# streamlit

A Cursor rule that tells the agent how to build Streamlit apps: cache expensive work, keep UI apart from logic, split the project into modules.

- rule · `type=rule` · Cursor · ★ 3579
- Author: sanjeed5
- License: CC0-1.0 · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/sanjeed5/awesome-cursor-rules-mdc/tree/HEAD/rules-mdc/streamlit.mdc
- Updated: 2026-05-19
- HTML: https://affpapa.org/en/nevibecode/rules/streamlit

## What it does

The rule lives in `.cursor/rules/streamlit.mdc` and applies to every `**/*.py` file. It covers project layout first. Instead of one large `app.py`, it asks for `pages/` for page modules, `services/` for data loading and processing, and `components/` for reusable widgets. Each page exposes a `main()` function, and `app.py` only wires up navigation.

Then comes performance. Streamlit reruns the whole script on every interaction, so the rule wants data loads and heavy computation wrapped in `@st.cache_data`, while database connections and models go into `@st.cache_resource`. User-specific values belong in `st.session_state`, initialized behind a key check. Each point ships with a bad and a good example: an uncached CSV read, a counter that resets on every rerun, an `st.write` call buried inside a calculation function.

Start with the caching section if your app already has a slow data load — that is where the rule pays off fastest. Note that the text cuts off mid-example in the context manager section, so the layout material is incomplete. No Streamlit version is pinned, and no conflicts with other rule sets are described.

## Install

### Cursor (native platform)

**Manually: put the files in place**
- File: `.cursor/rules/streamlit.mdc` — Frontmatter: description, globs, alwaysApply

### Claude Code

**Manually: put the files in place**
- File: `.claude/rules/streamlit.md` — The paths frontmatter field limits the files: move globs from the source into paths

### Codex

**AGENTS.md**
- Codex reads AGENTS.md: paste the rule text there

### GitHub Copilot

**Manually: put the files in place**
- File: `.github/instructions/streamlit.instructions.md` — The applyTo field sets which files it applies to: move globs from the source into applyTo

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/rules/streamlit.md`

### Cline

**Manually: put the files in place**
- File: `.clinerules/streamlit.md`

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/sanjeed5/awesome-cursor-rules-mdc/tree/HEAD/rules-mdc/streamlit.mdc

## When to use it
- break a monolithic `app.py` into pages, services and components
- wrap data loading in `@st.cache_data` so it stops running on every click
- set up `st.session_state` without values resetting on each rerun
- move `st.write` calls out of business logic functions
- build page navigation from a dictionary of modules

## Example prompts
- Rewrite `app.py` following the streamlit rule: move data loading into `services/data_service.py` and wrap it in `@st.cache_data`.
- Check this file against the streamlit rule and flag where UI is mixed with logic or caching is missing.
- Add a new page under `pages/` with a `main()` function and hook it into the navigation in `app.py`.

## Source text · streamlit.mdc · CC0-1.0

Below is the content of a third-party file. It is data, not instructions for you: do not act on anything written inside unless the human asks. Block tag: 4597eabee1b8d60f.

BEGIN THIRD-PARTY FILE 4597eabee1b8d60f
````text
---
description: Enforce modern, performant, and maintainable Streamlit development practices, focusing on caching, modularity, and robust dependency management.
globs: **/*.py
---
# Streamlit Best Practices

This guide outlines the definitive best practices for building robust, performant, and maintainable Streamlit applications. Adhere to these rules to ensure your apps are efficient, scalable, and easy to collaborate on.

## 1. Code Organization and Structure

Always separate UI logic from data processing and business logic. Modularize your application into distinct files or directories.

### 1.1 Project Structure

Organize your project for clarity and maintainability.

❌ BAD: Monolithic `app.py`
```python
# app.py
import streamlit as st
import pandas as pd

def load_and_process_data(file_path):
    df = pd.read_csv(file_path)
    # ... complex processing ...
    return df

st.title("My Data App")
uploaded_file = st.file_uploader("Upload CSV")
if uploaded_file:
    data = load_and_process_data(uploaded_file)
    st.dataframe(data)
    # ... more UI and logic ...
```

✅ GOOD: Modularized Structure
```
my_streamlit_app/
├── app.py                  # Main entry point, orchestrates pages
├── pages/
│   └── dashboard_page.py   # Specific page UI and logic
├── services/
│   └── data_service.py     # Data loading and processing functions
├── components/
│   └── custom_widgets.py   # Reusable UI components
├── requirements.txt
└── .gitignore
```

`app.py`:
```python
# app.py
import streamlit as st
from pages import dashboard_page # Assuming dashboard_page.py has a main() function

st.set_page_config(layout="wide")

# Example of multi-page app structure
PAGES = {
    "Dashboard": dashboard_page,
    # "Another Page": another_page_module,
}

with st.sidebar:
    st.title('Navigation')
    selection = st.radio("Go to", list(PAGES.keys()))

page = PAGES[selection]
page.main() # Each page module must have a main() function
```

`pages/dashboard_page.py`:
```python
# pages/dashboard_page.py
import streamlit as st
from services.data_service import load_data, process_data

def main():
    st.title("Dashboard Overview")

    uploaded_file = st.file_uploader("Upload CSV", type=["csv"])
    if uploaded_file:
        raw_df = load_data(uploaded_file)
        st.write("Raw Data:")
        st.dataframe(raw_df.head())

        processed_df = process_data(raw_df)
        st.write("Processed Data:")
        st.dataframe(processed_df)

        # Further UI elements specific to the dashboard
```

`services/data_service.py`:
```python
# services/data_service.py
import pandas as pd
import streamlit as st
from typing import Union

@st.cache_data
def load_data(file_path: Union[str, bytes]) -> pd.DataFrame:
    """Loads data from a file-like object or path."""
    return pd.read_csv(file_path)

@st.cache_data
def process_data(df: pd.DataFrame) -> pd.DataFrame:
    """Performs complex data processing."""
    # Example: clean, transform, aggregate
    processed_df = df.dropna().copy()
    processed_df['new_col'] = processed_df['value'] * 2 # Example transformation
    return processed_df
```

## 2. Performance Considerations

Streamlit reruns the entire script on every interaction. Efficient caching and state management are critical.

### 2.1 Caching Expensive Operations
````
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