How does the report generation work?

The skill analyzes the file and automatically generates a detailed Markdown report containing structure, statistical summaries, findings, and recommended next steps for your analysis.

Do I need to install specific Python libraries?

Yes, depending on the file type, you may need libraries like Biopython, RDKit, or nd2reader. The skill provides installation instructions if a required library is missing.

Can this skill handle large scientific datasets?

Yes, it is designed to handle large files by utilizing representative sampling and metadata-focused analysis to provide insights without overloading system memory.

What file formats are supported by this skill?

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

Scientific Exploratory Data Analysis

Name: Scientific Exploratory Data Analysis
Author: henriquescastilho

byhenriquescastilho

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数据科学与机器学习

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

This skill empowers researchers and data scientists to rapidly interpret complex scientific datasets by providing automated file detection and deep analysis across diverse domains including bioinformatics, chemistry, and imaging. It extracts format-specific metadata, assesses data integrity, and produces professional markdown reports that include statistical summaries and visualization recommendations. By bridging the gap between raw scientific data and actionable insights, it streamlines the initial phases of data exploration and ensures data quality before downstream processing.

主要功能

01Automatic detection and analysis of 200+ scientific file formats.

02Domain-specific metadata extraction for chemistry, genomics, and microscopy.

03Actionable recommendations for preprocessing and visualization strategies.

04Automated generation of detailed markdown reports for documentation.

05Comprehensive data quality assessment and integrity checking.

061 GitHub stars

使用场景

01Quickly summarizing the structure and content of unfamiliar scientific data files.

02Generating standardized documentation for datasets used in collaborative research.

03Automating data quality audits for large-scale bioinformatics or proteomics projects.

主要功能

01Automatic detection and analysis of 200+ scientific file formats.

02Domain-specific metadata extraction for chemistry, genomics, and microscopy.

03Actionable recommendations for preprocessing and visualization strategies.

04Automated generation of detailed markdown reports for documentation.

05Comprehensive data quality assessment and integrity checking.

061 GitHub stars

使用场景

01Quickly summarizing the structure and content of unfamiliar scientific data files.

02Generating standardized documentation for datasets used in collaborative research.

03Automating data quality audits for large-scale bioinformatics or proteomics projects.