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Estrella-231
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exploratory-data-analysis

Detect scientific file formats and generate markdown EDA reports.

Updated May 24, 2026
Search Tags:#report generation#bioinformatics#scientific data#eda#data preprocessing#format detection#qc metrics
One-click install
npx skills add https://github.com/Estrella-231/Mathematical_modeling_tongmeng --skill exploratory-data-analysis-estrella-231
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: exploratory-data-analysis
Source: https://github.com/Estrella-231/Mathematical_modeling_tongmeng/tree/main/.agents/skills/exploratory-data-analysis
Command: npx skills add https://github.com/Estrella-231/Mathematical_modeling_tongmeng --skill exploratory-data-analysis-estrella-231

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill removes the guesswork of opening unfamiliar scientific files by automatically detecting the format and producing a clear EDA report that explains structure, quality, and what to do next.

Core Features & Use Cases

  • Automatic file type detection (200+ formats): Determines file category and format from extension (and routes analysis accordingly).
  • Format-specific metadata extraction: Pulls structure/dimensions, key properties, and example identifiers tailored to the detected format category.
  • Data quality and integrity assessment: Checks completeness and key characteristics (e.g., summary stats, missing values where applicable).
  • Markdown report generation: Writes a detailed, documentation-ready EDA report for downstream modeling or analysis planning.
  • Cross-domain coverage: Works across chemistry/molecular, genomics, microscopy/imaging, spectroscopy, proteomics/metabolomics, and general scientific formats.

Quick Start

Use the exploratory-data-analysis skill to analyze the attached file path and generate the corresponding markdown EDA 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 scientific file formats and generate an EDA report?▼

To generate an EDA report, this process automatically detects over 200 scientific file formats from their extensions, extracts format-specific metadata, and computes quality statistics to produce a comprehensive markdown report.

Can I use this for exploratory data analysis on bioinformatics and microscopy data?▼

Yes, exploratory data analysis applies to cross-domain scientific data including bioinformatics, microscopy, spectroscopy, proteomics, metabolomics, and chemistry, extracting dimensions and summary statistics tailored to each format category.

What's the best way to assess scientific dataset quality and completeness before preprocessing?▼

The best way to assess dataset quality is computing summary statistics, extracting format-specific properties, and checking for missing values, which yields a documentation-ready markdown report with recommendations for downstream modeling.

Do I need biopython and pandas to compute QC metrics for scientific data?▼

You need biopython, pandas, numpy, h5py, pillow, and pyyaml installed in your environment to support format detection, metadata extraction, and QC metric computation across diverse scientific file categories.

Does format detection work for unfamiliar scientific files without manual configuration?▼

Format detection works automatically by routing file extensions to specific categories, optionally using reference-guided format detail loading to analyze unfamiliar scientific datasets without manual configuration.

When should I not use automated EDA for scientific data preprocessing?▼

Automated EDA should not be used as a replacement for domain-specific preprocessing when datasets require complex transformations, as it primarily focuses on understanding structure, computing summary stats, and recommending next steps.

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