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jupyter

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Create and execute Jupyter notebooks for interactive data analysis using jupyter_execute and jupyter_notebook tools. Use when the user asks to run Python code interactively, create notebooks, analyze data in cells, or mentions .ipynb files.

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Source SKILL.md: https://github.com/Prismer-AI/Prismer/blob/HEAD/docker/templates/data-scientist/skills/jupyter/SKILL.md

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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

Tool Examples

Execute Python code

jupyter_execute code="import pandas as pd\ndf = pd.read_csv('/workspace/data/results.csv')\nprint(df.describe())"

Create a notebook with cells

update_notebook cells=[{"type": "markdown", "source": "# Analysis"}, {"type": "code", "source": "import pandas as pd\nimport matplotlib.pyplot as plt"}] execute=false

Display generated plots

update_gallery images=[{"url": "/workspace/data/plot.png", "title": "Analysis Results"}]

Best Practices

  1. Cell Independence: Each cell should run independently when possible
  2. Import First: All imports at notebook start
  3. Check output before proceeding: Verify execution output is correct before running dependent cells
  4. Markdown Structure: Use headers for navigation
  5. Save Often: Checkpoint regularly