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exploratory-data-analysis

Analyze scientific data files and generate detailed Markdown reports.

322|45|Updated Dec 1, 2025
Search Tags:#data quality#data analysis#bioinformatics#scientific data#chemistry#eda#microscopy
One-click install
npx skills add https://github.com/Microck/ordinary-claude-skills --skill exploratory-data-analysis-microck
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: exploratory-data-analysis
Source: https://github.com/Microck/ordinary-claude-skills/tree/main/exploratory-data-analysis
Command: npx skills add https://github.com/Microck/ordinary-claude-skills --skill exploratory-data-analysis-microck

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

Analyzing diverse scientific data files to understand their structure, quality, and characteristics is a time-consuming and complex task, often requiring specialized knowledge of numerous file formats and programming libraries. This Skill automates comprehensive exploratory data analysis (EDA).

Core Features & Use Cases

  • 200+ File Format Support: Automatically detect and analyze a vast array of scientific data formats across chemistry, bioinformatics, microscopy, and more.
  • Automated Data Quality Reports: Generate detailed Markdown reports with format-specific analysis, quality metrics, and downstream analysis recommendations.
  • Downstream Analysis Guidance: Receive suggestions for appropriate preprocessing steps, analytical methods, and visualization approaches.
  • Use Case: When you receive a new .fastq file from a sequencing experiment, use this skill to automatically generate a comprehensive report detailing sequence counts, length distributions, quality scores, and recommendations for variant calling or assembly.

Quick Start

Analyze the scientific data file 'experiment_results.csv' and provide a detailed 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 analyze scientific data files to understand their structure and quality?▼

Exploratory data analysis on scientific files automatically detects 200+ formats across chemistry, bioinformatics, and microscopy, extracts format-specific metadata, computes quality metrics, and generates a detailed Markdown report with statistical summaries and visualization recommendations in one workflow.

Can I use pandas and numpy to analyze FASTQ, HDF5, and microscopy image files?▼

Yes. This Skill analyzes 200+ scientific formats including FASTQ, HDF5, DICOM, and others using pandas, numpy, BioPython, and RDKit, automatically detecting file type, extracting domain-specific metadata, calculating quality scores, and recommending downstream preprocessing and analysis methods.

What data quality checks and statistical summaries are generated for scientific datasets?▼

Data quality analysis produces format-specific metrics, statistical summaries, quality score distributions, and missing-value assessments. The Markdown report includes visualization suggestions, preprocessing recommendations, and guidance for downstream analytical methods suited to your data type and structure.

Do I need specialized knowledge of file formats to analyze bioinformatics and chemistry data?▼

No. Automatic file type detection eliminates format expertise requirements. The Skill handles format-specific parsing for chemistry structures, genomic sequences, proteomics, metabolomics, and spectroscopy data, delivering actionable insights without manual format configuration.

How do I get preprocessing and analytical method recommendations for my sequencing or imaging data?▼

After analyzing your file, the generated Markdown report includes downstream analysis guidance: suggested preprocessing steps, appropriate analytical methods, and visualization approaches tailored to your data's characteristics, domain, and quality metrics.

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