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
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.
npx skills add ruvnet/RuView --skill onboardgit clone --depth 1 https://github.com/ruvnet/RuViewWrote 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/onboard)<a href="https://agentmods.dev/skills/ruvnet/ruview/onboard"><img src="https://agentmods.dev/badge/skills/ruvnet/ruview/onboard/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/onboard"><img src="https://agentmods.dev/badge/skills/ruvnet/ruview/onboard.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Snyk pass
- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium MCP Rug Pull · line 19 Docker image references without a specific tag (:latest is implicit) or digest (@sha256:...) can be silently replaced by a malicious image.Fix: Pin the image: image:tag or image@sha256:abc123
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 | $0.00039 | $0.00363 |
| Opus 5.5 | $0.00016 | $0.00145 |
| Sonnet 5.5 | $0.00008 | $0.00073 |
| Haiku 4.5 | $0.00004 | $0.00036 |
Grade A, and why
onboard 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.
What it actually says
onboard
Get a newcomer from nothing to a working RuView setup. First fact to set: WiFi
sensing infers coarse pose/presence/breathing from Channel State Information — it
is not a camera, and any accuracy number must be MEASURED against a baseline
(use the verify skill / ruview_claim_check tool). Never present WiFi output as
camera-grade.
Pick a path
Run ruview_onboard {path} or decide from:
- docker-demo — fastest, no hardware. Replays sample CSI into the dashboard.
docker run -p 8000:8000 ruvnet/wifi-densepose→ openhttp://localhost:8000. Use to see what it looks like. - repo-build — for developers.
cd v2 && cargo test --workspace --no-default-features(1,031+ tests pass), thencargo run -p wifi-densepose-cli -- --help. - live-esp32 — a real install. Flash a node (
provision-nodeskill), point it at the sensing-server, thencalibrate-room. This is the only path that senses a real room.
Then
- Live sensing → go to provision-node, then calibrate-room.
- Evaluating a model/claim → go to verify and run
ruview_claim_checkon any report before you quote a number.
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 · 31 lines · 39 tokens per session scan A 97ee71f0aa98
onboard is a skill published in the GitHub repository ruvnet/RuView (98,659 stars, last pushed yesterday), licensed MIT. It adds 39 tokens to every session and 363 once invoked, about $0.0002 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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