great-tables-great-tables-with-streamlit
Sub-skill of great-tables: Great Tables with Streamlit (+1).
Best use case
great-tables-great-tables-with-streamlit is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Sub-skill of great-tables: Great Tables with Streamlit (+1).
Teams using great-tables-great-tables-with-streamlit should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/great-tables-with-streamlit/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How great-tables-great-tables-with-streamlit Compares
| Feature / Agent | great-tables-great-tables-with-streamlit | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Sub-skill of great-tables: Great Tables with Streamlit (+1).
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Great Tables with Streamlit (+1)
## Great Tables with Streamlit
```python
import streamlit as st
from great_tables import GT
import pandas as pd
st.set_page_config(page_title="Table Demo", layout="wide")
st.title("Great Tables in Streamlit")
# Sample data
df = pd.DataFrame({
"Product": ["Widget A", "Widget B", "Gadget X"],
"Price": [29.99, 49.99, 19.99],
"Stock": [150, 85, 200],
"Rating": [4.5, 4.2, 4.8]
})
# Create table
table = (
GT(df)
.tab_header(title="Product Catalog")
.fmt_currency(columns="Price", currency="USD")
.fmt_number(columns="Rating", decimals=1)
)
# Display in Streamlit
st.html(table.as_raw_html())
```
## Great Tables with Polars
```python
from great_tables import GT
import polars as pl
# Create Polars DataFrame
df_polars = pl.DataFrame({
"name": ["Alice", "Bob", "Charlie"],
"score": [95, 87, 92],
"grade": ["A", "B+", "A-"]
})
# Convert to pandas for Great Tables
df_pandas = df_polars.to_pandas()
# Create table
table = (
GT(df_pandas)
.tab_header(title="Student Scores")
.cols_label(
name="Student",
score="Score",
grade="Grade"
)
)
table.save("polars_table.html")
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