Jupyter Notebook Guide
SkillFiles & storageRead, modify, execute, and convert Jupyter notebooks programmatically. Use when working with .ipynb files for data science workflows, including editing cells, clearing outputs, or converting to other formats.
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the Jupyter Notebook Guide skill
What this skill tells your AI
The instructions your AI receives, as published by openhands/extensions in skills/jupyter/SKILL.md and read by ahel’s review.
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
Signals
- GitHub stars
- 144
- Forks
- 86
- Last commit
- Sep 2026
Advanced
- Catalog kind
- skill
- Gateway key
jupyter-openhands- Source
- github.com/openhands/extensions