Set up a video-editing project for me. Start on your cloud computer if it can support the work.

First report where the job will run. Check FFmpeg/ffprobe, Python, faster-whisper, Node.js, HyperFrames and its browser renderer, available memory and disk space. Separate installed tools from things you have actually tested.

Use the current official documentation. Prefer local Whisper transcription on that computer so we do not need a transcription API key. Choose a model and compute type that suit the hardware; try turbo if practical and explain any smaller-model choice. Check that the model really downloads and loads.

For HyperFrames, use an available supported integration or set up its CLI in the project. Do not assume locally installed skills are available in a cloud task. Test the renderer, font loading and audio.

Create a separate project folder. Preserve my original recording. Tell me about any required permission or paid service before using it. Do not change security settings to make a download work.

With my supplied clip, render a five-second test containing video, audio, one caption and a simple visual. Return the actual MP4, the project location and a short passed/blocked report before editing the full video.
