How do I install the Ruview Quickstart skill?

Run npx skillmds@latest add ruvnet/ruview-quickstart in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Ruview Quickstart safe to use?

This skill has not completed SkillMD's automated safety review yet. Capability flags: makes network calls. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Ruview Quickstart?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Ruview Quickstart free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Ruview Quickstart?

ruvnet (@ruvnet) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Ruview Quickstart by ruvnet — Agent Skill

Name: Ruview Quickstart
Rating: 4.2 (5 reviews)
Author: ruvnet

Ruview Quickstart

Onboarding and first-run for RuView (WiFi-DensePose) — Docker demo with simulated data, repo build, and the fastest path to a live sensing dashboard. Use when someone is new to RuView or wants the shortest path to "it works on my machine".

ruvnet 2262fd1 3.3 KB Updated Sep 9, 2026

File contents

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-training skill, 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`

Reference

README.md — feature matrix, hardware table, install options
docs/user-guide.md, docs/wifi-mat-user-guide.md, docs/build-guide.md, docs/TROUBLESHOOTING.md
docs/tutorials/, examples/ — runnable examples (environment, medical, sleep, stress, ruview_live.py)

Source: ruvnet/RuView → plugins/ruview/skills/ruview-quickstart/SKILL.md

---name: ruview-quickstartdescription: Onboarding and first-run for RuView (WiFi-DensePose) — Docker demo with simulated data, repo build, and the fastest path to a live sensing dashboard. Use when someone is new to RuView or wants the shortest path to "it works on my machine".--- # 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) ```bash 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 ```bash # 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: ```bash 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: ```bash # 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-training` skill, 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` | ## Reference - `README.md` — feature matrix, hardware table, install options - `docs/user-guide.md`, `docs/wifi-mat-user-guide.md`, `docs/build-guide.md`, `docs/TROUBLESHOOTING.md` - `docs/tutorials/`, `examples/` — runnable examples (environment, medical, sleep, stress, `ruview_live.py`) --- **Source:** [`ruvnet/RuView`](https://github.com/ruvnet/RuView) → `plugins/ruview/skills/ruview-quickstart/SKILL.md`

Frequently asked questions