scientific-eda-correlation

Compute descriptive statistics, distribution visuals, and correlation heatmaps for numeric datasets.

3|1|Updated Feb 11, 2026

Search Tags:#pandas #statistics #eda #data-visualization #correlation #exploratory-analysis

Ready to run

npx skills add https://github.com/nahisaho/satori --skill scientific-eda-correlation

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: scientific-eda-correlation
Source: https://github.com/nahisaho/satori/tree/main/src/.github/skills/scientific-eda-correlation
Command: npx skills add https://github.com/nahisaho/satori --skill scientific-eda-correlation

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill provides a streamlined workflow for exploratory data analysis (EDA), enabling quick understanding of distributions, outliers, and variable relationships in a dataset.

Core Features & Use Cases

Compute descriptive statistics (mean, median, quartiles) for numeric variables, with optional grouping.
Visualize distributions (boxplots/violin plots) and generate a correlation heatmap to reveal inter-variable relationships.
Use case: a data scientist receives a new dataset and needs a ready-made pipeline to summarize structure, identify outliers, and spot strong correlations for feature engineering.

Quick Start

Apply this skill to a new dataset to generate descriptive statistics, distribution visuals, and a correlation heatmap in one cohesive workflow.

Frequently Asked Questions about scientific-eda-correlation

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I automate exploratory data analysis for numeric variables in pandas?▼

You can automate exploratory data analysis by applying this skill to compute descriptive statistics, generate distribution visuals like boxplots, and output a correlation heatmap for numeric variables across groups.

What is the best way to visualize correlations and distributions in a new dataset?▼

Visualizing correlations and distributions is best handled by generating a correlation heatmap alongside boxplots or violin plots, allowing you to quickly reveal inter-variable relationships and identify outliers.

Can I generate descriptive statistics and correlation heatmaps for grouped numeric data?▼

Yes, you can generate descriptive statistics such as mean and quartiles with optional grouping, and simultaneously produce a correlation heatmap to analyze relationships across multiple numeric features.

Does this EDA workflow handle multiple numeric features at scale?▼

This EDA workflow satisfies requirements for scalable workflows by handling multiple numeric features, applying descriptive statistics, and generating distribution visuals and correlation outputs in one cohesive pipeline.

When do I need exploratory data analysis for feature engineering?▼

You need exploratory data analysis for feature engineering when you receive a new dataset and must quickly summarize its structure, spot strong correlations, and identify outliers before modeling.