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rUv
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A host-portable RuView agent harness minted via MetaHarness and hardened per ADR-182. Published as @ruvnet/ruview@0.1.0 (bare `ruview` blocked by npm's typosquat filter → scoped fallback). What it does: - 6 fail-closed `ruview.*` tools (onboard, claim_check, verify, node_monitor, calibrate, node_flash) exposed as CLI verbs + a dependency-free MCP stdio server. - The "prove everything" rule made executable: `ruview.claim_check` flags untagged accuracy claims and the retracted "100%" framing. - 5 host-neutral skills (onboard/provision-node/calibrate-room/ train-pose/verify) + bundled .claude/ config + provenance manifest. Validated: 17/17 unit tests, live MCP handshake, `ruview.verify` ran the real verify.py to VERDICT: PASS, clean `npx @ruvnet/ruview` from registry. Packs to 16.7 kB / 21 files; kernel+host are optionalDependencies so the operator tools install lightweight. README: documented as the portable, multi-host companion to the in-repo plugins/ruview/ Claude Code plugin (not a replacement).
34 lines
1.5 KiB
Markdown
34 lines
1.5 KiB
Markdown
---
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name: train-pose
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description: 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.
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---
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# train-pose
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Build a CSI→pose model without overstating it. The project has a **retracted 92.9%/100%**
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history — the discipline below exists so it never recurs.
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## The non-negotiable: mean-pose baseline first
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A pose model that always predicts the dataset's *mean pose* already scores ~50% PCK.
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**Quote PCK only as a delta over that baseline**, on a held-out split with no subject
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or temporal leakage. Example honest result (ADR-181):
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> Held-out PCK@20 **59.5%** vs a 50% mean-pose baseline = **+9.4 pp real signal** — MEASURED.
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## Paths
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- **camera-supervised** (ADR-079) — MediaPipe Pose labels the camera frame; paired CSI
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trains the net. Train/infer in one camera frame so the skeleton aligns.
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- **camera-free** (WiFlow, ADR-152) — no camera at inference; geometry-conditioned.
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- **in-browser** (ADR-181) — WebGPU/WASM trainer; the active backend is shown as a badge
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(honest about what's executing).
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## Before you publish a number
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1. Run the mean-pose baseline on the same split.
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2. Report `(model − baseline)` in pp, with the split definition (chronological /
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blocked-gap / grouped-bucket; no leakage).
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3. `ruview.claim_check` the writeup — it flags any untagged or 100%/perfect claim.
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4. If it's a benchmark vs SOTA, tag MEASURED-EQUIVALENT only with the reproducer.
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