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exploratory-data-analysis

Detect file types and generate Markdown EDA reports for 200+ scientific formats.

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

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires numpy, pandas, biopython, h5py, Pillow, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

Detect file types and perform format-aware exploratory data analysis across 200+ scientific data formats. Use when /ds:eda encounters non-tabular or unfamiliar data files, or when format-specific analysis guidance is needed.

Core Features & Use Cases

  • Automatic file type detection and mapping to reference formats.
  • Format-specific EDA workflows that extract metadata, assess data quality, and generate Markdown reports.
  • Use Case: Analyze a non-tabular data file (e.g., ND2, HDF5, FASTQ) to obtain a structured summary and recommended next steps for downstream analysis.

Quick Start

Provide a data file path to analyze and generate a comprehensive EDA report.

Frequently Asked Questions about exploratory-data-analysis

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

FAQPage Schema
How do I perform exploratory data analysis on non-tabular scientific data formats like HDF5 or ND2?▼

Format-aware exploratory data analysis automatically detects the file type of non-tabular scientific data and extracts metadata, basic statistics, and data quality insights to generate a structured Markdown report with actionable recommendations.

Can I extract metadata and data quality insights from genomics files like FASTQ?▼

Yes, format-aware exploratory data analysis supports over 200 scientific formats including genomics files like FASTQ, extracting structured metadata and assessing data quality to guide downstream analysis.

What's the best way to analyze unfamiliar scientific data files when standard tabular EDA fails?▼

Format-aware exploratory data analysis applies format-specific workflows to unfamiliar or non-tabular scientific data files, detecting the format and producing structured summaries with recommended next steps.

Does this EDA approach work with imaging and spectroscopy datasets across different formats?▼

Format-aware exploratory data analysis supports imaging, genomics, chemistry, and spectroscopy datasets across 200+ formats, applying format-specific workflows to extract metadata and generate quality insights.

How do I get a structured summary and recommended next steps for an unfamiliar data file?▼

Provide the data file path for format-aware exploratory data analysis to detect the format, extract metadata and statistics, assess data quality, and produce a Markdown report with actionable recommendations for downstream analysis.

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