# RuView Quickstart

Gets a new user to a live RuView sensing dashboard via Docker demo or repo build.

- Repository: https://github.com/ruvnet/RuView (101983 stars)
- Category: Engineering
- License: MIT
- Last updated: 2026-11-01
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add ruvnet/RuView --skill ruview-quickstart -g -a claude-code -y`
Codex: `npx skills add ruvnet/RuView --skill ruview-quickstart -g -a codex -y`
Cursor: `npx skills add ruvnet/RuView --skill ruview-quickstart -g -a cursor -y`
Gemini CLI: `npx skills add ruvnet/RuView --skill ruview-quickstart -g -a gemini-cli -y`
Claude Code plugin marketplace: `/plugin marketplace add ruvnet/RuView`

## Description

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".

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# 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

The rest: https://github.com/ruvnet/RuView/blob/HEAD/plugins/ruview/skills/ruview-quickstart/SKILL.md