Streamlit Skill Skill | Agent Skills

Streamlit Skill

Comprehensive assistance with Streamlit development, including API reference, tutorials, deployment guides, and best practices.

When to Use This Skill

This skill should be triggered when:

Building web apps with Python for data science, ML/AI, or analytics.
Creating dashboards with interactive visualizations and real-time data.
Developing data apps that need rapid prototyping and deployment.
Implementing widgets like buttons, sliders, file uploaders, or chat interfaces.
Debugging Streamlit apps and troubleshooting common issues (e.g., state management, caching).
Optimizing performance with @st.cache_data, @st.cache_resource, and @st.fragment.

Key Concepts

Core Architecture

Script-based execution: Streamlit apps run as Python scripts that rerun from top to bottom on every user interaction. Session State: Persistent data storage across reruns using st.session_state. Caching: Use @st.cache_data for data operations and @st.cache_resource for expensive resources like ML models or database connections.

Quick Reference

Example: Basic Display & Interactive Widgets

import streamlit as st
import pandas as pd

# Basic display
st.title("Streamlit Dashboard")
st.write("Hello, World!")

# Interactive widgets
if 'count' not in st.session_state:
    st.session_state.count = 0

if st.button('Increment'):
    st.session_state.count += 1

st.write(f'Count: {st.session_state.count}')

# Displaying data
df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})
st.dataframe(df)

Debugging & Troubleshooting

Check Session State: Ensure keys exist before accessing them.
Caching Issues: Clear cache with st.cache_data.clear() or st.cache_resource.clear() if data is stale.
Execution Flow: Remember that code runs top-to-bottom on every interaction.
Secrets Management: Use st.secrets for API keys and database credentials.

Agent Skills: Streamlit Skill

Install this agent skill to your local