Skate — AI-Powered YouTube → Viral Shorts CLI

Turn long-form videos into viral-ready vertical shorts, entirely on your local machine. Free, no API keys, no cloud.

Saw a bunch of paid tools doing this — why pay when you can run it locally? Skate uses faster-whisper for transcription, Ollama for AI ranking, OpenCV for face tracking, and FFmpeg for rendering. Everything runs on your machine.

Requirements

| Tool | Purpose | Check | | ------------------ | -------------------------- | ---------------------------- | | Bun | Runtime & package manager | bun --version | | FFmpeg | Video cutting & processing | ffmpeg -version | | yt-dlp | YouTube downloading | yt-dlp --version | | Ollama | Local LLM for AI ranking | ollama --version | | Python 3 | Whisper & OpenCV scripts | python3 --version | | faster-whisper | Local transcription | installed via setup-python | | OpenCV | Face detection | installed via setup-python |

Recommended Ollama Model

ollama pull llama3.2:3b

Installation

via npm (recommended)

npm install -g @r2hu1_npm/skate
skate setup

from source

git clone https://github.com/r2hu1/skate.git
cd skate
bun install
bun run setup
bun link

Python environment

skate setup

This prompts for a project directory, creates a virtual environment at ~/.skate/venv, and installs faster-whisper, opencv-contrib-python, and numpy. Output and temp dirs are set to <projectDir>/skate/output and <projectDir>/skate/temp.

CLI Usage

skate https://youtube.com/watch?v=abc123    # download + process
skate clip video.mp4                         # local file
skate analyze video.mp4                      # analyze only (skip render)
skate render video.mp4                       # render from cached analysis
skate watch ./videos                         # watch directory for new files
skate setup                                  # install Python deps (whisper + opencv)
skate doctor                                 # check dependencies

# With flags
skate clip video.mp4 --crop=1               # 1:1 square crop for mobile shorts
skate clip video.mp4 --captions=false       # no captions burned in
skate clip video.mp4 --crop=1 --captions=false  # both

Options

| Flag | Description | | --------------- | ------------------------------------------ | | --no-crop | Disable face tracking, use center crop | | --crop=true | Enable face tracking (default) | | --crop=1 | Crop to 1:1 square for mobile shorts | | --captions=false | Disable caption burn-in | | --no-captions | Disable caption burn-in |

Configuration

Config is stored at ~/.skate/config.json and auto-created on first run.

{
  "model": "llama3.2:3b",
  "clips": 10,
  "minLength": 20,
  "maxLength": 90,
  "subtitleStyle": "minimal",
  "projectDir": ".",
  "outputDir": "./skate/output",
  "tempDir": "./skate/temp",
  "cacheDir": "~/.skate/cache",
  "ollamaUrl": "http://localhost:11434",
  "crop": true
}

| Field | Default | Description | | --------------- | ------------------------ | ----------------------------------------------- | | model | llama3.2:3b | Ollama model for ranking | | clips | 10 | Number of clips to produce | | minLength | 20 | Minimum clip length (seconds) | | maxLength | 90 | Maximum clip length (seconds) | | subtitleStyle | minimal | Subtitle style (minimal, tiktok, mrbeast) | | projectDir | . | Base project directory (set via skate setup) | | outputDir | ./skate/output | Output directory (derived from projectDir) | | tempDir | ./skate/temp | Working temp directory (derived from projectDir)| | cacheDir | ~/.skate/cache | Cache directory | | ollamaUrl | http://localhost:11434 | Ollama API URL | | crop | true | Enable face tracking for smart vertical crop |

How It Works

Input (URL or file)
  → Download (yt-dlp)
  → Transcribe (faster-whisper)
  → Chunk transcript into segments
  → Score heuristically (speaking rate, emotion, story, hooks)
  → Auto-start Ollama if not running
  → Rank with local LLM (hook strength, momentum, value)
  → Select best clips (no overlap)
  → Track faces for smart vertical crop
  → Render clips with subtitles burned in
  → Output organized shorts + captions

Pipeline Steps

| Step | Description | | --------------- | ------------------------------------------------------------------ | | Download | Pulls video from YouTube via yt-dlp or uses local file | | Transcribe | Runs faster-whisper for speech-to-text with word-level timestamps | | Chunk | Splits transcript into 20-90 second natural segments | | Score | Heuristic scoring — speaking rate, emotion, story structure, hooks | | Rank | Sends top candidates to Ollama for virality scoring (auto-starts) | | Select | Picks best clips based on combined heuristic + AI scores | | Track Faces | Detects faces per frame via OpenCV for smart vertical crop | | Render | Cuts clips, applies crop, burns in subtitles |

Output Structure

<projectDir>/skate/output/
└── <video-name>/
    ├── clips/
    │   ├── clip-01.mp4
    │   ├── clip-02.mp4
    │   └── clip-03.mp4
    ├── captions/
    │   ├── clip-01.srt
    │   └── clip-02.srt
    └── metadata.json

npm Scripts

| Script | Command | | ---------------------- | --------------------------------------------- | | skate | Run Skate | | bun run dev | Run with watch mode | | bun run typecheck | TypeScript type checking | | bun run setup-python | Create venv and install Python deps |

Caching

Skate caches aggressively at ~/.skate/cache:

• Downloaded video/audio files
• Transcripts
• Face tracking data
• Analysis results

Re-running is fast — only changed steps are re-executed.

Project Structure

skate/
├── scripts/
│   ├── face_detect.py           # OpenCV face detection
│   ├── whisper_transcribe.py    # faster-whisper transcription
│   └── requirements.txt         # Python dependencies
├── src/
│   ├── commands/
│   │   ├── clip.ts              # Process local video
│   │   ├── analyze.ts           # Analysis only pipeline
│   │   ├── render.ts            # Render from cached analysis
│   │   ├── watch.ts             # Watch directory mode
│   │   └── doctor.ts            # Dependency checker
│   ├── core/
│   │   ├── pipeline.ts          # Main pipeline orchestrator
│   │   ├── downloader.ts        # yt-dlp integration
│   │   ├── transcriber.ts       # Whisper bridge
│   │   ├── chunker.ts           # Transcript chunking
│   │   ├── scorer.ts            # Heuristic scoring
│   │   ├── ranker.ts            # AI ranking bridge
│   │   ├── tracker.ts           # Face tracking
│   │   ├── renderer.ts          # FFmpeg rendering
│   │   └── subtitles.ts         # SRT/ASS generation
│   ├── ai/
│   │   ├── prompts.ts           # LLM prompt templates
│   │   ├── ollama.ts            # Ollama API client
│   │   └── ranking.ts           # AI ranking logic
│   ├── vision/
│   │   ├── face.ts              # Face detection
│   │   ├── scene.ts             # Scene detection
│   │   └── crop.ts              # Smart crop path
│   ├── ui/
│   │   └── tui.ts               # Terminal spinner UI
│   ├── config.ts                # Configuration loader
│   ├── types.ts                 # TypeScript types
│   └── index.ts                 # CLI entry point
├── skate/output/                # Rendered clips
├── skate/temp/                  # Working files
├── package.json
├── tsconfig.json
└── README.md

Why Build This?

Every "AI shorts" tool out there charges $20-50/month or requires API keys that bill per minute. Skate is:

• 100% local — nothing leaves your machine
• Free — no subscriptions, no API costs
• Private — your videos never hit a third-party server
• Customizable — swap models, tweak prompts, adjust scoring