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exploratory-data-analysis

Analyze scientific data files across 200+ formats and generate markdown EDA reports.

Updated Oct 7, 2022
Search Tags:#python#data quality#bioinformatics#scientific data#exploratory data analysis#markdown report#file format detection
One-click install
npx skills add https://github.com/tamagusko/linux-cfg --skill exploratory-data-analysis-tamagusko
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: exploratory-data-analysis
Source: https://github.com/tamagusko/linux-cfg/tree/main/dotfiles/claude/skills/exploratory-data-analysis
Command: npx skills add https://github.com/tamagusko/linux-cfg --skill exploratory-data-analysis-tamagusko

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve? Scientific data arrives in hundreds of domain-specific formats, and understanding a new file's structure, quality, and content before analysis is slow and error-prone. This Skill automates file type detection, format-specific analysis, and report generation so you can quickly understand any scientific dataset. ## Core Features & Use Cases - Automatic Format Detection: Identifies 200+ scientific file extensions across chemistry, bioinformatics, microscopy, spectroscopy, proteomics, and general scientific data categories. - Format-Specific Analysis: Loads detailed reference information per format and performs appropriate analyses such as sequence statistics for FASTQ, dimension checks for microscopy images, or summary statistics for CSV files. - Markdown Report Generation: Produces comprehensive reports covering file metadata, data structure, quality metrics, key findings, and downstream analysis recommendations. - Use Case: A researcher receives a FASTQ sequencing file and asks to explore it. The Skill detects the format, computes read counts, length distributions, and quality scores, then saves a structured report as reads_eda_report.md. ## Quick Start Analyze the file experiment_data.csv and generate a full exploratory data analysis 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 perform exploratory data analysis on a scientific data file?▼

Provide the file path and the Skill detects the extension, loads format-specific reference information, and runs appropriate analyses. It then generates a markdown report with structure, statistics, quality metrics, and recommendations saved as filename_eda_report.md.

What scientific file formats are supported for automated analysis?▼

Over 200 extensions across six categories: chemistry and molecular files like PDB and SDF, bioinformatics formats like FASTQ and BAM, microscopy formats like ND2 and CZI, spectroscopy formats like mzML and JCAMP, proteomics formats, and general formats like CSV, HDF5, and Parquet.

How do I analyze FASTQ sequencing files in Python?▼

Use Biopython's SeqIO to parse FASTQ files and compute read counts, length distributions, GC content, and Phred quality scores. The Skill's analyzer script samples large files automatically and reports mean quality and length statistics.

What Python libraries are needed to read microscopy image formats?▼

Common requirements include tifffile for TIFF stacks, nd2reader for Nikon ND2 files, aicsimageio for multi-vendor formats, and pydicom for medical DICOM images. The Skill provides installation instructions when a required library is missing.

Why does analysis fail with an import error on my data file?▼

Many scientific formats require specialized libraries that are not installed by default, such as biopython, h5py, or pymzml. Install the missing package with pip or uv as indicated in the error message, then rerun the analysis.

Can the analyzer handle very large data files?▼

Yes, it uses sampling strategies such as reading only the first 10,000 rows or records for CSV and FASTQ files. For HDF5 and NumPy data it supports memory-mapped access and chunked processing to avoid loading entire datasets into memory.

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