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Use for running and editing notebooks efficiently via jtool/Jupyter; prefers uv for deps and headless execution.

⭐12stars🔀2forks📁View on GitHub🕐Updated Dec 18, 2025
[Developer Tools]

When & Why to Use This Skill

This Claude skill provides a streamlined workflow for running, editing, and executing Jupyter notebooks programmatically. By integrating jtool for server management and uv for fast, isolated dependency handling, it enables efficient headless execution and automated notebook manipulation, making it an essential tool for data science automation and reproducible research.

Use Cases

  • Automated Data Pipelines: Execute complex data analysis notebooks headlessly as part of a scheduled task or data processing workflow.
  • Isolated Environment Execution: Use the 'uv' integration to run notebooks with specific library versions without polluting the global system environment.
  • Programmatic Notebook Editing: Automatically update cell content, parameters, or metadata in .ipynb files using scripts before execution.
  • Remote Server Management: Connect to and control active Jupyter servers via jtool to execute cells and retrieve outputs remotely.
namedata
descriptionUse for running and editing notebooks efficiently via jtool/Jupyter; prefers uv for deps and headless execution.

Data (notebook ops)

When to use

  • You need to run, edit, or execute Jupyter notebooks headlessly.
  • You want to use jtool for notebook execution/control when a Jupyter server is available.
  • You need a clean way to install deps for notebooks without polluting the system (use uv).

Notebook execution (prefer jtool if server exists)

  • Start a Jupyter server if needed: uv run jupyter notebook --no-browser --port 8888 --NotebookApp.token=''.
  • Add server once: jtool add-server http://127.0.0.1:8888.
  • Execute a notebook: jtool execute-cells path/to/notebook.ipynb --max-output-lines 200.
  • If no server is available, fall back to uv run ... jupyter nbconvert --execute.

Headless execution via uv (no server)

  • uv run --with pandas --with matplotlib --with notebook python -m jupyter nbconvert --to notebook --execute your.ipynb --output your.ipynb --ExecutePreprocessor.timeout=180
  • Use --with flags to supply lightweight deps without altering global envs.

Editing notebooks programmatically

  • Generate/patch notebooks with small Python scripts that write JSON (nbformat v4).
  • Keep cells compact; prefer clear markdown titles and minimal outputs when shipping.

Path & env tips

  • Default workspace: ~/github/....
  • Ensure ~/.local/bin on PATH so jtool is discoverable (export PATH=\"$HOME/.local/bin:$PATH\").