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https://github.com/ruvnet/RuView
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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>
78 lines
3.1 KiB
Markdown
78 lines
3.1 KiB
Markdown
---
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name: ruview-quickstart
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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".
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allowed-tools: Bash Read Write Edit Glob Grep
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---
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# RuView Quickstart
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Get a newcomer from zero to a running RuView sensing dashboard. Three tiers, pick the one that matches the hardware on hand.
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## Tier 0 — Docker, no hardware (2 minutes)
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```bash
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docker pull ruvnet/wifi-densepose:latest
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docker run -p 3000:3000 ruvnet/wifi-densepose:latest
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# open http://localhost:3000 — simulated CSI, full UI
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```
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Use this to demo the dashboard, explore the API, or develop UI without a sensor.
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## Tier 1 — Build the repo from source
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```bash
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# Rust workspace (1,400+ tests, ~2 min)
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cd v2
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cargo test --workspace --no-default-features
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# Single-crate sanity check (no GPU)
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cargo check -p wifi-densepose-train --no-default-features
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# Python proof (deterministic SHA-256 pipeline check)
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cd ..
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python archive/v1/data/proof/verify.py # must print VERDICT: PASS
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```
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If `verify.py` fails on a hash mismatch after a numpy/scipy bump:
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```bash
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python archive/v1/data/proof/verify.py --generate-hash
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python archive/v1/data/proof/verify.py
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```
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## Tier 2 — Live sensing with an ESP32-S3 ($9)
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This is the real thing. Hand off to the `ruview-hardware-setup` skill for the flash/provision/monitor loop, then:
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```bash
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# Lightweight sensing server (consumes the ESP32 UDP CSI stream)
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cd v2
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cargo run -p wifi-densepose-sensing-server
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# Live RF room scan / SNN learning helpers:
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node ../scripts/rf-scan.js --port 5006
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node ../scripts/snn-csi-processor.js --port 5006
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```
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## What to know before you start
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- **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.
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- A **single ESP32** has limited spatial resolution — 2+ nodes (or add a Cognitum Seed) for good results.
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- 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).
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- No cloud, no internet, no cameras required — everything runs on edge hardware.
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## Next steps to suggest
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| Goal | Skill / command |
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|------|-----------------|
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| Flash & provision an ESP32 node | `ruview-hardware-setup` · `/ruview-flash` · `/ruview-provision` |
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| Tune channels / MAC filter / edge modules | `ruview-configure` |
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| Run a sensing application (presence, vitals, pose, sleep, MAT) | `ruview-applications` · `/ruview-app` |
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| Train a pose / sensing model | `ruview-model-training` · `/ruview-train` |
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| Multistatic mesh, tomography, cross-viewpoint fusion | `ruview-advanced-sensing` · `/ruview-advanced` |
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| Verify the build + generate a witness bundle | `ruview-verify` · `/ruview-verify` |
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## Reference
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- `README.md` — feature matrix, hardware table, install options
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- `docs/user-guide.md`, `docs/wifi-mat-user-guide.md`, `docs/build-guide.md`, `docs/TROUBLESHOOTING.md`
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- `docs/tutorials/`, `examples/` — runnable examples (environment, medical, sleep, stress, `ruview_live.py`)
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