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 calibrate-roomgit 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/calibrate-room)<a href="https://agentmods.dev/skills/ruvnet/ruview/calibrate-room"><img src="https://agentmods.dev/badge/skills/ruvnet/ruview/calibrate-room.svg" alt="Measured on agentmods" 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 | $0.00041 | $0.00334 |
| Opus 5 | $0.00020 | $0.00167 |
| Sonnet 5 | $0.00008 | $0.00067 |
| Haiku 4.5 | $0.00004 | $0.00033 |
Grade A, and why
calibrate-room 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 3d 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
calibrate-room
Turn a provisioned node + sensing-server into a working room model. Pure-Rust,
edge-deployable (ADR-151). Use the ruview_calibrate tool (installed
wifi-densepose binary, else cargo run -p wifi-densepose-cli).
Sequence
- baseline — capture the empty room (Welford amplitude + von Mises phase). Leave
the room empty.
ruview_calibrate {step: "baseline"} - enroll — record the occupant(s) doing the target activities.
ruview_calibrate {step: "enroll"} - train-room — train the bank of small specialists from baseline + enrollment.
ruview_calibrate {step: "train-room"} - room-watch — live presence/posture/breathing from the trained room.
ruview_calibrate {step: "room-watch"}(or theroom-watchskill)
Honesty
The specialists are calibrated to this room; cross-room transfer is a separate
problem (LoRA recalibration, ADR-079 P9). Report which room a number came from, and
tag presence/vitals accuracy MEASURED only with a held-out check — run
ruview_claim_check on the writeup.
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.
- 3d ago First seen · 30 lines · 41 tokens per session scan A 4b29c7c331f4
calibrate-room is a skill published in the GitHub repository ruvnet/RuView (92,565 stars, last pushed yesterday), licensed MIT. It adds 41 tokens to every session and 334 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.
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