mirror of https://github.com/ruvnet/RuView synced 2026-06-24 12:43:18 +00:00

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ruv 8ff7c2c35a feat(plugins): RuView Claude Code + Codex marketplace plugin

Add `plugins/ruview` — an end-to-end toolkit for working with RuView
(WiFi-DensePose) from Claude Code, mirrored as Codex prompts.

Marketplace: `plugins/.claude-plugin/marketplace.json` (one plugin, `ruview`).

Skills (9): ruview-quickstart, ruview-hardware-setup, ruview-configure,
ruview-applications, ruview-model-training, ruview-advanced-sensing,
ruview-cli-api, ruview-mmwave, ruview-verify — shell-first (cargo / python /
idf.py / docker / node), no claude-flow MCP dependency.

Commands (7): /ruview-start, /ruview-flash, /ruview-provision, /ruview-app,
/ruview-train, /ruview-advanced, /ruview-verify.

Agents (3): ruview-onboarding-guide, ruview-config-engineer,
ruview-training-engineer.

Codex mirror: codex/AGENTS.md + codex/README.md + codex/prompts/*.md (full
command parity, enforced by scripts/smoke.sh).

Docs: docs/adrs/0001-ruview-plugin-contract.md (Proposed). Verification:
scripts/smoke.sh (13 structural checks). Provisioning docs reflect the full
`provision.py` flag set (TDM mesh, edge tiers, vitals, hop channels, Cognitum
Seed, swarm intervals) and the issue #391 NVS-namespace-replace gotcha.

Verified: `claude plugin validate` (plugin + marketplace), loads via
`claude --plugin-dir`, smoke 13/13, and confirmed against an attached ESP32-S3
on COM8 running the RuView CSI firmware (live adaptive_ctrl + csi_collector
serial output).

Co-Authored-By: claude-flow <ruv@ruv.net>

2026-05-11 17:39:16 -04:00

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name, description, allowed-tools

name	description	allowed-tools
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".	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-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)

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