Agent Skills
› ruvnet/RuView
› train-pose
train-pose
GitHub规范WiFi姿态估计模型的训练与评估流程，强制先运行均值基线以诚实报告性能提升，防止夸大结果。支持相机监督、无相机及浏览器端多种路径。
Trigger Scenarios
训练或评估CSI到姿态的模型
发布或报告模型PCK性能指标
Install
npx skills add ruvnet/RuView --skill train-pose -g -y
SKILL.md
Frontmatter
{
"name": "train-pose",
"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."
}
train-pose
Build a CSI→pose model without overstating it. The project has a retracted 92.9%/100% history — the discipline below exists so it never recurs.
The non-negotiable: mean-pose baseline first
A pose model that always predicts the dataset's mean pose already scores ~50% PCK. Quote PCK only as a delta over that baseline, on a held-out split with no subject or temporal leakage. Example honest result (ADR-181):
Held-out PCK@20 59.5% vs a 50% mean-pose baseline = +9.4 pp real signal — MEASURED.
Paths
- camera-supervised (ADR-079) — MediaPipe Pose labels the camera frame; paired CSI trains the net. Train/infer in one camera frame so the skeleton aligns.
- camera-free (WiFlow, ADR-152) — no camera at inference; geometry-conditioned.
- in-browser (ADR-181) — WebGPU/WASM trainer; the active backend is shown as a badge (honest about what's executing).
Before you publish a number
- Run the mean-pose baseline on the same split.
- Report
(model − baseline)in pp, with the split definition (chronological / blocked-gap / grouped-bucket; no leakage). ruview_claim_checkthe writeup — it flags any untagged or 100%/perfect claim.- If it's a benchmark vs SOTA, tag MEASURED-EQUIVALENT only with the reproducer.
Version History
- a3b6e1d Current 2026-08-20 13:11
