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› ruvnet/RuView
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ruview-quickstart
GitHubRuView快速入门指南，提供从Docker模拟数据到源码构建及ESP32硬件部署的三级路径，帮助用户快速搭建WiFi感知仪表板并验证环境。
Trigger Scenarios
新用户首次运行RuView
配置Docker演示环境
从源码构建项目
设置ESP32硬件进行实时感知
Install
npx skills add ruvnet/RuView --skill ruview-quickstart -g -y
SKILL.md
Frontmatter
{
"name": "ruview-quickstart",
"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\".",
"allowed-tools": "Bash Read Write Edit Glob Grep"
}
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 |
Reference
README.md— feature matrix, hardware table, install optionsdocs/user-guide.md,docs/wifi-mat-user-guide.md,docs/build-guide.md,docs/TROUBLESHOOTING.mddocs/tutorials/,examples/— runnable examples (environment, medical, sleep, stress,ruview_live.py)
Version History
- a3b6e1d Current 2026-08-20 13:12
