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Zero-to-sensing path picker for RuView (WiFi-DensePose) — pick docker-demo, repo-build, or live-esp32 and run the next concrete step.
Install with Codex or Claude Copy this prompt, paste it into Codex, Claude, or another assistant, and let it review the skill page and install it for you.
Based on SOC occupation classification
Run the ADR-151 per-room calibration pipeline — baseline → enroll → extract → train → a bank of small specialists (presence/posture/breathing/heartbeat/restlessness/anomaly).
Build, flash, and provision an ESP32-S3/C6 CSI node for RuView — firmware variant choice, ESP-IDF Windows-subprocess flow, NVS/WiFi/channel/MAC-filter overrides.
Train/evaluate WiFi pose models honestly — camera-supervised (MediaPipe + CSI) and camera-free (WiFlow), always checked against the mean-pose baseline before any PCK is quoted.
Prove a RuView result is real — run the deterministic SHA-256 proof and the witness bundle (ADR-028), and lint any claim for MEASURED-vs-CLAIMED honesty.
Explore and prototype rvAgent + RVF integration for RuView agentic flows. Use when working on cross-cog coordination, operator-facing agents reading BFLD / pose / vitals events live, or persisting agent state alongside sensing data in the same RVF container.
Advanced RuView capabilities — RuvSense multistatic sensing (attention-weighted fusion, geometric diversity, persistent field model), cross-viewpoint fusion across multiple nodes, RF tomography (ISTA L1 solver, voxel grids), longitudinal biomechanics drift, pre-movement intention signals, adversarial signal detection, and multistatic mesh security hardening. Use for research-grade or multi-node deployments.
| name | onboard |
| description | Zero-to-sensing path picker for RuView (WiFi-DensePose) — pick docker-demo, repo-build, or live-esp32 and run the next concrete step. |
Get a newcomer from nothing to a working RuView setup. First fact to set: WiFi
sensing infers coarse pose/presence/breathing from Channel State Information — it
is not a camera, and any accuracy number must be MEASURED against a baseline
(use the verify skill / ruview.claim_check tool). Never present WiFi output as
camera-grade.
Run ruview.onboard {path} or decide from:
docker run -p 8000:8000 ruvnet/wifi-densepose → open http://localhost:8000.
Use to see what it looks like.cd v2 && cargo test --workspace --no-default-features
(1,031+ tests pass), then cargo run -p wifi-densepose-cli -- --help.provision-node skill), point it at
the sensing-server, then calibrate-room. This is the only path that senses a real room.ruview.claim_check on any
report before you quote a number.