Does this skill require specific Python libraries?

Yes, depending on the file type, it may utilize specialized libraries such as Biopython, RDKit, MDAnalysis, or Scanpy to perform deep technical analysis.

What is included in the generated EDA report?

Each report includes file properties, format-specific details, statistical summaries, quality assessments, key findings, and recommended next steps for analysis.

Which scientific file formats are supported?

The skill supports over 200 formats across chemistry (.pdb, .sdf), bioinformatics (.fastq, .bam), microscopy (.czi, .nd2), spectroscopy (.mzML), and general data (.hdf5, .parquet).

Can it handle very large scientific datasets?

The skill is designed to handle large files by performing efficient metadata extraction and representative sampling to provide insights without crashing the environment.

Scientific Exploratory Data Analysis

Name: Scientific Exploratory Data Analysis
Author: pur3v4d3r

bypur3v4d3r

•

数据科学与机器学习

Performs comprehensive exploratory data analysis on over 200 scientific file formats to generate detailed quality reports and statistical summaries.

关于

This skill empowers Claude to act as a specialized scientific data analyst capable of handling 200+ domain-specific file formats across chemistry, bioinformatics, microscopy, and spectroscopy. It automates file type detection, extracts format-specific metadata, assesses data integrity, and produces professional markdown reports. Whether you are dealing with genomic sequences, molecular structures, or high-dimensional imaging data, this skill provides the necessary context, statistical insights, and actionable downstream analysis recommendations to streamline your research workflow.

主要功能

Automatic detection and analysis of 200+ scientific file formats
Generation of comprehensive Markdown reports with statistical summaries
Automated data quality assessment and integrity metrics
Actionable recommendations for preprocessing and downstream analysis tools
Format-specific metadata extraction for chemistry, genomics, and imaging
1 GitHub stars

使用场景

Summarizing multi-dimensional microscopy images (ND2/CZI) to extract spatial and channel metadata
Analyzing raw sequencing data (FASTQ/FASTA) for quality control and GC content
Exploring molecular structure files (PDB/SDF) for structural validation and atom counts

关于

主要功能

Automatic detection and analysis of 200+ scientific file formats
Generation of comprehensive Markdown reports with statistical summaries
Automated data quality assessment and integrity metrics
Actionable recommendations for preprocessing and downstream analysis tools
Format-specific metadata extraction for chemistry, genomics, and imaging
1 GitHub stars

使用场景

Summarizing multi-dimensional microscopy images (ND2/CZI) to extract spatial and channel metadata
Analyzing raw sequencing data (FASTQ/FASTA) for quality control and GC content
Exploring molecular structure files (PDB/SDF) for structural validation and atom counts