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Agent skill

Train Pose

by ruvnet in ruvnet/RuView

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.

MITAuto-check passedDevelopment

Install Train Pose

skills CLI
$ npx skills add ruvnet/RuView --skill train-pose -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install ruvnet/RuView train-pose --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/ruvnet/RuView.git skills-src && mkdir -p .claude/skills && cp -r skills-src/harness/ruview/.claude/skills/train-pose .claude/skills/train-pose && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
train-pose
GitHub stars
97k
Token cost
~504 tokens
SKILL.md length
196 words
Files
1
Skills in repo
24
Repo updated
First seen
Licence
MIT

At a glance

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.

  • Works in 4 steps: Run the mean-pose baseline on the same… → Report (model − baseline) in pp, with… → ruview_claim_check the writeup — it… → …
  • Tasks that involve Architecture decision records
  • SKILL.md covers The non-negotiable: mean-pose…, Paths, Run it through the harness… and Before you publish a number
  • Calls npx

What it does

Train Pose is an agent skill from ruvnet/RuView. 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.

Its SKILL.md is about 500 tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Development, covering Architecture decision records. The repository describes itself as: π RuView turns commodity WiFi signals into real-time spatial intelligence, vital sign monitoring, and presence detection — all without a single pixel of video. The licence is MIT.

When your agent uses it

  • Tasks that involve Architecture decision records

Example prompts

  • “/train-pose”

Requirements

  • Node.js

Workflow steps

4 steps, taken from the first numbered list in SKILL.md.

  1. Run the mean-pose baseline on the same split.
  2. Report (model − baseline) in pp, with the split definition (chronological /
  3. ruview_claim_check the writeup — it flags any untagged or 100%/perfect claim.
  4. If it's a benchmark vs SOTA, tag MEASURED-EQUIVALENT only with the reproducer.

What it can do on your machine

Read from SKILL.md and the folder at commit 0ef6b96. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • npx

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use npx, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Train Pose loads about 504 tokens when it runs. Until then it costs about 47 tokens; SKILL.md has 196 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~47
When it runs · the whole SKILL.md, loaded when a task matches
~504

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from ruvnet/RuView at commit 0ef6b96, republished under its MIT licence (© ruvnet). 196 words, ~504 tokens.

Download SKILL.mdSave it as .claude/skills/train-pose/SKILL.md (or your agent's skills folder).
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).

Run it through the harness (ADR-371)

npx @ruvnet/ruview train-plan --mode pose-smoke            # command, cwd, outputs; runs nothing
npx @ruvnet/ruview train --mode pose-smoke --confirm       # SYNTHETIC pipeline smoke (libtorch 2.11 for tch 0.24)
npx @ruvnet/ruview train --mode pose --data-dir <in-repo MM-Fi dir> --confirm
npx @ruvnet/ruview train-gate --file eval-report.json      # mean-pose baseline + leakage gate

The gate returns the only acceptable claim sentence. Quote nothing it fails.

Before you publish a number

  1. Run the mean-pose baseline on the same split.
  2. Report (model − baseline) in pp, with the split definition (chronological / blocked-gap / grouped-bucket; no leakage).
  3. ruview_claim_check the writeup — it flags any untagged or 100%/perfect claim.
  4. If it's a benchmark vs SOTA, tag MEASURED-EQUIVALENT only with the reproducer.

© ruvnet, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in harness/ruview/.claude/skills/train-pose of ruvnet/RuView.

Open the folder on GitHubat commit 0ef6b96

Compare with similar skills

Train Pose next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Train Pose compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Train Pose this skillruvnet/RuView97k—~504Automated safety check: PassMIT
PR Design DocOpenHands/OpenHands90k—~2.4kAutomated safety check: PassMIT
Cto AdvisorIbrahim-3d/orchestrator-supaconductor3804 repos~2.4kAutomated safety check: PassMIT
Improve Codebase Architectureywwynm/EverythingDone14415 repos~1.3kAutomated safety check: PassGPL-3.0
Domain Modelingbrim-borium/spotify_sdk1665 repos~806Automated safety check: PassApache-2.0
Design Doc MermaidSpillwaveSolutions/design-doc-mermaid1751 repos~5.6kAutomated safety check: PassNone

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More from ruvnet/RuView

All 24 skills in this repo

  • RuView Advanced Sensing

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    Reference for RuView's research-grade WiFi sensing features: multistatic fusion, cross-viewpoint geometry, persistent field models, RF tomography, intention signals and mesh security.

    97k GitHub stars~1.2k tokensUpdated today
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  • RuView Sensing Applications

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    Runs RuView's WiFi sensing applications: presence, vital signs, activity and fall detection, pose estimation, sleep monitoring and environment mapping.

    97k GitHub stars~1.1k tokensUpdated today
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  • RuView CLI, API and WASM

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    Covers the RuView `wifi-densepose` command line binary, its Axum REST API and the WebAssembly builds for browsers and ESP32, for embedding or scripting RuView.

    97k GitHub stars~1.2k tokensUpdated today
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  • RuView Deployment Configuration

    ruvnet/RuView

    Tunes a deployed RuView WiFi-sensing system without changing code: firmware sdkconfig variants, NVS provisioning over serial, channel and MAC filtering, edge processing tiers and mesh slotting.

    97k GitHub stars~1.7k tokensUpdated today
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  • Agent-Browser Automation

    ruvnet/RuView

    Drives a web browser through the agent-browser CLI, using compact accessibility snapshots with element refs in place of the full DOM to keep context small.

    97k GitHub starsUsed in 4 repos~1.3k tokens
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  • RuView Hardware Setup

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    Brings a RuView CSI sensing node online by building ESP32-S3 or ESP32-C6 firmware, flashing the board, provisioning WiFi and checking the serial output.

    97k GitHub stars~1.8k tokensUpdated today
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Categories

Questions about Train Pose

What does Train Pose 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. Train Pose is an agent skill from ruvnet/RuView. 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.

When should I use Train Pose?

Train Pose fits situations like: tasks that involve Architecture decision records.

How do I install Train Pose in Claude Code?

Run `npx skills add ruvnet/RuView --skill train-pose -a claude-code`. Or copy the skill folder (harness/ruview/.claude/skills/train-pose in ruvnet/RuView) into .claude/skills/train-pose in your project. Claude Code loads it when a task matches its description.

How do I install Train Pose in Codex?

Run `npx skills add ruvnet/RuView --skill train-pose -a codex`. Or copy the skill folder (harness/ruview/.claude/skills/train-pose in ruvnet/RuView) into .agents/skills/train-pose in your project. Codex loads it when a task matches its description.

Can I use Train Pose in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add ruvnet/RuView --skill train-pose -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/train-pose, .gemini/skills/train-pose, .github/skills/train-pose and .opencode/skills/train-pose in your project.

What does Train Pose need to run?

Going by SKILL.md and its folder, Train Pose needs the command-line tools its instructions call (npx). Our summary lists: Node.js.

Does Train Pose access the network?

SKILL.md contains no URLs. Its commands use npx, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Train Pose safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Train Pose use?

Train Pose is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Train Pose use?

About 504 tokens (SKILL.md is roughly 2k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Train Pose?

Skills that share tags, products or a category with Train Pose: PR Design Doc (OpenHands/OpenHands, 90k stars), Cto Advisor (Ibrahim-3d/orchestrator-supaconductor, 380 stars), Improve Codebase Architecture (ywwynm/EverythingDone, 144 stars) and Domain Modeling (brim-borium/spotify_sdk, 166 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Train Pose?

ruvnet (a GitHub user) maintains it in ruvnet/RuView, which has 96,741 GitHub stars. The repository holds 24 skills in this directory. The repository was last updated on October 7, 2026.

Source: ruvnet/RuView on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.