Data Analysis

This is a solid Polars-based workflow for CSV, JSON, and Parquet analysis. It includes reference docs for loading, transformations, aggregations, time series, and visualization, plus ready-to-use exploration scripts. The iteration checkpoints are genuinely helpful: they prompt you to show the user data shape and samples after loading, summary stats during exploration, before/after comparisons during transformation, then findings and charts. The quick start example is clean, and the lazy evaluation guidance is the kind of performance tip that actually matters when you're working with files that don't fit comfortably in memory. If you do regular data wrangling, this gives you patterns that work without reinventing the wheel each time.

skills.sh installs: 208
GitHub stars: 388
last updated: Jun 3, 2026

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Install

$ npx -y skills add artificialanalysis/stirrup --skill data_analysis --agent claude-code

Installs into .claude/skills of the current project.

Files

Data Analysis Skill

Comprehensive data analysis toolkit using Polars - a blazingly fast DataFrame library. This skill provides instructions, reference documentation, and ready-to-use scripts for common data analysis tasks.

Iteration Checkpoints

Step	What to Present	User Input Type
Data Loading	Shape, columns, sample rows	"Is this the right data?"
Data Exploration	Summary stats, data quality issues	"Any columns to focus on?"
Transformation	Before/after comparison	"Does this transformation look correct?"
Analysis	Key findings, charts	"Should I dig deeper into anything?"
Export	Output preview	"Ready to save, or any changes?"

Quick Start

import polars as pl
from polars import col

# Load data
df = pl.read_csv("data.csv")

# Explore
print(df.shape, df.schema)
df.describe()

# Transform and analyze
result = (
    df.filter(col("value") > 0)
    .group_by("category")
    .agg(col("value").sum().alias("total"))
    .sort("total", descending=True)
)

# Export
result.write_csv("output.csv")

When to Use This Skill

SKILL.md on GitHub

Details

Repository: github.com/artificialanalysis/stirrup
First seen: Jun 3, 2026