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
This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.
/plugin marketplace add ruvnet/RuView/plugin install 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/ruview-advanced-sensing)<a href="https://agentmods.dev/skills/ruvnet/ruview/ruview-advanced-sensing"><img src="https://agentmods.dev/badge/skills/ruvnet/ruview/ruview-advanced-sensing.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 | $0.00084 | $0.01153 |
| Opus 5 | $0.00042 | $0.00576 |
| Sonnet 5 | $0.00017 | $0.00231 |
| Haiku 4.5 | $0.00008 | $0.00115 |
Grade A, and why
ruview-advanced-sensing 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 2d 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 — 77 lines — stays where its author put it; the contents beside it link to each section on GitHub.
RuView Advanced Sensing
The deep end: multistatic mesh, tomography, persistent field models, and the security model that protects them. Most of this lives in wifi-densepose-signal/src/ruvsense/ (14 modules) and wifi-densepose-ruvector/src/viewpoint/ (5 modules).
RuvSense multistatic mode (ADR-029)
Treat every WiFi link in range — including neighbours' APs — as a bistatic radar pair, then fuse them.
Module (signal/src/ruvsense/) |
Purpose |
|---|---|
multiband.rs |
Multi-band CSI frame fusion, cross-channel coherence |
phase_align.rs |
Iterative LO phase-offset estimation, circular mean |
multistatic.rs |
Attention-weighted fusion, geometric diversity |
coherence.rs / coherence_gate.rs |
Z-score coherence scoring; Accept / PredictOnly / Reject / Recalibrate gate decisions |
pose_tracker.rs |
17-keypoint Kalman tracker with AETHER re-ID embeddings |
field_model.rs |
SVD room eigenstructure, perturbation extraction |
tomography.rs |
RF tomography, ISTA L1 solver, voxel grid |
longitudinal.rs |
Welford stats, biomechanics drift detection |
intention.rs |
Pre-movement lead signals (200–500 ms ahead) |
cross_room.rs |
Environment fingerprinting, transition graph |
gesture.rs |
DTW template-matching gesture classifier |
adversarial.rs |
Physically-impossible-signal detection, multi-link consistency |
Cross-viewpoint fusion (ADR-016 viewpoint module)
Combine 2+ nodes geometrically — more nodes, more independent looks, tighter localization.
Module (ruvector/src/viewpoint/) |
Purpose |
|---|---|
attention.rs |
CrossViewpointAttention, GeometricBias, softmax with G_bias |
geometry.rs |
GeometricDiversityIndex, Cramér–Rao bounds, Fisher Information |
coherence.rs |
Phase-phasor coherence, hysteresis gate |
fusion.rs |
MultistaticArray aggregate root, domain events |
Host-side helpers to explore the geometry before deploying: node scripts/mesh-graph-transformer.js, node scripts/passive-radar.js, node scripts/deep-scan.js.
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.
- 2d ago First seen · 77 lines · 84 tokens per session scan A ef500a308c5a
ruview-advanced-sensing is a skill published in the GitHub repository ruvnet/RuView (92,510 stars, last pushed today), licensed MIT. It adds 84 tokens to every session and 1,153 once invoked, about $0.0004 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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