Exploratory Data Analysis

This prompts Claude to walk through the essential phases of exploratory data analysis: profiling your dataset, examining distributions, spotting relationships between variables, catching outliers, and assessing data quality. It's designed for that crucial stage before you start modeling when you need to actually understand what you're working with. The skill comes from aj-geddes' useful-ai-prompts repository, which has a decent following at 245 stars and passes all the security audits. It's straightforward prompt engineering rather than custom tooling, so your mileage depends on how well Claude interprets statistical questions about your specific dataset, but it gives you a solid framework to avoid jumping straight into analysis blind.

GitHub stars: 317
last updated: Aug 21, 2026

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Install

$ npx -y skills add aj-geddes/useful-ai-prompts --skill 'Exploratory Data Analysis' --agent claude-code

Installs into .claude/skills of the current project.

Files

Exploratory Data Analysis (EDA)

Overview

Exploratory Data Analysis (EDA) is the critical first step in data science projects, systematically examining datasets to understand their characteristics, identify patterns, and assess data quality before formal modeling.

Core Concepts

Data Profiling: Understanding basic statistics and data types
Distribution Analysis: Examining how variables are distributed
Relationship Discovery: Identifying patterns between variables
Anomaly Detection: Finding outliers and unusual patterns
Data Quality Assessment: Evaluating completeness and consistency

When to Use

Starting a new dataset analysis
Understanding data before modeling
Identifying data quality issues
Generating hypotheses for testing
Communicating insights to stakeholders

Implementation with Python

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

# Load and explore data
df = pd.read_csv('customer_data.csv')

SKILL.md on GitHub