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 agentmods add skills/ruvnet/ruview/ruview-quickstartnpx skills add ruvnet/RuView --skill ruview-quickstartgit 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/ruview-quickstart)<a href="https://agentmods.dev/skills/ruvnet/ruview/ruview-quickstart"><img src="https://agentmods.dev/badge/skills/ruvnet/ruview/ruview-quickstart.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 | $0.00062 | $0.00869 |
| Opus 5 | $0.00031 | $0.00434 |
| Sonnet 5 | $0.00012 | $0.00174 |
| Haiku 4.5 | $0.00006 | $0.00087 |
Grade A, and why
ruview-quickstart 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.
How it starts
The opening of the file, as written. The whole thing — 78 lines — stays where its author put it; the contents beside it link to each section on GitHub.
RuView Quickstart
Get a newcomer from zero to a running RuView sensing dashboard. Three tiers, pick the one that matches the hardware on hand.
Tier 0 — Docker, no hardware (2 minutes)
docker pull ruvnet/wifi-densepose:latest
docker run -p 3000:3000 ruvnet/wifi-densepose:latest
# open http://localhost:3000 — simulated CSI, full UI
Use this to demo the dashboard, explore the API, or develop UI without a sensor.
Tier 1 — Build the repo from source
# Rust workspace (1,400+ tests, ~2 min)
cd v2
cargo test --workspace --no-default-features
# Single-crate sanity check (no GPU)
cargo check -p wifi-densepose-train --no-default-features
# Python proof (deterministic SHA-256 pipeline check)
cd ..
python archive/v1/data/proof/verify.py # must print VERDICT: PASS
If verify.py fails on a hash mismatch after a numpy/scipy bump:
python archive/v1/data/proof/verify.py --generate-hash
python archive/v1/data/proof/verify.py
Tier 2 — Live sensing with an ESP32-S3 ($9)
This is the real thing. Hand off to the ruview-hardware-setup skill for the flash/provision/monitor loop, then:
# Lightweight sensing server (consumes the ESP32 UDP CSI stream)
cd v2
cargo run -p wifi-densepose-sensing-server
# Live RF room scan / SNN learning helpers:
node ../scripts/rf-scan.js --port 5006
node ../scripts/snn-csi-processor.js --port 5006
What to know before you start
- ESP32-C3 and the original ESP32 are NOT supported — single-core, can't run the CSI DSP pipeline. Use ESP32-S3 (8MB or 4MB) or ESP32-C6.
- A single ESP32 has limited spatial resolution — 2+ nodes (or add a Cognitum Seed) for good results.
- Camera-free pose accuracy is limited (~84s to train, modest PCK). For 92.9% PCK@20 use camera-supervised training (see
ruview-model-trainingskill, ADR-079). - No cloud, no internet, no cameras required — everything runs on edge hardware.
Next steps to suggest
| Goal | Skill / command |
|---|---|
| Flash & provision an ESP32 node | ruview-hardware-setup · /ruview-flash · /ruview-provision |
| Tune channels / MAC filter / edge modules | ruview-configure |
| Run a sensing application (presence, vitals, pose, sleep, MAT) | ruview-applications · /ruview-app |
| Train a pose / sensing model | ruview-model-training · /ruview-train |
| Multistatic mesh, tomography, cross-viewpoint fusion | ruview-advanced-sensing · /ruview-advanced |
| Verify the build + generate a witness bundle | ruview-verify · /ruview-verify |
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.
- yesterday First seen · 78 lines · 62 tokens per session scan A a9f0856709f9
ruview-quickstart is a skill published in the GitHub repository ruvnet/RuView (92,510 stars, last pushed today), licensed MIT. It adds 62 tokens to every session and 869 once invoked, about $0.0003 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.
Other skills, from other repositories
meraki-wireless-ops
Cisco Meraki wireless (read-only) — SSID configuration, RF profiles, Air Marshal, channel utilization, signal quality, client connectivity events via Cisco's official Meraki MCP. Use when inspecting Meraki SSIDs, auditing RF configuration, or investigating WiFi connectivity.
esp32-arch-review
Review ESP32 FastLED firmware architecture for RTOS safety, DMA correctness, LED driver patterns, memory management, and peripheral safety. Use before merging significant driver changes, new platform ports, or when auditing existing ESP32 FastLED code.
esp32-log-triage
Parse and classify ESP32 serial log output to identify FastLED-related errors, RMT/I2S/SPI driver faults, timing violations, RTOS issues, and crash signatures. Use when debugging unexpected device behavior, boot failures, or LED output problems on ESP32.
embedded-debug
Firmware crash analysis, stack trace decoder, and register dump interpreter for ESP32/ARM/AVR platforms. Use when debugging device crashes, panics, guru meditation errors, hard faults, or analyzing core dumps.
platform-port
Guide porting FastLED to new MCU platforms, including int.h types, clockless drivers, SPI implementations, and platform detection. Use when adding support for a new microcontroller family or board.
fix-board
Automatically diagnose and fix PlatformIO board upload/monitor issues. Runs three-phase device workflow (Compile, Upload, Monitor) and applies fixes.