# game-analytics-platform-computer-vision

Guides the setup of a local fitness platform using computer vision, a Spring Boot backend, and a React dashboard for webcam workout tracking.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · 1600 installs · ★ 5
- Author: reason-machines
- License: LicenseRef-Unknown · policy=link-only · description only; the file stays with the author
- Source: https://github.com/reason-machines/data-skills/tree/HEAD/skills/game-analytics-platform-computer-vision/SKILL.md
- Updated: 2026-10-09
- HTML: https://affpapa.org/en/nevibecode/skills/game-analytics-platform-computer-vision

## What it does

This skill walks through building a three-tier workout tracker driven by webcam video. Python scripts handle local processing, pairing YOLO v8 with MediaPipe to validate posture across 16 exercises. The vision engine records movement metrics directly to CSV files, while pyttsx3 handles spoken feedback.

A Java 17 Spring Boot backend controls the lifecycle of these Python sub-processes via REST endpoints and serves a React frontend at `http://localhost:8080`. Without Java 17 and Python 3.10+ configured in your path, the build pipeline fails before any video capture begins.

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add reason-machines/data-skills --skill game-analytics-platform-computer-vision -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/game-analytics-platform-computer-vision/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/game-analytics-platform-computer-vision/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add reason-machines/data-skills --skill game-analytics-platform-computer-vision -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/game-analytics-platform-computer-vision/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add reason-machines/data-skills --skill game-analytics-platform-computer-vision -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/game-analytics-platform-computer-vision/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install reason-machines/data-skills game-analytics-platform-computer-vision
```

2. **Manually: put the files in place**
- File: `.github/skills/game-analytics-platform-computer-vision/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add reason-machines/data-skills --skill game-analytics-platform-computer-vision -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/reason-machines/data-skills --path skills/game-analytics-platform-computer-vision
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/game-analytics-platform-computer-vision/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add reason-machines/data-skills --skill game-analytics-platform-computer-vision -a opencode
```

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/reason-machines/data-skills/tree/HEAD/skills/game-analytics-platform-computer-vision/SKILL.md

## When to use it
- combine MediaPipe skeletal estimation with YOLO detection in Python
- manage vision sub-processes through a Spring Boot REST API
- trigger automated voice coaching during workouts via pyttsx3
- export exercise repetition data into CSV files

## Example prompts
- Help me configure the local fitness tracking game platform
- Combine YOLO tracking with MediaPipe pose estimation for webcam workouts
- Configure a Spring Boot service to manage vision sub-processes

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From the same set: [reason-machines/data-skills](https://affpapa.org/en/nevibecode/by/reason-machines/data-skills.md) — 74 cards

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