notebooklm-skill 1.2.0

NotebookLM research automation — CLI, MCP server, and Claude Code Skill

notebooklm-skill

NotebookLM does the research, Claude writes the content.

The only tool that connects trending topic discovery → NotebookLM deep research → AI content creation → multi-platform publishing. Works as a Claude Code Skill or standalone MCP Server.

繁體中文版 README

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Demo

Language	YouTube	Slides
English	Watch	6 pages, auto-generated
繁體中文	Watch	5 pages, auto-generated

All slides, podcasts, and videos were generated by NotebookLM using this tool.

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What is this?

notebooklm-skill bridges NotebookLM's research capabilities with Claude's content generation. Feed it URLs, PDFs, or trending topics — it creates a NotebookLM notebook, runs deep research queries, and hands structured findings to Claude for polished output: articles, social posts, newsletters, podcasts, or any format you need.

Built on notebooklm-py v0.3.4 — pure async Python, no OAuth setup needed.

Sources (URLs, PDFs)          NotebookLM                Claude               Artifacts & Platforms
+-----------------+    +------------------+    +-----------------+    +----------------------+
| Web articles    |--->| Create notebook  |--->| Draft article   |--->| Blog / CMS           |
| Research papers |    | Add sources      |    | Social posts    |    | Threads / X          |
| YouTube videos  |    | Ask questions    |    | Newsletter      |    | Newsletter           |
| Trending topics |    | Extract insights |    | Any format      |    | Any platform         |
+-----------------+    +------------------+    +-----------------+    +----------------------+
     Phase 1                Phase 2                Phase 3                  Phase 4
                                |
                                v
                       +------------------+
                       | Generate artifacts|
                       | Audio (podcast)   |
                       | Video             |
                       | Slides            |
                       | Report            |
                       | Quiz              |
                       | Flashcards        |
                       | Mind map          |
                       | Infographic       |
                       | Data table        |
                       | Study guide       |
                       +------------------+
                            Phase 2b

Quick Start

# Option A: uvx (recommended — zero install)
uvx notebooklm-skill --help
uvx --from notebooklm-skill notebooklm-mcp   # Start MCP server

# Option B: pip install from PyPI
pip install notebooklm-skill

# Option C: Install from source
git clone https://github.com/claude-world/notebooklm-skill.git
cd notebooklm-skill && pip install .

# Option D: One-line install (pip + Playwright + Claude Code Skill)
git clone https://github.com/claude-world/notebooklm-skill.git
cd notebooklm-skill && ./install.sh

# Authenticate with Google (one-time, opens browser)
python3 -m notebooklm login

# Use global commands
notebooklm-skill create --title "My Research" --sources https://example.com/article
notebooklm-skill ask --notebook "My Research" --query "What are the key findings?"
notebooklm-skill podcast --notebook "My Research" --lang en --output podcast.m4a
notebooklm-pipeline research-to-article --sources https://example.com --title "Topic"
notebooklm-mcp                   # Start MCP server (stdio mode)

Or use scripts directly: python scripts/notebooklm_client.py create ...

See docs/SETUP.md for the full setup guide.

Authentication

notebooklm-py uses browser-based Google login (no OAuth Client ID needed):

Step	Command	What happens
Login	python3 -m notebooklm login	Opens Chromium, user logs into Google
Session storage	Automatic	Saved to ~/.notebooklm/storage_state.json
Subsequent use	NotebookLMClient.from_storage()	Reads saved session, pure HTTP calls
Verify	python scripts/auth_helper.py verify	Loads client + calls notebooks.list()
Clear	python scripts/auth_helper.py clear	Removes ~/.notebooklm/ directory

Session typically lasts weeks. Re-run login if you get authentication errors.

Two Ways to Use

	Claude Code Skill	MCP Server
Best for	Claude Code users who want NotebookLM in their workflow	Any MCP-compatible client (Cursor, Gemini CLI, etc.)
Setup	Copy skill to .claude/skills/	Add server to MCP config
Invocation	Claude auto-detects when relevant	Tools appear in client tool list
Config	SKILL.md + .env	.mcp.json + .env
Requirements	Python 3.10+, notebooklm-py	Python 3.10+, notebooklm-py

Features

Feature	Description	Status
Notebook CRUD	Create, list, delete notebooks	Available
Source ingestion	Add URLs, PDFs, YouTube links, plain text	Available
Research queries	Ask questions against notebook sources with citations	Available
Structured extraction	Get key facts, arguments, timelines	Available
Content generation	Use research output as context for Claude	Available
Batch operations	Process multiple sources or queries at once	Available
trend-pulse integration	Auto-discover trending topics to research	Available
threads-viral-agent integration	Publish research-backed social posts	Available

Artifact Generation (10 types)

Artifact	Format	Description
Audio	M4A	AI-generated podcast discussion
Video	MP4	Video summary with visuals
Slides	PDF / PPTX	Presentation deck
Report	Markdown	Comprehensive written report
Quiz	JSON / Markdown / HTML	Multiple-choice assessment questions
Flashcards	JSON / Markdown / HTML	Study flashcard deck
Mind map	JSON	Visual concept map
Infographic	PNG	Visual data summary
Data table	CSV	Structured data extraction
Study guide	Markdown	Structured learning material

Most artifacts support language selection (e.g., --lang zh-TW). Exceptions: quiz, flashcards, mind-map.

Note: NotebookLM returns audio in MPEG-4 (M4A) format, not MP3.

Architecture

+---------------------------------------------------------------+
|                      notebooklm-skill                          |
|                                                                |
|  +---------+  +--------------+  +----------+  +------------+  |
|  | Phase 1 |  |   Phase 2    |  |  Phase 3 |  |  Phase 4   |  |
|  | Collect  |->|  Research    |->| Generate  |->|  Publish   |  |
|  +---------+  +--------------+  +----------+  +------------+  |
|      |              |                |               |         |
|  +--------+  +-------------+  +-----------+  +-----------+    |
|  | URLs   |  | NotebookLM  |  |  Claude    |  | Threads   |    |
|  | PDFs   |  | (via        |  |  Content   |  | Blog      |    |
|  | RSS    |  |  notebooklm |  |  Engine    |  | Email     |    |
|  | Trends |  |  -py 0.3.4) |  |            |  | CMS       |    |
|  +--------+  | - notebooks |  +-----------+  +-----------+    |
|              | - sources   |        |                          |
|              | - chat/ask  |  +-----------+                    |
|              | - artifacts |  | Artifacts |                    |
|              +-------------+  | audio     |                    |
|                               | video     |                    |
|                               | slides    |                    |
|                               | report    |                    |
|                               | quiz      |                    |
|                               | flashcards|                    |
|                               | mind-map  |                    |
|                               | infographic|                   |
|                               | data-table|                    |
|                               | study-guide|                   |
|                               +-----------+                    |
|                                                                |
|  +-----------------------------------------------------------+ |
|  |  Interfaces                                                | |
|  |  +-- scripts/          CLI tools (notebooklm-py direct)   | |
|  |  +-- mcp_server/       MCP protocol server                 | |
|  |  +-- SKILL.md          Claude Code skill definition        | |
|  +-----------------------------------------------------------+ |
+---------------------------------------------------------------+
         ^                                          ^
         |                                          |
   +-----------+                             +-----------+
   |trend-pulse|                             |threads-   |
   |(optional) |                             |viral-agent|
   +-----------+                             |(optional) |
                                             +-----------+

Usage Examples

1. Research to Article

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

2. Research to Social Posts

python scripts/pipeline.py research-to-social \
  --sources "https://example.com/ai-news" \
  --platform threads \
  --title "AI News This Week"

3. Trending Topics to Content

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

4. RSS Batch Digest

python scripts/pipeline.py batch-digest \
  --rss "https://example.com/feed.xml" \
  --title "Weekly AI Digest"

5. Generate All Artifacts

python scripts/pipeline.py generate-all \
  --sources "https://example.com/article" \
  --title "Research" \
  --output-dir ./output \
  --language zh-TW

6. Slides + Podcast → YouTube Video

Combine NotebookLM-generated slides and podcast into a YouTube-ready video:

# Generate slides and podcast
python scripts/notebooklm_client.py generate --notebook "Research" --type slides
python scripts/notebooklm_client.py podcast --notebook "Research" --lang en --output podcast.m4a
python scripts/notebooklm_client.py download --notebook "Research" --type slides --output slides.pdf

# Convert PDF to PNG + compose video
./scripts/make_video.sh slides.pdf podcast.m4a output.mp4

Pipeline Workflows

Workflow	Input	Output	Steps
research-to-article	URLs, text	Article draft JSON	Create notebook → 5 research questions → article draft
research-to-social	URLs, text	Social post draft	Create notebook → summarize → platform-specific post
trend-to-content	Geo, count	Content per trend	Fetch trends → create notebooks → research → draft
batch-digest	RSS URL	Newsletter digest	Fetch RSS → create notebook → digest + Q&A
generate-all	URLs, text	Audio, video, PDF, etc.	Create notebook → generate all artifacts → download

MCP Server Setup

Add to your project's .mcp.json:

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

Or if you installed via pip install notebooklm-skill:

{
  "mcpServers": {
    "notebooklm": {
      "command": "notebooklm-mcp"
    }
  }
}

Works with Claude Code, Cursor, Gemini CLI, and any MCP-compatible client.

Claude Code Skill Setup

# Option A: Symlink (auto-updates with git pull)
./install.sh

# Option B: Manual copy
mkdir -p .claude/skills/notebooklm
cp /path/to/notebooklm-skill/SKILL.md .claude/skills/notebooklm/
cp /path/to/notebooklm-skill/scripts/*.py .claude/skills/notebooklm/scripts/
cp /path/to/notebooklm-skill/requirements.txt .claude/skills/notebooklm/

# Authenticate (one-time)
python3 -m notebooklm login

Claude will automatically detect the skill when you ask about research, NotebookLM, or content creation.

API Reference

CLI Commands (11)

Command	Description
create	Create a notebook with URL/text sources
list	List all notebooks
delete	Delete a notebook
add-source	Add a source (URL, text, or file) to existing notebook
ask	Ask a research question (returns answer + citations)
summarize	Get notebook summary
generate	Generate an artifact (audio, video, slides, etc.)
download	Download a generated artifact
research	Run deep web research
podcast	Shortcut for generate --type audio (auto-downloads)
qa	Shortcut for generate --type quiz

MCP Tools (13)

Tool	Description
nlm_create_notebook	Create notebook with sources
nlm_list	List all notebooks
nlm_delete	Delete a notebook
nlm_add_source	Add source to existing notebook
nlm_ask	Ask question (returns answer + citations)
nlm_summarize	Get notebook summary
nlm_generate	Generate artifact (10 types)
nlm_download	Download generated artifact
nlm_list_sources	List sources in notebook
nlm_list_artifacts	List generated artifacts
nlm_research	Deep web research
nlm_research_pipeline	Full research pipeline
nlm_trend_research	Trend → research pipeline

Integrations

• trend-pulse — Real-time trending topic discovery from 7 sources
• threads-viral-agent — Auto-publish research-backed social posts

Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes
4. Push and open a Pull Request

# Development setup
git clone https://github.com/claude-world/notebooklm-skill.git
cd notebooklm-skill
pip install -e .
python3 -m notebooklm login
python -m pytest tests/

License

MIT License. See LICENSE.