jupyter
Create and execute Jupyter notebooks for interactive data analysis using jupyter_execute and jupyter_notebook tools
Best use case
jupyter is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Create and execute Jupyter notebooks for interactive data analysis using jupyter_execute and jupyter_notebook tools
Teams using jupyter should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/prismer-jupyter/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How jupyter Compares
| Feature / Agent | jupyter | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Create and execute Jupyter notebooks for interactive data analysis using jupyter_execute and jupyter_notebook tools
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Jupyter Notebook Skill ## Description Create and execute Jupyter notebooks for interactive data analysis and visualization. ## Tools Used - `jupyter_execute` - Execute Python code in Jupyter kernel (auto-switches to Jupyter) - `jupyter_notebook` - Create, read, update, delete, and list notebooks - `update_notebook` - Add or update cells in the notebook without executing - `update_gallery` - Display generated plots and visualizations in gallery view - `update_data_grid` - Display structured tabular data (DataFrames, query results) in AG Grid - `update_code` - Show code examples and scripts in the Code Playground - `save_artifact` - Save generated artifacts (plots, data files) to workspace collection ## Capabilities - Create new notebooks with proper structure - Add and execute code cells - Add markdown documentation cells - Display inline visualizations - Display tabular data in interactive grid view - Show code examples with syntax highlighting - Export to various formats (HTML, PDF) ## Usage Patterns ### Create Analysis Notebook When user says: "Create a notebook for [analysis]" 1. Create notebook with title and imports 2. Add data loading cell 3. Add exploration cells 4. Structure with markdown headers 5. Execute cells sequentially ### Execute and Debug When user says: "Run this code" 1. Execute cell 2. Capture output and errors 3. If error, diagnose and fix 4. Show results or visualizations ### Document Workflow When user says: "Add explanation for this step" 1. Add markdown cell before code 2. Explain methodology 3. Note assumptions and limitations ## Best Practices 1. **Cell Independence**: Each cell should run independently when possible 2. **Import First**: All imports at notebook start 3. **Clear Outputs**: Clean outputs before sharing 4. **Markdown Structure**: Use headers for navigation 5. **Save Often**: Checkpoint regularly
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