RuView is a WiFi sensing platform that uses disturbances in radio signals, captured by low-cost ESP32 sensors, to detect presence, movement, breathing, and heart rate without cameras or wearables. It is intended for spatial monitoring and smart-home integrations. The catalogue add-ons support workflows for operating and integrating RuView.
Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/ruvnet/RuViewnpx agentmods add skills/ruvnet/ruview/ruview-model-trainingWrote 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/skills/ruvnet/ruview/ruview-model-training)<a href="https://agentmods.dev/skills/ruvnet/ruview/ruview-model-training"><img src="https://agentmods.dev/badge/skills/ruvnet/ruview/ruview-model-training/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/skills/ruvnet/ruview/ruview-model-training"><img src="https://agentmods.dev/badge/skills/ruvnet/ruview/ruview-model-training.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Snyk pass
- NVIDIA SkillSpector pass
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 | $0.00107 | $0.01475 |
| Opus 5.5 | $0.00043 | $0.00590 |
| Sonnet 5.5 | $0.00021 | $0.00295 |
| Haiku 4.5 | $0.00011 | $0.00147 |
Grade A, and why
ruview-model-training 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 2mo 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.
How it starts
The opening of the file, as written. The whole thing — 123 lines — stays where its author put it; the contents beside it link to each section on GitHub.
RuView Model Training
RuView trains several kinds of model. Pick the track that matches the goal; all of them run on a laptop, with an optional GPU path.
Track A — Camera-free pose (WiFlow), no cameras, no labels
Trains 17-keypoint pose from 10 sensor signals. Fast, fully unsupervised, modest accuracy.
cd v2
# Pretrain on raw CSI (contrastive)
cargo run -p wifi-densepose-sensing-server -- --pretrain --dataset data/csi/ --pretrain-epochs 50
# Train pose head, save an RVF artifact
cargo run -p wifi-densepose-sensing-server -- --train --dataset data/mmfi/ --epochs 100 --save-rvf model.rvf
~84 s on an M4 Pro. Benchmarks: node scripts/benchmark-wiflow.js, eval: node scripts/eval-wiflow.js.
Track B — Camera-supervised pose (ADR-079) → 92.9% PCK@20
Uses a webcam + MediaPipe as ground truth, paired with ESP32 CSI. ~19 min on a laptop.
# 1. Collect paired data (camera + CSI)
python scripts/collect-ground-truth.py # MediaPipe pose landmarks
python scripts/collect-training-data.py # CSI capture, time-synced
node scripts/align-ground-truth.js # align camera ↔ CSI timestamps
# 2. Train (the camera-supervised path through the sensing-server / train crate)
cd v2
cargo run -p wifi-densepose-sensing-server -- --train --dataset data/paired/ --epochs <N> --save-rvf model.rvf
# 3. Evaluate
cd .. && node scripts/eval-wiflow.js # reports PCK@20
Requires data/pose_landmarker_lite.task (MediaPipe model). See docs/adr/ADR-079-camera-ground-truth-training.md.
Track C — RuVector contrastive embeddings (AETHER, ADR-024)
CSI subcarrier amplitude/phase → embeddings for re-ID and retrieval (171K emb/s on M4 Pro). Driven by wifi-densepose-train + wifi-densepose-ruvector (RuVector v2.0.4). Spectrogram embeddings: ADR-076.
cd v2
cargo check -p wifi-densepose-train --no-default-features # sanity
cargo run -p wifi-densepose-sensing-server -- --model model.rvf --embed
cargo run -p wifi-densepose-sensing-server -- --model model.rvf --build-index env
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.
- 2mo ago First seen · 123 lines · 107 tokens per session scan A 2cf816958eff
ruview-model-training is a skill published in the GitHub repository ruvnet/RuView (102,265 stars, last pushed today), licensed MIT. It adds 107 tokens to every session and 1,475 once invoked, about $0.0004 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.
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