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Mekann2904
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research-data-analysis

Perform exploratory data analysis on over 200 scientific file formats and generate markdown reports.

Updated May 13, 2026
Search Tags:#data analysis#bioinformatics#data processing#EDA#chemistry#imaging#scientific formats
One-click install
npx skills add https://github.com/Mekann2904/mekann --skill research-data-analysis
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: research-data-analysis
Source: https://github.com/Mekann2904/mekann/tree/main/.pi/lib/skills/research-data-analysis
Command: npx skills add https://github.com/Mekann2904/mekann --skill research-data-analysis

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandas, polars, dask, vaex, markitdown, biopython, pillow, numpy, h5py, scipy, tifffile, pydicom, nibabel, rasterio, pymzml, nmrglue, gemmi, astropy, cclib, pyarrow, magic, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill tackles the challenge of analyzing diverse and complex scientific data formats, enabling rapid exploration and understanding of research datasets.

Core Features & Use Cases

  • Universal Format Support: Handles over 200 scientific file types including chemical, biological, imaging, and general data formats.
  • Automated EDA: Performs comprehensive Exploratory Data Analysis (EDA) including format detection, data quality assessment, statistical summaries, and visualization recommendations.
  • Large Data Handling: Integrates Dask, Vaex, and Polars for efficient processing of datasets that exceed RAM.
  • Document Conversion: Converts formats like PDF, DOCX, and XLSX to Markdown.
  • Use Case: You have a new dataset in a FITS file from an astronomical observation. This Skill can automatically detect the format, load the data, provide basic statistics, suggest relevant visualizations, and generate a markdown report summarizing its characteristics.

Quick Start

Use the research-data-analysis skill to analyze the file 'my_experiment_data.mzML'.

Frequently Asked Questions about research-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 file formats like FITS or mzML?▼

Exploratory data analysis on scientific formats involves detecting the file type, loading data, and generating statistical summaries. This Skill automates EDA for over 200 formats, providing quality assessments and visualization recommendations in a markdown report.

Can I process large datasets that exceed available RAM with Polars or Dask?▼

Yes, you can process large datasets exceeding RAM using integrated frameworks like Dask, Vaex, and Polars. These libraries enable efficient out-of-core data processing for large scientific files.

Does this tool convert PDF and DOCX files to markdown for data extraction?▼

Yes, the tool converts documents like PDF, DOCX, and XLSX to markdown using the Markitdown library. This allows you to extract and analyze data from research documents.

What is the best way to analyze bioinformatics data stored in HDF5 or DICOM files?▼

The best way to analyze bioinformatics data in HDF5 or DICOM files is using specialized libraries like h5py and pydicom. This Skill automatically detects these formats, extracts metadata, and provides statistical summaries.

How do I generate a statistical summary for an astronomical FITS file?▼

To generate a statistical summary for a FITS file, the Skill uses the Astropy library to load the data and calculate basic statistics. It then recommends relevant visualizations and compiles the results into a markdown report.

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