Train a RuView model — camera-free WiFlow pose, camera-supervised pose (92.9% PCK@20), RuVector embeddings, domain generalization, local SNN, with optional GPU on GCloud.

Command

not rated 92k today A scan 45 tokens MIT
View the source file ruvnet/RuView
Install

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.

agentmods
npx agentmods add commands/ruvnet/ruview/ruview-train
Clone the repo
git clone --depth 1 https://github.com/ruvnet/RuView

Wrote 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.

agentmods badge for ruview-train

README.md
[![agentmods](https://agentmods.dev/badge/commands/ruvnet/ruview/ruview-train.svg)](https://agentmods.dev/commands/ruvnet/ruview/ruview-train)
Your own site
<a href="https://agentmods.dev/commands/ruvnet/ruview/ruview-train"><img src="https://agentmods.dev/badge/commands/ruvnet/ruview/ruview-train.svg" alt="Measured on agentmods" height="20"></a>
Per session 45 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 528 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin unknown No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00045 $0.00528
Opus 5 $0.00023 $0.00264
Sonnet 5 $0.00009 $0.00106
Haiku 4.5 $0.00005 $0.00053

Measured yesterday against content hash c8022ff4d393, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

ruview-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 yesterday.

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.

plugins/ruview/commands/ruview-train.md · 19 lines

What it actually says

/ruview-train

Train, fine-tune, evaluate, or publish a RuView model.

  1. Invoke the ruview-model-training skill.
  2. Pick the track from $ARGUMENTS; if empty, ask which:
    • camera-free (Track A) — cargo run -p wifi-densepose-sensing-server -- --pretrain --dataset data/csi/ --pretrain-epochs 50 then -- --train --dataset data/mmfi/ --epochs 100 --save-rvf model.rvf. ~84 s on M4 Pro, modest accuracy.
    • camera-supervised (Track B, ADR-079) — python scripts/collect-ground-truth.py, python scripts/collect-training-data.py, node scripts/align-ground-truth.js, then train on data/paired/, eval with node scripts/eval-wiflow.js. ~19 min, 92.9% PCK@20. Needs data/pose_landmarker_lite.task.
    • embeddings (Track C, AETHER ADR-024) — wifi-densepose-train + wifi-densepose-ruvector; -- --model model.rvf --embed, -- --build-index env.
    • domain-gen (Track D, MERIDIAN ADR-027) / snn (Track E) — node scripts/snn-csi-processor.js --port 5006; cognitum-seed-pretraining tutorial.
    • gpu — gcloud config set project cognitum-20260110; bash scripts/gcloud-train.sh --gpu l4 --hours 2 (or --gpu a100 --sweep, --dry-run to smoke-test). VM auto-deletes unless --keep-vm.
  3. After training: cd v2 && cargo test --workspace --no-default-features, python archive/v1/data/proof/verify.py. To publish: python scripts/publish-huggingface.py.
  4. Hand off to /ruview-verify for the witness bundle.
Changes

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.

  1. yesterday First seen · 19 lines · 45 tokens per session scan A c8022ff4d393

Subscribe to this mod's changes

ruview-train is a command published in the GitHub repository ruvnet/RuView (92,432 stars, last pushed today), licensed MIT. It adds 45 tokens to every session and 528 once invoked, about $0.0002 per session on Opus 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.

Data and AIdenseposeesp32monitoringrfwifiwifi-security
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