Do I need to clean my data before using this skill?

Yes, it is a mandatory prerequisite to have your data imported and cleaned using the importing-data and cleaning-data skills before starting the exploratory analysis process.

What are the three vectors of pattern discovery used?

The skill explores Temporal Patterns (time-based trends), Segmentation Patterns (groups and categories), and Relationship Patterns (correlations and associations).

What is the primary purpose of the exploratory-analysis skill?

It is designed to help you systematically explore unfamiliar datasets to discover patterns and generate hypotheses when you do not yet have specific questions or claims to test.

How does this differ from hypothesis-testing?

Hypothesis-testing validates a specific claim or question, whereas exploratory analysis is open-ended discovery intended to help you identify what questions you should be asking in the first place.

Exploratory Data Analysis (EDA)

Name: Exploratory Data Analysis (EDA)
Author: tilmon-engineering

bytilmon-engineering

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Data Science & ML

Conducts systematic exploratory data analysis to uncover hidden patterns, anomalies, and actionable insights in unfamiliar datasets.

About

The exploratory-analysis skill provides a structured framework for Claude to investigate new datasets without prior hypotheses. By following a rigorous five-phase process—from data familiarization to question formulation—it helps users transition from simply viewing raw data to identifying meaningful business trends and outliers. It utilizes SQL queries and text-based visualizations to catalog temporal, segmentation, and relationship patterns, ensuring a comprehensive understanding of the data landscape before moving into deeper hypothesis testing.

Key Features

Structured insight generation with specific confidence level assessments
2 GitHub stars
Systematic 5-phase EDA workflow for consistent and repeatable results
Automated question formulation to bridge the gap between exploration and deep-dive analysis
Dedicated anomaly investigation to distinguish data quality issues from real-world phenomena
Multi-vector pattern discovery across temporal, segmentation, and relationship dimensions

Use Cases

Initial exploration of a newly imported dataset or legacy database
Identifying unexpected data anomalies, seasonal trends, or hidden correlations
Generating actionable business hypotheses from raw, unstructured information

About

Key Features

Structured insight generation with specific confidence level assessments
2 GitHub stars
Systematic 5-phase EDA workflow for consistent and repeatable results
Automated question formulation to bridge the gap between exploration and deep-dive analysis
Dedicated anomaly investigation to distinguish data quality issues from real-world phenomena
Multi-vector pattern discovery across temporal, segmentation, and relationship dimensions

Use Cases

Initial exploration of a newly imported dataset or legacy database
Identifying unexpected data anomalies, seasonal trends, or hidden correlations
Generating actionable business hypotheses from raw, unstructured information