data

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

$ インストール

git clone https://github.com/gakonst/dotfiles /tmp/dotfiles && cp -r /tmp/dotfiles/.codex/skills/data ~/.claude/skills/dotfiles

// tip: Run this command in your terminal to install the skill


name: data description: Use 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\").