Ruview Quickstart

CommunityPopular

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".

Overview

Publisher	ruvnet
Repository	`RuView`
Skill name	`ruview-quickstart`
Stars	94.3K
Forks	12.5K
Bundled files	Instructions only
License	MIT
Links	Skill source Repository Homepage

Markdown instructions
A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.
Works with any LLM
AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.
Self-contained
Everything the model needs lives in the instructions — no extra files to sync.
Open source
Published by ruvnet on GitHub. Read the source before you install it.

Installation

Install the Ruview Quickstart AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
Enable the skill in any chat where you want it available.

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills

git clone --depth 1 https://github.com/ruvnet/RuView.git /tmp/RuView
mkdir -p .claude/skills
cp -r /tmp/RuView/plugins/ruview/skills/ruview-quickstart .claude/skills/ruview-quickstart

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Ruview Quickstart in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Ruview Quickstart on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Ruview Quickstart is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

Loaded only when it is needed
The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.
Switch models mid-chat
Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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)

Frequently asked questions

What does the Ruview Quickstart AI skill do?

Why use Ruview Quickstart on TypingMind?

Because you install it once and use it with any model. Ruview Quickstart is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Ruview Quickstart in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/ruvnet/RuView/tree/main/plugins/ruview/skills/ruview-quickstart. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Ruview Quickstart?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Ruview Quickstart?

As many as you like. As long as a model supports skills, you can use Ruview Quickstart with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Ruview Quickstart AI skill free?

Yes. It is published on GitHub by ruvnet under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

RuView Quickstart

Tier 0 — Docker, no hardware (2 minutes)

Tier 1 — Build the repo from source

Tier 2 — Live sensing with an ESP32-S3 ($9)

What to know before you start

Next steps to suggest

Reference

Frequently asked questions

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