@r2hu1_npm/skate

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.

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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

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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.

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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

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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

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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

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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

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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.

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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

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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