NotebookLM Research

SkillMedia

Run YouTube-driven research in ArgentOS by searching videos and sending links into NotebookLM for synthesis and infographic outputs via youtube_notebooklm.

Available today. Use it from your connected AI after setup.

Connect ahel once, and every AI you use reads what you have installed.

Then ask your AI: use the NotebookLM Research skill

What this skill tells your AI

The instructions your AI receives, as published by argentaios/argentos-core in skills/notebooklm-research/SKILL.md and read by ahel’s review.

Use youtube_notebooklm when the user wants market research, trend mapping, competitor scans, or synthesis from YouTube sources through NotebookLM.

Primary Action

Use youtube_to_notebook_workflow for end-to-end execution:

  1. Search YouTube
  2. Create NotebookLM notebook
  3. Add videos as sources
  4. Ask NotebookLM for analysis
  5. Optionally generate/download infographic

First-run check:

  • Call setup_status first. If setupRequired: true, run the returned next_steps commands before continuing.

Fast Start

{
  "action": "youtube_to_notebook_workflow",
  "query": "Claude Code skills marketing",
  "count": 10,
  "months": 6,
  "question": "What are the strongest recurring GTM patterns and differentiators across these videos?",
  "generate_infographic": true,
  "infographic_prompt": "Create a handwritten blueprint style infographic with GTM patterns, risks, and opportunities.",
  "infographic_orientation": "portrait",
  "infographic_detail": "detailed"
}

Granular Actions

  • youtube_search
  • setup_status
  • notebook_create
  • notebook_add_sources
  • notebook_ask
  • notebook_generate_infographic

Use granular actions if the user wants custom sequencing or to reuse an existing notebook.

Prerequisites

Install dependencies once:

pip install yt-dlp
pip install "notebooklm-py[browser]"
playwright install chromium
notebooklm login

If commands fail, report missing prerequisites and suggest the exact install/login command.

Operating Notes

  • Default recency filter is last 6 months (months: 6).
  • Set no_date_filter: true to include all upload dates.
  • Keep wait_for_sources: true for reliable downstream analysis.
  • For stakeholder-ready output, enable generate_infographic and keep the prompt specific.

Signals

GitHub stars
126
Forks
22
Last commit
Jul 2026
Advanced
Catalog kind
skill
Gateway key
notebooklm-research
Source
github.com/argentaios/argentos-core