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exploratory-data-analysis

Identify data quality gaps and descriptive insights across CSV, Parquet, Excel, and database tables.

Updated Mar 8, 2026
Search Tags:#visualization#data-analysis#eda#data-quality#outliers#missing-values
One-click install
npx skills add https://github.com/Phife726/ds_agent --skill exploratory-data-analysis-phife726
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: exploratory-data-analysis
Source: https://github.com/Phife726/ds_agent/tree/main/exploratory-data-analysis
Command: npx skills add https://github.com/Phife726/ds_agent --skill exploratory-data-analysis-phife726

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Every dataset tells a story. EDA is how you read the first chapter before writing the rest. This skill provides a systematic, repeatable methodology for initial data exploration that ensures nothing gets missed.

Core principle: UNDERSTAND the data completely before any modeling, transformation, or feature engineering. Skipping EDA leads to garbage-in, garbage-out.

Core Features & Use Cases

  • Phase-driven exploration: shape, missing values, summary statistics, outliers, distributions, correlations, and target analysis.
  • Visualization and reporting: generate artifacts (plots and summary reports) to communicate data quality and insights.
  • Practical scenarios: onboarding to a new dataset, auditing data quality before pipelines, detecting data drift, validating before modeling.

Quick Start

Ingest a new dataset and follow the seven EDA phases to produce a full exploratory data analysis report.

Frequently Asked Questions about exploratory-data-analysis

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

FAQPage Schema
What is exploratory data analysis and why do I need it before modeling?▼

EDA systematically assesses dataset shape, missingness, distributions, and outliers to prevent garbage-in, garbage-out modeling. It reveals data quality gaps and descriptive insights through structured phases before any feature engineering begins.

How do I check data quality and missing values in a CSV or Parquet dataset?▼

Apply a systematic EDA workflow across CSV, Parquet, Excel, and database tables to identify missing values and data quality gaps. The seven-phase protocol evaluates shape, missingness, and outliers, producing a structured data-quality report.

Can I use this EDA workflow to analyze database tables and Excel files?▼

Yes, the EDA workflow applies directly to database tables, Excel, CSV, and Parquet files. It assesses shape, missingness, distributions, and relationships across these formats to generate summary artifacts and data-quality reports.

What is the best way to detect outliers and data drift in a new dataset?▼

Use a phase-driven EDA protocol to detect outliers and data drift by evaluating distributions and correlations across your dataset. This systematic approach generates plots and summary reports to validate data quality before pipelines or modeling.

What artifacts and reports does an exploratory data analysis workflow generate?▼

An EDA workflow generates plots, summary statistics, and a structured data-quality report. These artifacts communicate data quality gaps, descriptive insights, and potential target signals discovered across the seven exploration phases.

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