Setup — Prerequisites for the video-edit skill
This skill orchestrates several external tools. A fresh machine needs the following one-time installs. The skill itself just calls these CLIs; nothing else is bundled.
Required
| Tool | Version | Purpose |
|---|---|---|
| Node.js | ≥ 22 | npx hyperframes … (scaffold, lint, render, background-remove) |
| Python | ≥ 3.10 | Whisper transcription + caption generator + review scripts |
| FFmpeg | any recent | Audio extraction, frame extraction, footage re-encoding |
| faster-whisper | latest | Word-level transcription (Python package) |
Install commands
Windows (PowerShell, with winget)
winget install OpenJS.NodeJS.LTS
winget install Python.Python.3.12
winget install Gyan.FFmpeg
pip install faster-whispermacOS (with Homebrew)
brew install node@22 python@3.12 ffmpeg
pip3 install faster-whisperLinux (Debian / Ubuntu)
sudo apt update
sudo apt install -y python3 python3-pip ffmpeg
curl -fsSL https://deb.nodesource.com/setup_22.x | sudo bash -
sudo apt install -y nodejs
pip3 install faster-whisperVerify
node --version # v22+ expected
python --version # 3.10+ expected
ffmpeg -version # any recent build
python -c "import faster_whisper; print(faster_whisper.__version__)"
npx hyperframes doctor # checks Chrome / FFmpeg / memory for rendersOptional but recommended
GPU acceleration for Whisper
faster-whisper can run on CUDA, but on Windows it usually crashes mid-decode because cuDNN is
not on the PATH. The bundled transcribe.py is hard-coded to CPU int8 for that reason —
fast enough on a modern CPU (~5 min for a 3-minute clip with the large-v3 model). If you have
cuDNN properly installed on Linux, swap device="cpu" for device="cuda" and
compute_type="int8" for compute_type="float16".
GPU for background removal (npx hyperframes remove-background)
CoreML (macOS), CUDA (Linux with proper drivers) or DirectML (Windows) accelerate the u2net mask model. Without GPU it falls back to CPU — a 10-second 1440p clip takes ~3-8 minutes.
npx hyperframes remove-background --info # lists available execution providersPre-cache Whisper model
The first transcribe downloads the large-v3 model (~3 GB) into
~/.cache/huggingface/hub/. Subsequent runs are instant to start.
Where files live
The skill expects to operate inside a HyperFrames project directory created by
npx hyperframes init. Reference assets in this skill (references/) are copied into that
project as part of step 4–6 in the main SKILL.md workflow.