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Jupyter Notebooks

Cline provides comprehensive support for Jupyter notebooks (.ipynb files), enabling AI-assisted editing with full cell-level context awareness. This feature was developed in collaboration with Amazon to bring AI coding assistance to data science workflows.

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Getting started

Jupyter notebook support is a built-in feature of Cline. To use it, you just need to have the Jupyter notebook extension enabled in VS Code. Once you open any .ipynb file, you’ll see AI-assisted buttons in your notebook interface.

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How to use

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Generate Cell

Click the sparkle icon (✨) in the notebook toolbar to generate new cells with AI assistance. Generate Cell demo How it works: The AI receives context from surrounding cells, so it understands the variables and imports already in scope. This means you can reference existing DataFrames, functions, and other objects without re-explaining them. Example prompt: “Create a visualization showing the correlation matrix of numeric columns with a heatmap” The cell is inserted with proper notebook JSON structure, preserving metadata and ready to execute.

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Explain Cell

Click the Explain button in any cell’s title bar to get a detailed explanation of what the cell does. This is useful for:
  • Revisiting old notebooks
  • Onboarding to a teammate’s analysis
  • Understanding complex transformations
How it works: Cline extracts the full cell context, including outputs, so explanations can reference actual results like column names, row counts, and computed values.

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Improve Cell

Click the Improve button in any cell’s title bar to enhance existing cells with AI suggestions. Explain and Improve Cell demo Use this to:
  • Optimize slow pandas operations
  • Add error handling
  • Refactor for clarity
  • Convert loops to vectorized operations
How it works: Cline suggests improvements while preserving the cell’s position and metadata in the notebook structure. The AI explains what was changed and why.

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How cell context works

Unlike traditional file editing, Jupyter notebooks are JSON documents containing arrays of cells. Each cell has its own type, source content, metadata, execution count, and outputs. When you use a Jupyter command, Cline extracts structured context that includes:
  • Cell type (code, markdown, or raw)
  • Source content as an array of lines
  • Cell metadata and configuration
  • Execution count for code cells
  • Outputs including data, text, and error traces
This structured representation allows the AI to understand not just the code, but its context within the notebook and its actual output.

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JSON structure preservation

Cline is designed to work carefully with the cell JSON structure, aiming to:
  • Keep cell boundaries intact
  • Preserve execution counts
  • Maintain cell metadata
  • Keep outputs associated with their source cells
The AI is specifically prompted to preserve notebook structure, though you should always review changes to ensure your notebook format remains correct.

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Keyboard shortcuts

You can bind any of these commands to keyboard shortcuts for faster access:
  1. Open VS Code keyboard shortcuts (Cmd/Ctrl + K, Cmd/Ctrl + S)
  2. Search for cline.jupyterGenerateCell, cline.jupyterExplainCell, or cline.jupyterImproveCell
  3. Assign your preferred key combinations

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Tips for best results

For Generate Cell:
  • Be specific about what you want the cell to do
  • Reference existing variables by name (the AI can see them)
  • Mention preferred libraries if you have a preference (e.g., “use seaborn” or “use plotly”)
For Explain Cell:
  • Works best on cells that have been executed (outputs provide additional context)
  • Good for complex chained operations like pandas groupby/merge sequences
For Improve Cell:
  • Mention what aspect you want to improve (performance, readability, error handling)
  • The AI will explain the changes it suggests

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Limitations

  • Notebook support requires the Jupyter notebook extension to be enabled in VS Code
  • Cell context extraction depends on VS Code’s notebook API
  • Very large notebooks may require more context than some models can handle efficiently