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exploratory-data-analysis

Analyze scientific data files for type detection, metadata extraction, and quality assessment.

Updated Aug 23, 2026
Search Tags:#data analysis#metadata extraction#automated reporting#quality assessment#scientific formats#format detection
One-click install
npx skills add https://github.com/ogngnaoh/scientific-agent-skills --skill exploratory-data-analysis-ogngnaoh
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: exploratory-data-analysis
Source: https://github.com/ogngnaoh/scientific-agent-skills/tree/main/scientific-agent-skills/skills/exploratory-data-analysis
Command: npx skills add https://github.com/ogngnaoh/scientific-agent-skills --skill exploratory-data-analysis-ogngnaoh

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandas, numpy, PyBioMed, h5py, biopython, nd2reader, pyBigWig, pyteomics, bioformats, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill addresses the challenge of understanding complex scientific data by providing automated, comprehensive exploratory analysis reports.

Core Features & Use Cases

  • Automated File Type Detection and Metadata Extraction: Recognizes over 200 scientific data formats and pulls format-specific details.
  • Data Quality and Structural Analysis: Assesses data completeness, distributions, and integrity across various scientific domains.
  • Use Case: Researchers can upload a raw spectroscopy file, and the Skill generates insights on data quality, spectrum ranges, and recommended next analyses, saving time and reducing errors.

Quick Start

Use the exploratory-data-analysis skill to analyze the file 'experiment1.mzML' and generate a report summarizing its structure and quality.

Frequently Asked Questions about exploratory-data-analysis

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

FAQPage Schema
How do I extract metadata from scientific data files automatically?▼

Automated metadata extraction recognizes over 200 scientific formats across chemistry, genomics, imaging, and spectroscopy domains to pull format-specific details and generate comprehensive reports.

Can I assess data quality and structural integrity for raw spectroscopy files?▼

Yes, you can assess data quality for raw spectroscopy files by evaluating data completeness, distributions, and integrity to generate insights on spectrum ranges and recommend next analyses.

Does this automated reporting tool support genomics and imaging formats?▼

Yes, this automated reporting tool supports genomics and imaging formats by utilizing format-specific libraries like pyBigWig and nd2reader to interpret complex datasets across multiple scientific domains.

What's the best way to analyze an mzML file and summarize its structure?▼

The best way to analyze an mzML file is to run an exploratory data analysis that detects the file type, extracts metadata, and generates a report summarizing its structure and overall quality.

Do I need specific Python libraries like biopython or h5py to detect scientific file formats?▼

The automated format detection utilizes format-specific libraries including biopython, h5py, and pyteomics to accurately identify over 200 scientific formats and extract their detailed metadata.

Why does format detection fail for complex microscopy datasets?▼

Format detection for complex microscopy datasets may fail if the specific format requires specialized libraries like bioformats or nd2reader that are not properly configured in your current environment.

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