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jimmc414
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exploratory-data-analysis

Analyze scientific data files across 200+ formats and generate markdown reports.

557|98|Updated Nov 7, 2025
Search Tags:#data analysis#bioinformatics#exploratory data analysis#chemistry#file type detection#microscopy#scientific formats
One-click install
npx skills add https://github.com/jimmc414/Kosmos --skill exploratory-data-analysis-jimmc414
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: exploratory-data-analysis
Source: https://github.com/jimmc414/Kosmos/tree/main/kosmos-claude-scientific-skills/scientific-skills/exploratory-data-analysis
Command: npx skills add https://github.com/jimmc414/Kosmos --skill exploratory-data-analysis-jimmc414

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires numpy, pandas, biopython, pillow, scikit-image, h5py, json, re, pathlib, datetime, os, sys, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill automates the process of understanding complex scientific data files, saving researchers significant time and effort in initial data assessment.

Core Features & Use Cases

  • Automatic File Type Detection: Identifies over 200 scientific file formats across various domains (chemistry, bioinformatics, imaging, etc.).
  • Format-Specific Analysis: Performs tailored EDA based on the detected file type, extracting metadata, assessing quality, and providing statistical summaries.
  • Markdown Report Generation: Creates comprehensive, human-readable reports detailing findings and recommendations.
  • Use Case: You have a newly acquired .fastq file from a sequencing run. Use this Skill to quickly understand its read count, average quality score, and length distribution, along with recommendations for downstream analysis.

Quick Start

Use the exploratory-data-analysis skill to analyze the file located at '/home/user/data/sample.csv'.

Frequently Asked Questions about exploratory-data-analysis

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

FAQPage Schema
How do I automatically detect scientific file formats and extract metadata for exploratory data analysis?▼

Exploratory data analysis automates file type detection across 200+ scientific formats, extracting format-specific metadata and assessing data quality to generate detailed markdown reports with analysis findings and downstream recommendations.

Does exploratory data analysis work with bioinformatics formats like FASTQ for sequencing quality assessment?▼

Yes, exploratory data analysis supports bioinformatics formats like FASTQ, automatically identifying read counts, average quality scores, and length distributions while providing recommendations for downstream analysis.

What's the best way to perform EDA on microscopy and spectroscopy data files without manual format specification?▼

Exploratory data analysis performs format-specific EDA on microscopy and spectroscopy files by automatically detecting the file type, extracting relevant metadata, assessing data quality, and generating comprehensive markdown reports with statistical summaries.

Can I use pandas and numpy to assess data quality and generate statistical summaries for chemistry data formats?▼

Yes, the skill leverages pandas, numpy, scikit-image, and biopython dependencies to assess data quality and extract statistical summaries from chemistry data formats, outputting detailed markdown reports with analysis findings.

How do I generate markdown reports with downstream recommendations for proteomics and metabolomics data?▼

Exploratory data analysis generates detailed markdown reports for proteomics and metabolomics data by extracting format-specific metadata, assessing data quality, and providing statistical summaries alongside downstream analysis recommendations.

When do I need exploratory data analysis for scientific data files, and what are its limitations?▼

You need exploratory data analysis when assessing newly acquired scientific files across domains like chemistry, bioinformatics, and microscopy. It automates initial data assessment but focuses on exploratory analysis rather than downstream processing execution.

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