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exploratory-data-analysis

Analyze scientific files to generate markdown reports on structure and quality.

1|Updated Mar 20, 2026
Search Tags:#data quality#report generation#file analysis#scientific data#metadata extraction
One-click install
npx skills add https://github.com/jadzoghaib/Sabadell_Capstone --skill exploratory-data-analysis-jadzoghaib
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: exploratory-data-analysis
Source: https://github.com/jadzoghaib/Sabadell_Capstone/tree/main/.claude/skills/exploratory-data-analysis
Command: npx skills add https://github.com/jadzoghaib/Sabadell_Capstone --skill exploratory-data-analysis-jadzoghaib

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill enables users to quickly understand complex scientific data files by automatically performing exploratory analysis and generating detailed reports.

Core Features & Use Cases

  • Comprehensive File Analysis: Detects file types across 200+ scientific formats and extracts format-specific metadata.
  • Insight Generation: Produces detailed markdown reports highlighting data structure, quality, and content.
  • Use Case: Researchers can upload a microscopy image, and this Skill will analyze pixel statistics, extract calibration metadata, and recommend further imaging steps, facilitating quality control and data interpretation.

Quick Start

Analyze your scientific data file 'experiment.xyz' to generate an insights 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 automate exploratory data analysis for scientific files?▼

You can automate exploratory data analysis by uploading your scientific files to generate detailed markdown reports. The process detects file types across 200+ formats, extracts metadata, and assesses data quality automatically.

Can I extract metadata and assess data quality from microscopy images?▼

Yes, metadata extraction and data quality assessment are supported for microscopy images. The analysis covers pixel statistics, extracts calibration metadata, and recommends further imaging steps for quality control.

Does this exploratory data analysis tool work with biopython and pandas?▼

Yes, the tool requires numpy, pandas, and biopython libraries to function. These dependencies enable format detection and metadata extraction across different scientific file types.

What is the best way to generate reports from complex scientific datasets?▼

The best way to generate reports from complex scientific datasets is using an automated analysis tool that produces detailed markdown summaries. These reports highlight data structure, quality, and content for quick comprehension.

What file formats are supported for automated scientific data insights?▼

Automated scientific data insights support over 200 scientific file formats. The tool detects the file type automatically and applies format-specific metadata extraction and quality assessment.

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