streamlit▌

Routing skill for all Streamlit development tasks: app creation, editing, debugging, styling, optimization, and deployment. \n \n Routes to specialized sub-skills based on task type: performance optimization, dashboard building, design improvement, widget selection, theming, layouts, data display, multi-page apps, session state, chat UI, custom components, and Snowflake integration \n Includes a workflow for locating Streamlit source files, identifying entry points ( streamlit_app.py , app.py ),

Build and deploy Streamlit apps natively in Snowflake with Snowpark integration and Marketplace publishing. \n \n Supports two runtime options: Warehouse Runtime (personal instances, Anaconda packages) and Container Runtime (shared instances, PyPI packages, significantly lower cost) \n Includes Snowpark session integration for native data access, multi-page app structure, and caller's rights connections (v1.53.0+) for per-user data isolation \n Prevents 14 documented errors including package cha

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.

Streamlit is a Python framework for rapidly building and deploying interactive web applications for data science and machine learning. Create beautiful web apps with just Python - no frontend development experience required. Apps automatically update in real-time as code changes.

This starts both backend (Streamlit/Python) and frontend (Vite/React) with hot-reload. The app URL is printed on startup (default http://localhost:3001; 3000 is reserved for manual make frontend-dev; it may use 3002+ if other debug sessions are running). Avoid pinning VITE_PORT unless you have a specific hard requirement (last resort).