Agent skill
streamlit
Build interactive data applications and dashboards with pure Python - no frontend experience required
Install this agent skill to your Project
npx add-skill https://github.com/vamseeachanta/workspace-hub/tree/main/.claude/skills/data/analysis/streamlit
SKILL.md
Streamlit
When to Use This Skill
USE Streamlit when:
- Rapid prototyping - Need to build a data app quickly
- Internal tools - Creating tools for your team
- Data exploration - Interactive exploration of datasets
- Demo applications - Showcasing data science projects
- ML model demos - Building interfaces for model inference
- Simple dashboards - Quick insights without complex setup
- Python-only development - No JavaScript/frontend knowledge required
DON'T USE Streamlit when:
- Complex interactivity - Need fine-grained callback control (use Dash)
- Enterprise deployment - Require advanced authentication/scaling (use Dash Enterprise)
- Custom components - Heavy custom JavaScript requirements
- High-traffic production - Thousands of concurrent users
- Real-time streaming - Sub-second update requirements
Prerequisites
# Basic installation
pip install streamlit
# With common extras
pip install streamlit plotly pandas polars
# Using uv (recommended)
uv pip install streamlit plotly pandas polars altair
# Verify installation
streamlit hello
Complete Examples
Example 1: Sales Dashboard
import streamlit as st
import pandas as pd
import polars as pl
import plotly.express as px
import plotly.graph_objects as go
from datetime import datetime, timedelta
# Page config
st.set_page_config(
*See sub-skills for full details.*
### Example 2: Data Explorer Tool
```python
import streamlit as st
import pandas as pd
import polars as pl
import plotly.express as px
st.set_page_config(page_title="Data Explorer", page_icon="🔍", layout="wide")
st.title("🔍 Interactive Data Explorer")
*See sub-skills for full details.*
### Example 3: ML Model Demo
```python
import streamlit as st
import pandas as pd
import numpy as np
import plotly.express as px
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, classification_report
st.set_page_config(page_title="ML Demo", page_icon="🤖", layout="wide")
*See sub-skills for full details.*
## Deployment Patterns
### Streamlit Cloud Deployment
```yaml
# requirements.txt
streamlit>=1.32.0
pandas>=2.0.0
polars>=0.20.0
plotly>=5.18.0
numpy>=1.24.0
*See sub-skills for full details.*
### Docker Deployment
```dockerfile
# Dockerfile
FROM python:3.11-slim
WORKDIR /app
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt
COPY . .
*See sub-skills for full details.*
## Version History
- **1.0.0** (2026-01-17): Initial release
- Basic app structure and widgets
- Layout and organization patterns
- Data visualization integration
- Caching strategies
- Session state management
- Multi-page applications
- Complete dashboard examples
- Deployment patterns
- Best practices and troubleshooting
## Resources
- **Official Docs**: https://docs.streamlit.io/
- **Gallery**: https://streamlit.io/gallery
- **Components**: https://streamlit.io/components
- **Cloud**: https://streamlit.io/cloud
- **GitHub**: https://github.com/streamlit/streamlit
---
**Build beautiful data apps with pure Python - no frontend experience required!**
## Sub-Skills
- [1. Basic Application Structure (+1)](1-basic-application-structure/SKILL.md)
- [3. Layout and Organization](3-layout-and-organization/SKILL.md)
- [4. Data Visualization (+1)](4-data-visualization/SKILL.md)
- [6. Session State (+1)](6-session-state/SKILL.md)
- [8. Advanced Features](8-advanced-features/SKILL.md)
- [1. Use Caching Appropriately (+3)](1-use-caching-appropriately/SKILL.md)
- [Common Issues](common-issues/SKILL.md)
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