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exploratory-data-analysis

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

Updated Mar 10, 2026
Search Tags:#reporting#data analysis#bioinformatics#scientific data#chemistry#file format#eda
One-click install
npx skills add https://github.com/felixboehm/biochem-allergy --skill exploratory-data-analysis-felixboehm
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: exploratory-data-analysis
Source: https://github.com/felixboehm/biochem-allergy/tree/main/.claude/skills/exploratory-data-analysis
Command: npx skills add https://github.com/felixboehm/biochem-allergy --skill exploratory-data-analysis-felixboehm

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

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

Core Features & Use Cases

  • File Type Detection: Automatically identifies over 200 scientific file formats.
  • Format-Specific Analysis: Performs tailored EDA based on the detected file type (e.g., sequence stats for FASTQ, shape/dtype for NPY, dimensions for TIFF).
  • Comprehensive Reporting: Generates detailed Markdown reports with findings and recommendations.
  • Use Case: Upload a .mzML file from a mass spectrometry experiment, and the Skill will analyze its spectral content, identify potential quality issues, and suggest appropriate downstream analysis pipelines.

Quick Start

Use the exploratory-data-analysis skill to analyze the file 'my_experiment.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 automate exploratory data analysis on scientific data files across different formats?▼

Automated exploratory data analysis identifies over 200 scientific file formats and performs format-specific analysis on structure, content, and quality, generating detailed markdown reports with downstream analysis recommendations.

What's the best way to analyze a mass spectrometry mzML file for spectral content and quality?▼

Analyzing mass spectrometry mzML files involves format-specific EDA that inspects spectral content, identifies potential quality issues, and suggests appropriate downstream analysis pipelines within a generated markdown report.

Does this EDA process work with bioinformatics data formats like FASTQ and microscopy files?▼

Yes, EDA works with bioinformatics FASTQ files and microscopy TIFF files by performing tailored analysis, such as calculating sequence statistics for FASTQ and inspecting dimensions for TIFF images.

Can I use pandas and scipy for comprehensive data quality assessment on chemistry and proteomics data?▼

Data quality assessment for chemistry and proteomics data utilizes dependencies like pandas, numpy, and scipy to evaluate file characteristics and structure, outputting detailed markdown insights.

What file formats are supported for automated scientific data exploration and reporting?▼

Automated scientific data exploration supports over 200 formats across chemistry, bioinformatics, microscopy, spectroscopy, proteomics, metabolomics, and general scientific data, detecting file types automatically.

How do I generate detailed markdown reports with downstream analysis recommendations for an NPY file?▼

Generating markdown reports for NPY files involves EDA that analyzes array shape and data type, producing detailed findings and recommendations for subsequent processing steps.

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