| name | repl |
| description | Use nteract notebooks as a persistent Python REPL. Trigger this skill whenever you're about to run python3 -c, write a throwaway .py script, or chain multiple shell commands for data exploration, analysis, plotting, or iterative computation. Notebooks preserve state between cells, show rich output, and can be used in realtime with users. |
Use a Notebook Instead of python3 -c
When you have nteract MCP tools available and you're about to do multi-step Python work — chaining python3 -c commands, writing a throwaway .py script, or running exploratory code — use a notebook instead. You get persistent state between cells, rich output (tables, plots, errors with tracebacks), and a shareable .ipynb file.
Quick Start
create_notebook(path="~/analysis.ipynb")
create_cell(source="import pandas as pd\ndf = pd.read_csv('data.csv')\ndf.head()", cell_type="code", and_run=true)
Core Workflow
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Start a notebook:
create_notebook(path="~/analysis.ipynb") — creates and opens it.
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Add and run code cells:
create_cell(source="your code here", cell_type="code", and_run=true) — creates the cell AND executes it in one call. State persists: variables from earlier cells are available in later ones.
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Iterate on a cell:
set_cell(cell_id="...", source="updated code") then execute_cell(cell_id="...") — edit and re-run without creating a new cell.
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Check your work:
get_all_cells(format="summary", include_outputs=true) — see all cells with output previews at a glance.
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Save when done:
save_notebook() — writes the .ipynb to disk.
When to Use This
- Exploring a dataset (load, filter, plot, iterate)
- Running multi-step computations where later steps depend on earlier results
- Generating visualizations (matplotlib, plotly, altair)
- Prototyping code that you'll refine over several iterations
- Any task where you'd otherwise chain 3+
python3 -c commands
When NOT to Use This
- One-shot commands (
python3 -c "print(2+2)" is fine as-is)
- Running existing scripts (
python3 script.py)
- Non-Python tasks
