Claude + NotebookLM Research Pipeline

Step 1. Paste this into Claude Code

This one prompt builds the YouTube research skill, installs NotebookLM-PY, and wires everything together. No other files needed.

Setup Prompt → Claude Code

I want to set up an automated research pipeline connecting Claude Code to NotebookLM. Please perform the following steps and I will 'accept' all your proposed changes:

1. Build a YouTube Research Skill: Create a custom Python-based skill using the yt-dlp dependency. This skill should be able to scrape YouTube metadata, including video titles, views, author, duration, and URLs based on a search query.

2. Integrate NotebookLM-PY: I want to use the unofficial Python API for NotebookLM created by Teng Lin. You can find the repository and installation instructions here: https://github.com/teng-lin/notebooklm-py. Please install the necessary packages and set up a skill that allows you to:
   • Create new notebooks
   • Upload YouTube URLs as sources
   • Request analysis and deliverables like infographics, slide decks, and flashcards

3. Guide me through Authentication: Once the tools are installed, remind me to open a separate terminal window to run the notebooklm login command so I can authenticate with my Google account.

4. Confirm Readiness: Once everything is installed and the skills are recognized, let me know you are ready for a test run.

The goal is that once this is finished, I should be able to give you a single command like this:

"Use the yt-research skill to find the 25 latest trending videos on [YOUR TOPIC]. Once we have those videos, send them over to NotebookLM using the notebooklm skill. Give me its analysis on the top findings, then have NotebookLM create an infographic in a handwritten / chalkboard style depicting that analysis."

Important: If I give you the research command without specifying a topic, ask me what topic I want to research before proceeding.

What happens next

1. Say yes to everything. Claude Code starts writing the Python scripts and creating the skills. When it asks permission to run commands or install packages, hit Y or Enter. If Python isn't installed yet, Claude Code handles that too. Just keep accepting.
2. The login step (read carefully). When Claude tells you to authenticate with NotebookLM, open a completely separate terminal window. This has nothing to do with Claude. Don't type anything Claude-related in it. Just open a fresh terminal and run:

notebooklm login

Mac: press Cmd+Space, type Terminal, hit Enter. Windows: press Win+R, type cmd, hit Enter. A browser opens, log in with your Google account, done. Close that terminal and go back to Claude Code.

1. Claude confirms it's ready. Claude Code tells you the skills are installed and recognized. It will ask what topic you want to research, or you can jump straight to a test prompt.

Step 2. Run your first research command

Once setup is confirmed, give Claude Code a command like this. Swap in your topic.

Research Command → Claude Code

Use the yt-research skill to find the 25 latest trending videos on [YOUR TOPIC]. Once we have those videos, send them over to NotebookLM using the notebooklm skill. Give me its analysis on the top findings, then have NotebookLM create an infographic in a handwritten / chalkboard style depicting that analysis.

Ten minutes later you have your research brief and a visual summary, both built from 25 expert sources.

The use case that changed how I learn

This is what I actually use this for. It's worth walking through because once you see the pattern, the applications across your own work and life are obvious.

Take a health question I actually had: is high cholesterol something to worry about?

The traditional approach: read 10 articles online, get confused by conflicting takes, watch a few podcast episodes from people I trust, take notes, compare. Probably 8 to 10 hours over a weekend. End up with more questions than I started with.

With this pipeline, the prompt is:

Use yt-research to find the latest videos on cholesterol from Andrew Huberman, Bryan Johnson, Peter Attia, Rhonda Patrick, and Mark Hyman. Send them to NotebookLM. Give me a synthesized brief on: (1) is high cholesterol actually dangerous, (2) where do these experts agree, (3) where do they disagree, (4) what specific protocols do they each recommend, (5) what peptides or supplements do they recommend or warn against. Then create an infographic summarizing the consensus and the disagreements.

Ten minutes later I have a research brief built on hours of content from people who actually know what they're talking about. Not a Google summary. Not a generic AI answer. The actual synthesized opinions of the specific experts I trust on this topic.

The pattern works for anything:

• Health and longevity: any protocol, supplement, or condition, pulled from your trusted experts
• Business and operations: how do the top creators in [niche] structure their content, what tools do they use, where do they disagree about strategy
• Investing: what are the bear and bull cases for [theme] according to the analysts you respect
• Learning a new domain: absorb a year of conference talks on a topic in one afternoon
• Content research: scan everything that's been said about a topic before you create your own piece
• Decision-making: when you need expert input but don't know anyone you can call, this is the next best thing

The shift isn't that you can find information faster. You always could. The shift is that you can hear from five trusted experts on a specific question, get their actual positions side by side, and act on the synthesis. That used to be the job of a research analyst.

If something goes wrong

• Browser doesn't open on login: Make sure you ran notebooklm login in a fresh terminal, not inside Claude Code, and not in the same window you're using for anything else.
• Install fails: Tell Claude Code exactly what error you see. It will diagnose and fix it. Just paste the error message back and keep accepting.
• The skills aren't building correctly: Tell Claude Code to verify it has yt-dlp and notebooklm-py installed by running pip show yt-dlp notebooklm-py in your terminal. If either is missing, ask Claude Code to install them and rebuild the skills.
• Skills not recognized after setup: Ask Claude Code: "Can you verify the yt-research and notebooklm skills are installed and show me how to use them?"
• NotebookLM rate limit: Google caps how many artifacts you can generate. If an infographic fails, wait 5 minutes and try again.

Why this matters more than it looks

Everyone I know has a stack of "I should really go deep on this" topics they never get to. New supplement they want to evaluate. A market they're considering entering. A skill they want to learn. A health protocol they keep hearing about. The reason these stay on the list is the time cost. Going deep on anything used to mean a week of evenings, and you don't have a week of evenings.

This pipeline changes the equation. Going deep on a topic now costs you 10 minutes plus an afternoon of reading the output. That's a 50x compression on the time cost of learning anything.

If you scale that across a year, the difference is enormous. You go from learning seriously about 4-5 topics a year to learning seriously about 50-60. The compounding on that is hard to overstate. The people who figure this out first are going to know more, decide better, and move faster than people who don't.

This is one of the highest-leverage workflows I've added to my stack this year. Set it up once. Use it forever.