Built for creators who move fast.
ClipMaster started as an internal tool for repurposing podcast episodes. The core insight was simple: the best moments in long-form content are already there — they just need to be found, explained, and formatted correctly.
Most AI clipping tools either output every segment indiscriminately (leaving you to sort through dozens of mediocre suggestions) or apply black-box labels that mean nothing. ClipMaster takes a different approach: every clip gets a numeric self-containment score and a one-sentence explanation of why it scored that way.
That explanation is the product. It lets you agree with the model, override it, or understand where it's wrong for your specific audience. Scoring without reasoning is just a number.
The platform handles the full pipeline: download, transcribe, suggest moments, trim, add captions, apply brand kit, export to the right aspect ratio. The goal is to reduce the time from “raw video” to “ready to post” to under 15 minutes per episode — and under 2 minutes of active work.
No manual scrubbing
Clipping a 2-hour podcast by hand takes 3–4 hours. That's time better spent on the next episode. ClipMaster's goal is to eliminate that work entirely.
Honest scoring, not magic
The score rates clip quality and self-containment. It does not predict views or platform performance, and every score comes with reasoning you can inspect.
Real data, no dark patterns
We don't hide credit costs, inflate performance claims, or obscure what the AI is actually doing. Every score comes with an explanation.
Built on the actual pipeline
yt-dlp for download, Whisper for upload transcription, OpenAI for model scoring, ffmpeg for rendering. No silent fallback paths that pretend a model ran.
Try it before you commit to anything
Every new account gets 30 free credits. No credit card required. The landing page demo runs without an account at all when the scoring model is configured — paste any YouTube URL and see suggested moments in real time.