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exploratory-data-analysis

Detect scientific data file formats and generate Markdown analysis reports.

2|Updated Nov 15, 2025
Search Tags:#automation#report generation#data analysis#bioinformatics#EDA#scientific data#chemistry#imaging
One-click install
npx skills add https://github.com/dakesan/hiro-favorite-skills --skill exploratory-data-analysis
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: exploratory-data-analysis
Source: https://github.com/dakesan/hiro-favorite-skills/tree/main/skills/exploratory-data-analysis
Command: npx skills add https://github.com/dakesan/hiro-favorite-skills --skill exploratory-data-analysis

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires biopython, pysam, pyBigWig, rdkit, mdanalysis, cclib, tifffile, nd2reader, aicsimageio, pydicom, nmrglue, pymzml, pyteomics, pandas, numpy, h5py, scipy, pillow, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill automates the initial, often complex, process of understanding scientific datasets. It eliminates the need for manual file type identification, custom scripting for different formats, and time-consuming data quality checks, allowing researchers to quickly grasp their data's characteristics and plan further analysis.

Core Features & Use Cases

  • 200+ Format Support: Automatically detects and analyzes a vast array of scientific data formats across chemistry, bioinformatics, imaging, and more.
  • Automated Report Generation: Produces detailed Markdown reports with format-specific analysis, quality metrics, and downstream recommendations.
  • Data Quality Assessment: Provides insights into data structure, content, and integrity, highlighting potential issues.
  • Use Case: You've received a new .fastq file from a sequencing experiment. Use this Skill to generate a comprehensive report detailing read counts, quality scores, GC content, and recommendations for variant calling, all in minutes.

Quick Start

Use the exploratory-data-analysis skill to analyze the file experiment_data.fastq and generate a 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 automatically detect and analyze scientific data file formats?▼

Automated file-type detection identifies your data format instantly—whether FASTQ, HDF5, NMR, microscopy, or chemistry files—then extracts format-specific metadata and generates a comprehensive Markdown report with quality metrics and downstream recommendations without manual identification.

What scientific data formats does this analysis support?▼

Support spans 200+ formats across bioinformatics (FASTQ, BAM, BigWig), chemistry (via RDKit), microscopy (TIFF, ND2, DICOM), spectroscopy (NMR via nmrglue), proteomics (mzML, mzXML via pyteomics), and general scientific data (HDF5, NetCDF), enabling analysis of diverse domain-specific datasets.

Can I use this for data quality assessment before downstream analysis?▼

Yes—the Skill evaluates data structure, content, and integrity to surface potential issues, provides statistical summaries and quality metrics, then outputs actionable recommendations for variant calling, alignment, or other downstream processes specific to your file type.

Do I need to manually write parsing scripts for each scientific data format?▼

No—format-specific analysis is automated for chemistry, bioinformatics, imaging, and spectroscopy data, eliminating custom scripting and manual format identification so you extract metadata and generate reports in minutes instead of hours.

What output does the analysis generate?▼

Detailed Markdown reports include format-specific analysis results, data-quality metrics highlighting structural and content issues, statistical summaries, visualization guidance, and tailored recommendations for your next analytical step based on detected file type and content.

Can this Skill work with multiple scientific domains in a single workflow?▼

Yes—apply the same Skill across chemistry, bioinformatics, microscopy, spectroscopy, proteomics, and imaging datasets without reconfiguring; each domain receives domain-appropriate analysis, quality checks, and recommendations within a unified automated workflow.

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