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exploratory-data-analysis

Detect scientific data file types and generate markdown EDA reports.

21|1|Updated Mar 19, 2026
Search Tags:#data-analysis#eda#markdown-report#scientific-data#file-detection#format-references
One-click install
npx skills add https://github.com/OwnLabAI/ownlab --skill exploratory-data-analysis-ownlabai
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: exploratory-data-analysis
Source: https://github.com/OwnLabAI/ownlab/tree/main/mart/skills/scientific-skills/exploratory-data-analysis
Command: npx skills add https://github.com/OwnLabAI/ownlab --skill exploratory-data-analysis-ownlabai

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This skill automates the exploratory data analysis of scientific data files across dozens of domains, generating a comprehensive markdown report that captures metadata, quality metrics, and recommended next steps for data curation and downstream analysis.

Core Features & Use Cases

  • Automatic file type detection for 200+ scientific formats and format-specific metadata extraction.
  • Data quality assessment, structural summaries, and statistical insights across chemistry, biology, microscopy, spectroscopy, and general scientific data formats.
  • Markdown report generation using a reusable template, suitable for documentation and downstream analysis planning.
  • Guidance and recommendations for data preprocessing, QC, and suitable analytical approaches tailored to detected formats and data characteristics.

Quick Start

Run the analyzer on a target scientific data file to produce a complete 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 perform exploratory data analysis on scientific data files with unknown formats?▼

Exploratory data analysis on unknown scientific files is automated by detecting over 200 formats, extracting metadata, and generating a detailed markdown report with quality metrics and next-step recommendations.

Can I extract metadata and assess data quality across microscopy and spectroscopy formats?▼

Metadata extraction and data quality assessment are supported across microscopy, spectroscopy, chemistry, and biology formats, providing structural summaries, statistical insights, and format-specific downstream guidance.

What is the best way to generate a markdown report for biological data curation?▼

Generating a markdown report for biological data curation is achieved by running the analyzer on a target file, which uses a reusable template to document metadata, quality metrics, and analytical recommendations.

Does this automated file detection handle chemistry and biology formats without manual configuration?▼

Automated file detection handles chemistry, biology, microscopy, spectroscopy, and general data formats without manual configuration, leveraging dependencies like biopython and h5py to parse diverse scientific structures.

How do I get downstream analysis recommendations for diverse scientific data formats?▼

Downstream analysis recommendations are generated by assessing data characteristics and detected formats, offering tailored guidance for data preprocessing, quality control, and suitable analytical approaches in a markdown report.

Can I use numpy and pandas to analyze structural summaries from detected scientific files?▼

Numpy and pandas are utilized under the hood to compute statistical insights and structural summaries from detected scientific files, outputting the results directly into the generated markdown documentation.

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