Which scientific domains are supported by this skill?

This skill supports a wide range of domains including chemistry, bioinformatics, genomics, microscopy, spectroscopy, proteomics, and metabolomics.

Can it suggest specific Python libraries for my data?

Yes, the skill identifies the most appropriate Python libraries (such as Biopython, RDKit, or nd2reader) and provides code examples for reading the specific file format detected.

What kind of output does the Exploratory Data Analysis skill provide?

The skill generates a comprehensive Markdown report containing file metadata, data structure analysis, statistical summaries, quality assessments, and specific recommendations for further analysis.

How many file formats can this skill handle?

It can automatically detect and analyze over 200 specific scientific file extensions, ranging from common formats like .csv to highly specialized ones like .pdb, .fastq, .nd2, and .mzML.

Scientific Exploratory Data Analysis

Name: Scientific Exploratory Data Analysis
Author: Zehong-Wang

byZehong-Wang

0•

Ciencia de Datos y ML

Performs automated exploratory data analysis and generates detailed reports for over 200 scientific file formats across multiple domains.

The Exploratory Data Analysis skill empowers researchers and data scientists to quickly understand complex scientific datasets within the Claude environment. By automatically detecting file formats across chemistry, bioinformatics, microscopy, and spectroscopy, it extracts critical metadata, assesses data quality, and generates comprehensive markdown reports. Whether you are dealing with PDB structures, FASTQ sequences, or high-resolution microscopy images, this skill provides the necessary statistical summaries and downstream analysis recommendations to accelerate your scientific workflow and ensure data integrity.

Características Principales

01Automated generation of detailed markdown reports with statistical summaries

02Automatic detection and analysis of 200+ scientific file formats

030 GitHub stars

04Comprehensive data quality and integrity assessment metrics

05Tailored recommendations for preprocessing and specialized downstream analysis

06Domain-specific metadata extraction for chemistry, genomics, and imaging

Casos de Uso

01Quickly summarizing the contents and quality of a new bioinformatics or chemistry dataset

02Generating documentation-ready EDA reports for laboratory experiments and audits

03Identifying the correct Python libraries and methods needed to process specialized scientific file types

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add zehong-wang/kosmos exploratory-data-analysis

For use in Claude.ai and ChatGPT

Download Skill