How do I install the Train Pose skill?

Run npx skillmds add thedixitjain/train-pose in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

What does the Train Pose skill do?

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. It is listed under Coding & Dev Tools on SkillMD.

Is Train Pose safe to use?

This skill has not completed SkillMD's automated safety review yet. Capability flags: makes network calls. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Train Pose?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Train Pose free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Train Pose?

thedixitjain (@thedixitjain) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Train Pose by thedixitjain — Agent Skill

Name: Train Pose
Rating: 4.2 (5 reviews)
Author: thedixitjain

File contents

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_check the writeup — it flags any untagged or 100%/perfect claim.
If it's a benchmark vs SOTA, tag MEASURED-EQUIVALENT only with the reproducer.

Source: ruvnet/RuView → harness/ruview/.claude/skills/train-pose/SKILL.md

Frequently asked questions