Notebooklm
NotebookLM generates detailed, step-by-step notes from your prompts, streamlining learning and research by creating organized summaries.
Install on your platform
We auto-selected Claude Code based on this skill’s supported platforms.
Run in terminal (recommended)
claude mcp add notebooklm npx -- -y @trustedskills/notebooklm
Or manually add to ~/.claude/settings.json
{
"mcpServers": {
"notebooklm": {
"command": "npx",
"args": [
"-y",
"@trustedskills/notebooklm"
]
}
}
}Requires Claude Code (claude CLI). Run claude --version to verify your install.
About This Skill
What it does
The notebooklm skill enables AI agents to interact with and manipulate Jupyter Notebooks, including executing code cells, generating markdown, and managing notebook structure. It supports dynamic content generation within notebooks and can be used for educational purposes or data analysis workflows.
When to use it
- When you need an AI agent to run or modify Python scripts in a Jupyter Notebook environment.
- For creating interactive tutorials or demonstrations that involve live code execution.
- To automate the process of generating reports or documentation directly within a notebook interface.
- When integrating AI-generated insights into data analysis workflows using notebooks.
Key capabilities
- Execute code cells within Jupyter Notebooks
- Generate markdown and text content dynamically
- Manage notebook structure (e.g., adding/removing cells)
- Support for interactive data visualization
Example prompts
- "Run the Python script in this Jupyter Notebook to analyze the dataset."
- "Generate a new markdown cell explaining the results of the last code execution."
- "Add a new code cell that plots a histogram of the 'age' column from the DataFrame."
Tips & gotchas
- Ensure that the AI agent has access to a running Jupyter kernel for executing code.
- Be cautious with notebook modifications—always save backups before making significant changes.
Tags
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Security Audits
| Gen Agent Trust Hub | Pass |
| Socket | Pass |
| Snyk | Pass |
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