great-tables-great-tables-with-streamlit

Sub-skill of great-tables: Great Tables with Streamlit (+1).

5 stars

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

$curl -o ~/.claude/skills/great-tables-with-streamlit/SKILL.md --create-dirs "https://raw.githubusercontent.com/vamseeachanta/workspace-hub/main/.agents/skills/_archive/data/analysis/great-tables/great-tables-with-streamlit/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/great-tables-with-streamlit/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How great-tables-great-tables-with-streamlit Compares

Feature / Agentgreat-tables-great-tables-with-streamlitStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/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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