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eda-checklist

Guide structured EDA checklist checks for tabular dataset quality issues.

14|2|Updated Feb 24, 2026
Search Tags:#checklist#eda#data-quality#data-cleaning#exploratory-data-analysis#tabular-data
One-click install
npx skills add https://github.com/andikarachman/data-science-plugin --skill eda-checklist
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: eda-checklist
Source: https://github.com/andikarachman/data-science-plugin/tree/main/skills/eda-checklist
Command: npx skills add https://github.com/andikarachman/data-science-plugin --skill eda-checklist

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Provides a structured, repeatable checklist to guide exploratory data analysis on tabular datasets, ensuring critical quality checks are not overlooked.

Core Features & Use Cases

  • Systematic checks for shape, missing data, duplicates, distributions, and correlations to accelerate EDA.
  • Checklists for each facet (structure, data quality, numerical and categorical distributions, temporal patterns, and red flags) to standardize analyses.
  • Use Case: When starting a new tabular dataset, the checklist helps ensure you assess data quality before modeling.

Quick Start

Run the EDA checklist at the start of every dataset analysis to verify data readiness and identify key quality issues.

Frequently Asked Questions about eda-checklist

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

FAQPage Schema
What is exploratory data analysis for tabular data and when do I need it?▼

Exploratory data analysis is the initial investigation of tabular datasets to verify shape, missingness, distributions, and potential red flags before modeling. You need it when starting a new dataset to ensure critical data quality checks are not overlooked.

How do I check data quality and identify missing values in a new dataset?▼

Check data quality by running a structured exploratory data analysis checklist that systematically evaluates shape, missing data, duplicates, distributions, and correlations to identify red flags and verify data readiness before modeling.

What's the best way to standardize exploratory data analysis across different domains?▼

Standardize exploratory data analysis by applying a repeatable checklist that covers structure, data quality, numerical and categorical distributions, temporal patterns, and red flags to ensure consistent evaluations across domains and datasets.

Can I use a structured checklist for data cleaning on any tabular dataset?▼

Yes, a structured exploratory data analysis checklist applies to initial data investigations on new tabular datasets across domains, guiding you through step-by-step evaluations of structure, distributions, and feature relationships within a repeatable workflow.

What red flags should I look for during exploratory data analysis before modeling?▼

During exploratory data analysis, look for red flags including missing data, duplicates, abnormal distributions, and unexpected feature relationships by running explicit checks per category to verify data readiness before modeling.

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