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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.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
| 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.SOC 직업 분류 기준
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.
