Do I need to manually specify the file type for analysis?

No, the skill features automatic file type detection. It identifies the extension, matches it against a comprehensive reference library, and applies the appropriate format-specific analysis workflow.

What scientific file formats does this skill support?

The skill supports over 200 formats across six major categories, including chemistry (.pdb, .sdf), bioinformatics (.fasta, .bam), microscopy (.nd2, .czi), spectroscopy (.mzML), and general scientific data (.hdf5, .zarr).

Does this skill provide code for further analysis?

Yes, the generated EDA reports include recommendations for specific Python libraries (like Biopython, RDKit, or Scanpy) and code snippets to help you move directly into deeper analysis.

Can it handle very large scientific datasets?

Yes, the skill is designed to handle large files by performing representative sampling and efficient metadata extraction, ensuring you get a comprehensive summary without crashing your environment.

Exploratory Scientific Data Analysis

Name: Exploratory Scientific Data Analysis
Author: x-cmd

byx-cmd

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Data Science & ML

Performs comprehensive exploratory data analysis and generates detailed reports for over 200 scientific file formats.

About

This skill empowers Claude to automatically detect, analyze, and summarize complex scientific datasets across diverse domains including bioinformatics, chemistry, spectroscopy, and microscopy. By leveraging domain-specific metadata extraction and automated quality assessments, it provides researchers with an immediate understanding of their data structure and integrity. Whether you are dealing with molecular trajectories, genomic sequences, or high-resolution imaging, this skill generates actionable Markdown reports that streamline documentation and guide downstream analysis planning.

Key Features

Automated data quality, completeness, and integrity assessments
8 GitHub stars
Automatic detection and analysis of 200+ specialized scientific file formats
Format-specific metadata extraction for chemistry, genomics, and imaging
Detailed Markdown report generation with statistical summaries
Actionable recommendations for preprocessing and downstream analysis tools

Use Cases

Extracting metadata and intensity statistics from multi-dimensional microscopy images (ND2, CZI, LIF)
Quickly assessing the quality and content of large bioinformatics datasets like FASTQ or VCF files
Validating chemical structure files and molecular dynamics trajectories for research documentation

About

Key Features

Automated data quality, completeness, and integrity assessments
8 GitHub stars
Automatic detection and analysis of 200+ specialized scientific file formats
Format-specific metadata extraction for chemistry, genomics, and imaging
Detailed Markdown report generation with statistical summaries
Actionable recommendations for preprocessing and downstream analysis tools

Use Cases

Extracting metadata and intensity statistics from multi-dimensional microscopy images (ND2, CZI, LIF)
Quickly assessing the quality and content of large bioinformatics datasets like FASTQ or VCF files
Validating chemical structure files and molecular dynamics trajectories for research documentation