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https://github.com/ruvnet/RuView
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ruv
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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>
14 lines
1.9 KiB
Markdown
14 lines
1.9 KiB
Markdown
# /ruview-app — run a RuView sensing application
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Run a RuView application. Which one: `$ARGUMENTS` (one of `presence`, `vitals`, `pose`, `sleep`, `environment`, `mat`, `pointcloud`, or a novel-RF app name; if empty, show the catalogue and ask).
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- **presence / vitals / pose / environment** → `cd v2 && cargo run -p wifi-densepose-sensing-server` against a live ESP32 sink, or the Docker demo (`docker run -p 3000:3000 ruvnet/wifi-densepose:latest`) for simulated CSI. For environment also `-- --model model.rvf --build-index env`. Vitals: breathing 6–30 BPM (bandpass 0.1–0.5 Hz), heart rate 40–120 BPM (bandpass 0.8–2.0 Hz), `wifi-densepose-vitals` crate (ADR-021). Pose: 17 COCO keypoints via WiFlow (ADR-059 live pipeline) — train for accuracy (`/ruview-train`).
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- **sleep** → `examples/sleep/` + `node scripts/apnea-detector.js` (sleep-stage classification, apnea screening).
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- **mat** (Mass Casualty Assessment — disaster survivor detection) → `wifi-densepose-mat` crate, `docs/wifi-mat-user-guide.md`.
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- **pointcloud** → `python scripts/mmwave_fusion_bridge.py` (camera depth via MiDaS + WiFi CSI + mmWave radar → unified spatial model, ~22 ms, 19K+ pts/frame; ADR-094).
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- **novel RF** → `scripts/passive-radar.js`, `material-classifier.js`, `device-fingerprint.js`, `mincut-person-counter.js`, `gait-analyzer.js` (ADR-077/078).
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No hardware? Fall back to the Docker demo or `python examples/ruview_live.py`. Visualisers: `node scripts/csi-spectrogram.js`, `node scripts/csi-graph-visualizer.js`.
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Help me pick: through-wall → presence/activity (≤5 m depth); stationary subject → vitals/sleep; need skeletons → pose (train it); search & rescue → MAT; best spatial accuracy → 2+ ESP32 nodes + cross-viewpoint fusion (`v2/crates/wifi-densepose-ruvector/src/viewpoint/`), optionally + Cognitum Seed. Examples: `examples/{environment,medical,sleep,stress,happiness-vector}/`.
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