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streamlit-to-marimo — Claude Code skill: convert a Streamlit app

Turns a Streamlit app into a marimo notebook: maps the widgets, state and caching, writes a new file and checks it.

Install into Claude Code · Skills CLI (Vercel) All methods and platforms ↓
npx skills add marimo-team/skills --skill streamlit-to-marimo -a claude-code
Open on GitHub

What streamlit-to-marimo does

The skill reads your Streamlit app to see which widgets, layout and state handling it uses, then builds a separate marimo notebook next to it. Your original file stays untouched. Dependencies such as pandas, plotly and altair carry over, while streamlit is swapped for marimo.

The work is driven by mapping tables. st.slider() becomes mo.ui.slider(), st.selectbox() becomes mo.ui.dropdown(), and a DataFrame just needs to be the last expression in a cell. A widget is assigned to a variable and read through .value; any cell referencing it re-runs on interaction. Callbacks and st.rerun() go away, because marimo runs a reactive cell DAG. Caching moves from @st.cache_data to @mo.cache and from @st.cache_resource to @mo.persistent_cache.

A few things resist a straight port. Streamlit custom components don't work in marimo, so the skill tells you to talk through an anywidget replacement first. Multi-page apps get two options: routes inside one notebook, or a folder of notebooks served with marimo run folder/. At the end the skill runs uvx marimo check and fixes what it finds, so uv has to be available.

What it helps with

  • port a Streamlit app to a marimo notebook without rewriting widgets by hand
  • drop callbacks and st.rerun() in favour of reactive cells
  • move caching from @st.cache_data and @st.cache_resource to marimo decorators
  • split a multi-page app into routes or a notebook folder
  • validate the converted notebook with uvx marimo check

Example prompts for the agent

  • Convert app.py from Streamlit into a marimo notebook, leave the original alone
  • Swap the widgets and caching in this app for marimo equivalents and run uvx marimo check
  • My Streamlit app has several pages — show me how to build the same thing in one marimo notebook

How to install streamlit-to-marimo

1Skills CLI (Vercel)

npx skills add marimo-team/skills --skill streamlit-to-marimo -a claude-code

2Manually: put the files in place

~/.claude/skills/streamlit-to-marimo/SKILL.md

Copy the whole skill folder; for one project use .claude/skills/streamlit-to-marimo/

Step by step, with commands for each platform: How to install skills in Claude Code: five ways

Questions about streamlit-to-marimo

How do I install streamlit-to-marimo in Claude?

Run npx skills add marimo-team/skills --skill streamlit-to-marimo -a claude-code. The skill lands in ~/.claude/skills/streamlit-to-marimo/SKILL.md.

What does streamlit-to-marimo do?

It writes a new marimo notebook from your Streamlit code, carrying over widgets, layout, state and caching, then checks the result with uvx marimo check.

Do Streamlit custom components carry over?

No, they are not compatible with marimo. The skill suggests discussing an anywidget replacement before doing any work on it.

Source text

SKILL.md · Apache-2.0
---
name: streamlit-to-marimo
description: Convert a Streamlit app to a marimo notebook
---

# Converting Streamlit Apps to Marimo

For general marimo notebook conventions (cell structure, PEP 723 metadata, output rendering, `marimo check`, variable naming, etc.), refer to the `marimo-notebook` skill. This skill focuses specifically on **mapping Streamlit concepts to marimo equivalents**.

## Steps

1. **Read the Streamlit app** to understand its widgets, layout, and state management.

2. **Create a new marimo notebook** following the `marimo-notebook` skill conventions. Add all dependencies the Streamlit app uses (pandas, plotly, altair, etc.) — but replace `streamlit` with `marimo`. You should not overwrite the original file. 

3. **Map Streamlit components to marimo equivalents** using the reference tables below. Key principles:
   - UI elements are **assigned to variables** and their current value is accessed via `.value`.
   - Cells that reference a UI element automatically re-run when the user interacts with it — no callbacks needed.

4. **Handle conceptual differences** in execution model, state, and caching (see below).

5. **Run `uvx marimo check`** on the result and fix any issues.

## Widget Mapping Reference

### Input Widgets

| Streamlit | marimo | Notes |
|-----------|--------|-------|
| `st.slider()` | `mo.ui.slider()` | |
| `st.select_slider()` | `mo.ui.slider(steps=[...])` | Pass discrete values via `steps` |
| `st.text_input()` | `mo.ui.text()` | |
| `st.text_area()` | `mo.ui.text_area()` | |
| `st.number_input()` | `mo.ui.number()` | |
| `st.checkbox()` | `mo.ui.checkbox()` | |
| `st.toggle()` | `mo.ui.switch()` | |
| `st.radio()` | `mo.ui.radio()` | |
| `st.selectbox()` | `mo.ui.dropdown()` | |
| `st.multiselect()` | `mo.ui.multiselect()` | |
| `st.date_input()` | `mo.ui.date()` | |
| `st.time_input()` | `mo.ui.text()` | No dedicated time widget |
| `st.file_uploader()` | `mo.ui.file()` | Use `.contents()` to read bytes |
| `st.color_picker()` | `mo.ui.text(value="#000000")` | No dedicated color picker |
| `st.button()` | `mo.ui.button()` or `mo.ui.run_button()` | Use `run_button` for triggering expensive computations |
| `st.download_button()` | `mo.download()` | Returns a download link element |
| `st.form()` + `st.form_submit_button()` | `mo.ui.form(element)` | Wraps any element so its value only updates on submit |

### Display Elements

| Streamlit | marimo | Notes |
|-----------|--------|-------|
| `st.write()` | `mo.md()` or last expression | |
| `st.markdown()` | `mo.md()` | Supports f-strings: `mo.md(f"Value: {x.value}")` |
| `st.latex()` | `mo.md(r"$...$")` | marimo uses KaTeX; see `references/latex.md` |
| `st.code()` | `mo.md("```python\n...\n```")` | |
| `st.dataframe()` | `df` (last expression) | DataFrames render as interactive marimo widgets natively; use `mo.ui.dataframe(df)` only for no-code transformations |
| `st.table()` | `df` (last expression) | Use `mo.ui.table(df)` if you need row selection |
| `st.metric()` | `mo.stat()` | |
| `st.json()` | `mo.json()` or `mo.tree()` | `mo.tree()` for interactive collapsible view |
| `st.image()` | `mo.image()` | |
| `st.audio()` | `mo.audio()` | |
| `st.video()` | `mo.video()` | |

### Charts

| Streamlit | marimo | Notes |
|-----------|--------|-------|
| `st.plotly_chart(fig)` | `fig` (last expression) | Use `mo.ui.plotly(fig)` for selections |
| `st.altair_chart(chart)` | `chart` (last expression) | Use `mo.ui.altair_chart(chart)` for selections |
| `st.pyplot(fig)` | `fig` (last expression) | Use `mo.ui.matplotlib(fig)` for interactive matplotlib |

### Layout

| Streamlit | marimo | Notes |
|-----------|--------|-------|
| `st.sidebar` | `mo.sidebar([...])` | Pass a list of elements |
| `st.columns()` | `mo.hstack([...])` | Use `widths=[...]` for column ratios |
| `st.tabs()` | `mo.ui.tabs({...})` | Dict of `{"Tab Name": content}` |
| `st.expander()` | `mo.accordion({...})` | Dict of `{"Title": content}` |
| `st.container()` | `mo.vstack([...])` | |
| `st.empty()` | `mo.output.replace()` | |
| `st.progress()` | `mo.status.progress_bar()` | |
| `st.spinner()` | `mo.status.spinner()` | Context manager |

## Key Conceptual Differences

### Execution Model

Streamlit reruns the **entire script** top-to-bottom on every interaction. Marimo uses a **reactive cell DAG** — only cells that depend on changed variables re-execute.

- No need for `st.rerun()` — reactivity is automatic.
- No need for `st.stop()` — structure cells so downstream cells naturally depend on upstream values.

### State Management

| Streamlit | marimo |
|-----------|--------|
| `st.session_state["key"]` | Regular Python variables between cells |
| Callback functions (`on_change`) | Cells referencing `widget.value` re-run automatically |
| `st.query_params` | `mo.query_params` |

### Caching

| Streamlit | marimo |
|-----------|--------|
| `@st.cache_data` | `@mo.cache` | Caches based on function arguments; marimo-aware |
| `@st.cache_resource` | `@mo.persistent_cache` | Persists across notebook restarts (serializes to disk) |

`@mo.cache` is the primary caching decorator — it works like `functools.cache` but is aware of marimo's reactivity. `@mo.persistent_cache` goes further by persisting results to disk across sessions, useful for expensive computations like model training.

### Multi-Page Apps

Marimo offers two approaches for multi-page Streamlit apps:

- **Single notebook with routing**: Use `mo.routes` with `mo.nav_menu` or `mo.sidebar` to build multiple "pages" (tabs/routes) inside one notebook.
- **Multiple notebooks as a gallery**: Run a folder of notebooks with `marimo run folder/` to serve them as a gallery with navigation.

### Deploying 

marimo features molab to host marimo apps instead of the streamlit community cloud. You can generate an "open in molab" button via the `add-molab-badge` skill. 

### Custom components 

streamlit has a feature for custom components. These are not compatible with marimo. You might be able to generate an equivalent anywidget via the `marimo-anywidget` skill but discuss this with the user before working on that. 

© marimo-team · Apache-2.0 · marimo-team/skills

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