Turn YouTube tutorials into structured steps and topics
A tutorial walks through steps in order and names the tools as it goes. Ask for each step, what it does and the tools mentioned, and get an outline you can search, quiz on or link to.
Describe the fields once
explanation is a string column, so it is written in the model's own words, while topic stays word for word.
The output above shows the shape a video returns, not a recorded result. Types are enforced: numbers come back as numbers, list columns as arrays.
What to expect
A 12-minute video is 120 credits: 10 per started minute, however many rows come back. Videos that fail, such as those without captions, are never charged. See pricing.
- TubeExtract reads the video’s existing captions, manual or auto-generated. It does not listen to audio or read the screen, and a video with no captions fails as
no_captions. - Code that is only shown on screen is not read. If the instructor never says the command, it will not be in the rows.
- A long course that lists dozens of steps will not yield every one; expect good coverage of what is explained at length.
- Send a whole playlist in one call: up to 200 videos, one job id to track.
One call, up to 200 videos
Send the schema with your list of videos, then read the job once every video has finished.
Turn YouTube product reviews into a dataset: product names, prices, verdicts and pros as typed JSON rows, from 1 to 200 videos per API call.
Build a list of every restaurant, city and dish from YouTube food and travel videos, returned as structured JSON rows through one API call.
Turn video podcasts and interviews on YouTube into structured data: guests, their roles, topics and the claims they make, as JSON rows via API.
Try it on your own videos
New accounts start with 1,000 credits, about 100 minutes of video. Paste the schema above, point it at a video, and check the rows.