NotebookLM Skill

The Decision

NotebookLM Skill bridges two powerful tools: Google’s NotebookLM for deep research and Claude for content creation. Feed it URLs, PDFs, or trending topics — it creates a research notebook, runs deep queries with citations, and hands structured findings to Claude for polished output: articles, social posts, newsletters, podcasts, slides, or any format you need.

It replaces manual web research, Perplexity-based drafting, and standalone NotebookLM usage.

Our testing rates it 9.1/10 overall. Works as both a Claude Code skill and an MCP server. MIT licensed with free Google authentication.

Who It’s For

• Content creators who need research-backed articles and newsletters
• Newsletter writers processing multiple sources into digests
• Educators creating podcasts, slides, and study materials
• Teams turning research papers into actionable content

Who Should Skip

• Casual social media posters who don’t need deep research
• Creators who write opinion pieces without sourcing
• Anyone who already has a manual research workflow they’re happy with

Why This Skill Matters

Most content creation starts with research — reading articles, watching videos, scanning papers. Then you switch to a writing tool. Then you convert the article into social posts, a newsletter intro, maybe a podcast. Each step is a separate tool, a separate context switch, a separate manual handoff.

NotebookLM Skill collapses this entire pipeline into one Agent workflow. You provide sources, and the Agent researches, writes, and generates artifacts — all in your terminal, all with one conversation.

The Pipeline

Phase 1: Collect Sources
  URLs, PDFs, YouTube videos, trending topics
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        v
Phase 2: Deep Research (via NotebookLM)
  Create notebook → Add sources → Ask questions → Extract insights
  NotebookLM provides citations and source-grounded answers
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        v
Phase 3: Content Generation (via Claude)
  Structured findings → Article draft, social posts, newsletter
  Agent formats for your target platform
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        v
Phase 4: Artifacts & Publishing
  Audio podcast, video summary, slide deck, report,
  quiz, flashcards, mind map, infographic, data table
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        v
  → Blog / CMS / Threads / Newsletter / Any platform

Core Features

1. Research Pipeline (research-to-article)
   Feed URLs or PDFs to NotebookLM, which creates a research notebook and asks 5 deep questions. The structured findings — with citations — get passed to Claude for article drafting. This is faster and more thorough than manual research. Verdict: excellent.

2. 9 Artifact Types
   From a single research session, generate: audio podcast (M4A), video summary (MP4), slide deck (PDF/PPTX), written report (Markdown), quiz (JSON/MD/HTML), flashcards, mind map (JSON), infographic (PNG), and data table (CSV). Most artifacts support multi-language output. Verdict: excellent.

3. Trend-to-Content Pipeline
   Connect to trend-pulse for real-time trending topic discovery. The Agent fetches trends, creates research notebooks for each, and drafts platform-specific social posts — all automated. Verdict: great.

4. MCP Server Mode
   Runs as an MCP server (notebooklm-mcp), making all 13 NotebookLM tools available to any compatible client: Claude Code, Cursor, Gemini CLI. One JSON config line to add it. Verdict: great.

5. Batch RSS Digest
   Feed an RSS URL → auto-fetch articles → create research notebook → generate newsletter digest with summary and Q&A section. Great for weekly newsletter workflows. Verdict: great.

Hands-On

Install (zero-config via uvx):

uvx notebooklm-skill --help

Or install via pip:

pip install notebooklm-skill

One-time Google authentication:

uvx notebooklm login
# Opens Chromium → sign in with Google → session saved automatically

Research to article:

notebooklm-skill create --title "AI Agents Survey" --sources https://arxiv.org/abs/2401.00001 https://blog.example.com/ai-agents
notebooklm-skill ask --notebook "AI Agents Survey" --query "What are the key architectural patterns?"

Full pipeline — research to article (one command):

python scripts/pipeline.py research-to-article \
  --sources "https://arxiv.org/abs/2401.00001" \
            "https://blog.example.com/ai-agents" \
  --title "AI Agent Survey"

Generate podcast from research:

notebooklm-skill podcast --notebook "AI Agents Survey" --lang en --output podcast.m4a

Trending topics → social posts:

python scripts/pipeline.py trend-to-content --geo US --count 5 --platform threads

MCP Server setup (add to .mcp.json):

{
  "mcpServers": {
    "notebooklm": {
      "command": "uvx",
      "args": ["--from", "notebooklm-skill", "notebooklm-mcp"]
    }
  }
}

Claude Code skill setup:

./install.sh
# Then just ask Claude to research topics — it auto-detects the skill

Real-World Case Study: Turning a 50-Page Research Report into a 15-Minute Podcast in 3 Minutes

The Challenge

You’re a marketing manager at a B2B SaaS company. Your team just received a 50-page industry research report from a partner agency. You need to:

• Share the key findings with your 12-person team during a 15-minute standup
• Give the CEO a quick audio briefing for their commute
• Extract specific data points for a client presentation

Reading the full report takes 45+ minutes. Summarizing it manually takes another 30 minutes. Goal: get the key insights in under 5 minutes using NotebookLM.

The Prompt Chain (Exact Steps)

1. Upload the PDF to NotebookLM

   Go to notebooklm.google → Sign in with Google → Create new Notebook → Add Source → Upload PDF

   Agent output: NotebookLM processes the 50-page PDF in ~30 seconds. You can now ask questions about the document.

2. Generate an AI Podcast Summary

   Click "Generate Audio Overview" → NotebookLM creates a 15-minute podcast with two AI hosts discussing the report's key findings.

   Agent output: A natural-sounding podcast with two hosts (one plays the skeptic, one the expert) discussing the report’s methodology, findings, and implications. Perfect for listening during a commute.

3. Ask Specific Questions About the Data

   "What are the top 3 findings from this report?"
   "Which data points support the conclusion about market growth?"
   "Summarize the methodology section in bullet points."

   Agent output: Each answer includes citation numbers that link back to the original PDF page. You can verify the AI’s claims by clicking the citations.

4. Create a Team Summary Document

   "Create a one-page executive summary of this report with: key findings, methodology, data highlights, and implications for our business."

   Agent output: A structured summary document you can share with your team. Each claim is cited to the original source.

5. Export for Offline Use

   Download the podcast as MP3 → Share with CEO for commute listening
   Copy the summary text → Paste into team chat for standup

   Agent output: MP3 file ready for mobile listening. Summary text ready for Slack/Teams.

The Result

• Before: 45 minutes reading + 30 minutes summarizing = 75 minutes
• After: 3 minutes uploading + 2 minutes asking questions = 5 minutes
• Output quality: AI podcast captures nuanced findings; citations ensure accuracy
• Team impact: CEO gets audio briefing during commute; team gets written summary for standup
• Cost: Free (NotebookLM is free with Google account)

Key Takeaways for Prompt Engineering

• Use NotebookLM for document-specific Q&A: Unlike ChatGPT, NotebookLM only answers based on your uploaded sources — no hallucinations.
• Generate Audio Overview for quick consumption: The AI podcast format is surprisingly engaging for complex topics.
• Always check citations: Click the citation numbers to verify AI claims against the original document.
• Combine with other tools: Export the summary to Google Docs, share the podcast via Slack, use the data for presentations.
• Free tier is generous: 50 sources per notebook, 500K words per source — enough for most business use cases.

This workflow turns a 50-page research report into a 15-minute audio briefing and a one-page summary in under 5 minutes.

Pricing

Free (MIT License) — Full pipeline, all 9 artifact types, MCP server mode. Requires only a free Google account for NotebookLM authentication.

Verdict: 9.1/10

NotebookLM Skill is the most complete research-to-content pipeline available as an Agent skill. The combination of NotebookLM’s source-grounded research with Claude’s writing quality creates content that’s both well-researched and well-written. The 9 artifact types — especially audio podcasts and slide decks — make it a Swiss Army knife for content creators. For newsletter writers, educators, and research-heavy content creators, this skill eliminates the biggest bottleneck in content production: turning raw sources into polished output.

Try It

pip install notebooklm-skill
notebooklm login

Source Code · 265 ⭐ · MIT License

FAQ

Q: Do I need a Google Cloud project or API key?
A: No. notebooklm-skill uses browser-based Google login — one-time authentication that opens Chromium. No OAuth setup, no API keys, no Google Cloud project required.

Q: Can I use it without Claude Code?
A: Yes. It works as an MCP server compatible with Cursor, Gemini CLI, and any MCP-compatible client. Add one line to your .mcp.json and you’re set.

Q: How is this different from using NotebookLM directly?
A: NotebookLM is a standalone app. This skill integrates it into your Agent workflow — Claude orchestrates the research, then uses findings to write content, generate artifacts, and publish. You stay in your terminal instead of switching between apps.

Q: What if NotebookLM goes down or changes its API?
A: The underlying notebooklm-py library handles NotebookLM’s web interface, not an official API. If Google changes the interface, the library may need updates. The MIT license and open-source nature mean the community can maintain it independently.

Q: Can it generate podcasts in other languages?
A: Yes. Pass --lang zh-TW (or other language codes) when generating audio. Most artifact types support language selection. Quiz, flashcards, and mind maps currently don’t support language override.