exploratory-data-analysis

Name: exploratory-data-analysis
Availability: InStock
Author: Engstrom

Detect scientific data file formats and generate Markdown EDA reports.

18|1|Updated Dec 27, 2025

Search Tags:#metadata #data-analysis #eda #report-generation #scientific-data #file-detection

Ready to run

npx skills add https://github.com/LogauaEngstrom/claude-scientific-skills --skill exploratory-data-analysis-logauaengstrom

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: exploratory-data-analysis
Source: https://github.com/LogauaEngstrom/claude-scientific-skills/tree/main/scientific-skills/exploratory-data-analysis
Command: npx skills add https://github.com/LogauaEngstrom/claude-scientific-skills --skill exploratory-data-analysis-logauaengstrom

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

Automate the discovery and analysis of scientific data files by automatically detecting formats and applying domain-aware exploratory data analysis to generate actionable insights and documentation.

Core Features & Use Cases

Automatic file type detection and category assignment to route to appropriate domain-specific analyses.
Domain-aware data analysis that covers general scientific formats, genomics, and microscopy/imaging with markdown report generation.
On-demand reference extraction and artifact reporting to support reproducible research.

Quick Start

Provide a data file path to generate a comprehensive EDA report in markdown.

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 genomics and microscopy files?▼

Automated exploratory data analysis detects scientific data file formats by extension and content, then applies domain-specific processing for genomics and microscopy to generate structured Markdown reports. You provide a file path, and the tool routes the data to the appropriate analysis category.

What is automatic file type detection for scientific data analysis?▼

Automatic file type detection for scientific data analysis inspects file extensions and internal content to classify data into categories like general scientific formats, genomics, or microscopy. This classification enables targeted, domain-aware analysis instead of generic processing.

How do I generate Markdown reports from HDF5 and imaging data?▼

To generate Markdown reports from HDF5 and imaging data, provide the file path to the automated analysis tool. It uses dependencies like h5py and Pillow to extract metadata, analyze the content, and output a structured report with file information and reference summaries.

Does exploratory data analysis work with pandas, numpy, and biopython dependencies?▼

Yes, exploratory data analysis utilizes pandas and numpy for general scientific data processing, and biopython specifically for genomics formats. These dependencies enable the tool to parse, analyze, and extract metadata across various scientific data categories.

Can I use this for extracting metadata from general scientific formats?▼

Yes, you can use this for extracting metadata from general scientific formats. The analysis process identifies file characteristics, applies format-specific extraction logic, and returns structured metadata alongside a Markdown report for downstream interpretation.

What are the limitations of automated scientific data exploration?▼

Automated scientific data exploration is limited to supported general scientific, genomics, and microscopy formats recognized by the file detection logic. It produces metadata and Markdown reports for initial interpretation but does not replace specialized downstream statistical modeling.