Make YouTube learning more efficient with this simple workflow

Here’s a simple workflow to make learning from YouTube videos more efficient 🎯 This async, agent-native workflow has been super helpful so far (and it’s not fancy at all)

I specifically struggle with two categories of videos:

🙄 Too much hype – Inflated titles and misleading thumbnails.

⏰ Quality content, no time – Sitting through 30+ min videos when I have a gazillion other things to do (thanks to AI agents, humans are the bottleneck now, right?)

So here’s what I’ve started doing: I point GitHub Copilot CLI at a YouTube video and ask it to help me out.

🔍 If the title sounds too good to be true, I ask it to verify — “does this video actually deliver what the title promises?”

📝 For videos I know are good but I’m short on time, I ask Copilot CLI to extract the key insights, break down the structure, or answer specific questions about the content.

💡 If something piques my curiosity, I jump straight into that section.

The agent figures out how to get things done on its own. In my case, Copilot CLI reached for yt-dlp — a popular open-source CLI tool (⭐ 152K+ GitHub stars) that can pull transcripts, metadata, and more!

I tried it to dive into Episode 110 of Azure Cosmos DB TV 📹 where Sajeetharan Sinnathurai and Mark Brown cover the Cosmos DB MCP Toolkit 🤖