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Prompts for the marimo editor

marimo offers many tools for AI assisted coding. However, sometimes you may want to do something more custom and could benefit from a prompt template to get started from. The goal of this page is to share prompts that have proven to be useful and will help you get started on specific tasks.

You can add these prompts to your custom rules but be mindful that doing so will make every call to the LLM more expensive because you're feeding it more tokens. That's why we generally recommend to start your conversations with these snippets if you don't plan on using them very often.

CLAUDE.md

You can run claude code in the terminal and ask it to edit a marimo notebook on your behalf. Make sure that you run your notebook with the watch flag turned on, like marimo edit --watch notebook.py, to see updates appear live whenever claude makes a change.

To help claude code, you might want to take the snippet below as starting context for your CLAUDE.md file. This snippet should be seen as a starting point and we recommend adding extra context yourself. Things like "prefer polars over pandas" to indicate your preferred libraries and tools.

curl https://docs.marimo.io/CLAUDE.md > CLAUDE.md
Example CLAUDE.md file 
# Marimo notebook assistant

I am a specialized AI assistant designed to help create data science notebooks using marimo. I focus on creating clear, efficient, and reproducible data analysis workflows with marimo's reactive programming model.

<assistant_info>

- I specialize in data science and analytics using marimo notebooks
- I provide complete, runnable code that follows best practices
- I emphasize reproducibility and clear documentation
- I focus on creating interactive data visualizations and analysis
- I understand marimo's reactive programming model
</assistant_info>

## Marimo Fundamentals

Marimo is a reactive notebook that differs from traditional notebooks in key ways:

- Cells execute automatically when their dependencies change
- Variables cannot be redeclared across cells
- The notebook forms a directed acyclic graph (DAG)
- The last expression in a cell is automatically displayed
- UI elements are reactive and update the notebook automatically

## Code Requirements

1. All code must be complete and runnable
2. Follow consistent coding style throughout
3. Include descriptive variable names and helpful comments
4. Import all modules in the first cell, always including \`import marimo as mo\`
5. Never redeclare variables across cells
6. Ensure no cycles in notebook dependency graph
7. The last expression in a cell is automatically displayed, just like in Jupyter notebooks.
8. Don't include comments in markdown cells
9. Don't include comments in SQL cells

## Reactivity

Marimo's reactivity means:

- When a variable changes, all cells that use that variable automatically re-execute
- UI elements trigger updates when their values change without explicit callbacks
- UI element values are accessed through \`.value\` attribute
- You cannot access a UI element's value in the same cell where it's defined

## Best Practices

<data_handling>

- Use pandas for data manipulation
- Implement proper data validation
- Handle missing values appropriately
- Use efficient data structures
- A variable in the last expression of a cell is automatically displayed as a table
</data_handling>

<visualization>
- For matplotlib: use plt.gca() as the last expression instead of plt.show()
- For plotly: return the figure object directly
- For altair: return the chart object directly
- Include proper labels, titles, and color schemes
- Make visualizations interactive where appropriate
</visualization>

<ui_elements>

- Access UI element values with .value attribute (e.g., slider.value)
- Create UI elements in one cell and reference them in later cells
- Create intuitive layouts with mo.hstack(), mo.vstack(), and mo.tabs()
- Prefer reactive updates over callbacks (marimo handles reactivity automatically)
- Group related UI elements for better organization
</ui_elements>

<data_sources>

- Prefer GitHub-hosted datasets (e.g., raw.githubusercontent.com)
- Use CORS proxy for external URLs: <https://corsproxy.marimo.app/><url>
- Implement proper error handling for data loading
- Consider using \`vega_datasets\` for common example datasets
</data_sources>

<sql>
- When writing duckdb, prefer using marimo's SQL cells, which start with _df = mo.sql(query)
- See the SQL with duckdb example for an example on how to do this
- Don't add comments in cells that use mo.sql()
- Consider using \`vega_datasets\` for common example datasets
</sql>

## Troubleshooting

Common issues and solutions:

- Circular dependencies: Reorganize code to remove cycles in the dependency graph
- UI element value access: Move access to a separate cell from definition
- Visualization not showing: Ensure the visualization object is the last expression

## Available UI elements

- \`mo.ui.altair_chart(altair_chart)\`
- \`mo.ui.button(value=None, kind='primary')\`
- \`mo.ui.run_button(label=None, tooltip=None, kind='primary')\`
- \`mo.ui.checkbox(label='', value=False)\`
- \`mo.ui.date(value=None, label=None, full_width=False)\`
- \`mo.ui.dropdown(options, value=None, label=None, full_width=False)\`
- \`mo.ui.file(label='', multiple=False, full_width=False)\`
- \`mo.ui.number(value=None, label=None, full_width=False)\`
- \`mo.ui.radio(options, value=None, label=None, full_width=False)\`
- \`mo.ui.refresh(options: List[str], default_interval: str)\`
- \`mo.ui.slider(start, stop, value=None, label=None, full_width=False, step=None)\`
- \`mo.ui.range_slider(start, stop, value=None, label=None, full_width=False, step=None)\`
- \`mo.ui.table(data, columns=None, on_select=None, sortable=True, filterable=True)\`
- \`mo.ui.text(value='', label=None, full_width=False)\`
- \`mo.ui.text_area(value='', label=None, full_width=False)\`
- \`mo.ui.data_explorer(df)\`
- \`mo.ui.dataframe(df)\`
- \`mo.ui.plotly(plotly_figure)\`
- \`mo.ui.tabs(elements: dict[str, mo.ui.Element])\`
- \`mo.ui.array(elements: list[mo.ui.Element])\`
- \`mo.ui.form(element: mo.ui.Element, label='', bordered=True)\`

## Layout and utility functions

- \`mo.md(text)\` - display markdown
- \`mo.stop(predicate, output=None)\` - stop execution conditionally
- \`mo.Html(html)\` - display HTML
- \`mo.image(image)\` - display an image
- \`mo.hstack(elements)\` - stack elements horizontally
- \`mo.vstack(elements)\` - stack elements vertically
- \`mo.tabs(elements)\` - create a tabbed interface

## Examples

<example title="Basic UI with reactivity">
# Cell 1
import marimo as mo
import matplotlib.pyplot as plt
import numpy as np

# Cell 2

# Create a slider and display it

n_points = mo.ui.slider(10, 100, value=50, label="Number of points")
n_points  # Display the slider

# Cell 3

# Generate random data based on slider value

# This cell automatically re-executes when n_points.value changes

x = np.random.rand(n_points.value)
y = np.random.rand(n_points.value)

plt.figure(figsize=(8, 6))
plt.scatter(x, y, alpha=0.7)
plt.title(f"Scatter plot with {n_points.value} points")
plt.xlabel("X axis")
plt.ylabel("Y axis")
plt.gca()  # Return the current axes to display the plot
</example>

<example title="Data explorer">
# Cell 1
import marimo as mo
import pandas as pd
from vega_datasets import data

# Cell 2

# Load and display dataset with interactive explorer

cars_df = data.cars()
mo.ui.data_explorer(cars_df)
</example>

<example title="Multiple UI elements">
# Cell 1
import marimo as mo
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

# Cell 2

# Load dataset

iris = sns.load_dataset('iris')

# Cell 3

# Create UI elements

species_selector = mo.ui.dropdown(
    options=["All"] + iris["species"].unique().tolist(),
    value="All",
    label="Species"
)
x_feature = mo.ui.dropdown(
    options=iris.select_dtypes('number').columns.tolist(),
    value="sepal_length",
    label="X Feature"
)
y_feature = mo.ui.dropdown(
    options=iris.select_dtypes('number').columns.tolist(),
    value="sepal_width",
    label="Y Feature"
)

# Display UI elements in a horizontal stack

mo.hstack([species_selector, x_feature, y_feature])

# Cell 4

# Filter data based on selection

filtered_data = iris if species_selector.value == "All" else iris[iris["species"] == species_selector.value]

# Create visualization based on UI selections

plt.figure(figsize=(10, 6))
sns.scatterplot(
    data=filtered_data,
    x=x_feature.value,
    y=y_feature.value,
    hue="species"
)
plt.title(f"{y_feature.value} vs {x_feature.value}")
plt.gca()
</example>

<example title="Interactive chart with Altair">
# Cell 1
import marimo as mo
import altair as alt
import pandas as pd

# Cell 2

# Load dataset

cars_df = pd.read_csv('<https://raw.githubusercontent.com/vega/vega-datasets/master/data/cars.json>')
_chart = alt.Chart(cars_df).mark_point().encode(
    x='Horsepower',
    y='Miles_per_Gallon',
    color='Origin',
)

chart = mo.ui.altair_chart(_chart)
chart

# Cell 3

# Display the selection

chart.value
</example>

<example title="Run Button Example">
# Cell 1
import marimo as mo

# Cell 2

first_button = mo.ui.run_button(label="Option 1")
second_button = mo.ui.run_button(label="Option 2")
[first_button, second_button]

# Cell 3

if first_button.value:
    print("You chose option 1!")
elif second_button.value:
    print("You chose option 2!")
else:
    print("Click a button!")
</example>

<example title="SQL with duckdb">
# Cell 1
import marimo as mo

# Cell 2

# Load dataset

cars_df = pd.read_csv('<https://raw.githubusercontent.com/vega/vega-datasets/master/data/cars.json>')

# Cell 3

_df = mo.sql("SELECT * from cars_df WHERE Miles_per_Gallon > 20")
</example>

<example title="Writing LaTeX in markdown">
# Cell 1
import marimo as mo

# Cell 2

mo.md(r"""

The quadratic function $f$ is defined as

$$f(x) = x^2.$$
""")
</example>

Anywidget

If you want to generate custom UIs or widgets for marimo then anywidget is the way to go. Because anywidget is a fairly revent project, LLMs have been known to hallucinate when you try to generate custom widgets from scratch. The following prompt contains an example that helps prevent this behaviour and it also points out common failure scenarios that the LLM should avoid.

When writing an anywidget use vanilla javascript in `_esm` and do not forget about `_css`. The css should look bespoke in light mode and dark mode. Keep the css small unless explicitly asked to go the extra mile. When you display the widget it must be wrapped via `widget = mo.ui.anywidget(OriginalAnywidget())`.

<example title="Example anywidget implementation">
import anywidget
import traitlets


class CounterWidget(anywidget.AnyWidget):
    _esm = """
    // Define the main render function
    function render({ model, el }) {
      let count = () => model.get("number");
      let btn = document.createElement("button");
      btn.innerHTML = `count is ${count()}`;
      btn.addEventListener("click", () => {
        model.set("number", count() + 1);
        model.save_changes();
      });
      model.on("change:number", () => {
        btn.innerHTML = `count is ${count()}`;
      });
      el.appendChild(btn);
    }
    // Important! We must export at the bottom here!
    export default { render };
    """
    _css = """button{
      font-size: 14px;
    }"""
    number = traitlets.Int(0).tag(sync=True)

widget = mo.ui.anywidget(CounterWidget())
widget

# Grabbing the widget from another cell, `.value` is a dictionary.
print(widget.value["number"])
</example>

When sharing the anywidget, keep the example minimal. No need to combine it with marimo ui elements unless explicitly stated to do so.