exploratory-data-analysis

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

Detect scientific file types, extract metadata, and generate Markdown EDA reports.

15|2|Updated Dec 17, 2025

Search Tags:#metadata #data-analysis #eda #reference-guides #markdown-report #scientific-formats

Ready to run

npx skills add https://github.com/rubensliv/k-dense-ai --skill exploratory-data-analysis-rubensliv

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: exploratory-data-analysis
Source: https://github.com/rubensliv/k-dense-ai/tree/main/scientific-skills/exploratory-data-analysis
Command: npx skills add https://github.com/rubensliv/k-dense-ai --skill exploratory-data-analysis-rubensliv

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

Automates comprehensive exploratory data analysis on scientific data files across 200+ formats, automatically detects file type, extracts format-specific metadata, assesses data quality, and generates Markdown reports for documentation and downstream analysis planning.

Core Features & Use Cases

Automatic detection of 200+ scientific file formats and category-aware metadata extraction.
Comprehensive data quality assessment, statistical summaries, and format-specific insights.
Markdown report generation with downstream recommendations for analysis and visualization.
Use cases include analyzing a single data file or batches to inform preprocessing, quality control, and experimental planning.

Quick Start

Provide a path to a scientific data file and run the analyzer to generate an EDA 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 data files across different formats?▼

Automated exploratory data analysis identifies file types, extracts format-specific metadata, and generates a Markdown report with statistical summaries and quality assessments for scientific data files. It supports over 200 formats in chemistry, biology, imaging, and spectroscopy.

Can I extract metadata and assess data quality for batch scientific files?▼

Yes, batch metadata extraction and data quality assessment are supported for scientific files. The analyzer detects formats across batches, applies category-aware extraction, and outputs a comprehensive Markdown report detailing quality and downstream recommendations.

Does this exploratory data analysis tool work with HDF5 and biological data formats?▼

Yes, the exploratory data analysis tool works with HDF5 files and biological data formats using dependencies like h5py and biopython. It automatically detects formats in proteomics, metabolomics, and general scientific data to extract metadata and generate reports.

What is the best way to generate an EDA report for spectroscopy and imaging data?▼

The best way to generate an EDA report for spectroscopy and imaging data is using an automated analyzer that detects the file format, extracts metadata using libraries like Pillow, assesses data quality, and outputs a Markdown report with downstream recommendations.

Do I need Python and pandas installed to run automated scientific data exploration?▼

Yes, you need Python with pandas, numpy, and other specified dependencies installed to run automated scientific data exploration. These libraries enable the analyzer to load references, extract metadata, and generate the EDA report.

What limitations exist when analyzing unknown or mixed scientific data formats?▼

The analysis relies on local reference guides under references/ and optional components in scripts/ and assets/ to detect over 200 scientific formats. If a file format is unrecognized or lacks a specific reference, the metadata extraction and quality assessment may be limited.