About jupyter
A practical guide skill for working with Jupyter notebooks as JSON files. It covers editing cells, executing notebooks, converting formats, searching code, and clearing outputs.
Key features
- 01Edit notebook cells as JSON
- 02Execute notebooks with nbconvert
- 03Convert notebooks to HTML, script, or Markdown
- 04Search notebook contents
- 05Clear code cell outputs
Use cases
- 01Editing notebook source programmatically
- 02Running a notebook in place
- 03Exporting notebook content to other formats
Install
Copy the command and run it in a terminal.
$
npx skills add https:/ / github.com/ openhands/ extensions --skill jupyterAfter installing, type/jupyterin Claude Code to confirm it loaded.
Bundled files.plugin/plugin.jsonREADME.md
Details
From the project READMEJupyter Notebook Guide
Notebooks are JSON files. Cells are in nb['cells'], each has source (list of strings) and cell_type ('code', 'markdown', or 'raw').
Modifying Notebooks
import json
with open('notebook.ipynb') as f:
nb = json.load(f)
# Modify nb['cells'][i]['source'], then:
with open('notebook.ipynb', 'w') as f:
json.dump(nb, f, indent=1)
Executing & Converting
jupyter nbconvert --to notebook --execute --inplace notebook.ipynb # Execute in place
jupyter nbconvert --to html notebook.ipynb # Convert to HTML
jupyter nbconvert --to script notebook.ipynb # Convert to Python
jupyter nbconvert --to markdown notebook.ipynb # Convert to Markdown
Finding Code
grep -n "search_term" notebook.ipynb
PowerShell equivalent:
Select-String -Path notebook.ipynb -Pattern "search_term"
Cell Structure
# Code cell
{"cell_type": "code", "execution_count": None, "metadata": {}, "outputs": [], "source": ["code\n"]}
# Markdown cell
{"cell_type": "markdown", "metadata": {}, "source": ["# Title\n"]}
Clear Outputs
for cell in nb['cells']:
if cell['cell_type'] == 'code':
cell['outputs'] = []
cell['execution_count'] = None
Comments
Markdown supported