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exploratory-data-analysis

Detect scientific file formats and generate markdown analysis reports.

Updated Jan 10, 2026
Search Tags:#reporting#data analysis#bioinformatics#scientific data#chemistry#file format#microscopy
One-click install
npx skills add https://github.com/robinbarvaag/poynt --skill exploratory-data-analysis-robinbarvaag
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: exploratory-data-analysis
Source: https://github.com/robinbarvaag/poynt/tree/main/.github/skills/exploratory-data-analysis
Command: npx skills add https://github.com/robinbarvaag/poynt --skill exploratory-data-analysis-robinbarvaag

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandas, numpy, biopython, pillow, h5py, scikit-image, nd2reader, czifile, pydicom, nibabel, tifffile, pymzml, nmrglue, spc, gemmi, astropy, uproot, zarr, pyarrow, scipy, json, pathlib, datetime, os, sys, re, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill automates the process of understanding scientific data files, saving researchers significant time and effort in data exploration and quality assessment.

Core Features & Use Cases

  • Automated File Type Detection: Identifies over 200 scientific file formats.
  • Format-Specific Analysis: Generates detailed reports on structure, content, and quality.
  • Use Case: When provided with a complex .mzML file from a mass spectrometry experiment, this Skill will automatically detect its format, extract key metadata like scan types and retention times, assess data quality, and provide recommendations for downstream analysis like peak picking and metabolite identification.

Quick Start

Use the exploratory-data-analysis skill to analyze the file 'my_experiment.fastq'.

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 extract metadata for exploratory analysis?▼

You can analyze scientific data files like microscopy, bioinformatics, and spectroscopy formats by providing the file path to the exploratory-data-analysis skill, which automatically detects the format, extracts metadata, assesses quality, and outputs a detailed markdown report.

What scientific data formats are supported for automated metadata extraction and quality assessment?▼

Automated metadata extraction supports 200+ scientific data formats spanning chemistry, bioinformatics, microscopy, spectroscopy, proteomics, metabolomics, and general scientific files, including specialized formats like mzML, FASTQ, and DICOM.

Does the exploratory data analysis skill work with mass spectrometry mzML files and provide downstream recommendations?▼

Yes, the skill detects mzML mass spectrometry files, extracts key metadata like scan types and retention times, assesses data quality, and provides downstream recommendations for tasks such as peak picking and metabolite identification.

How do I generate data quality reports for complex bioinformatics files like FASTQ?▼

To generate data quality reports for bioinformatics files, provide the file to the skill which automatically detects the format, analyzes structural content, evaluates quality, and outputs a comprehensive markdown report with analysis findings.

Can I use pandas and numpy to analyze microscopy file formats like CZI and ND2?▼

Yes, the skill leverages libraries like pandas, numpy, scikit-image, czifile, and nd2reader to analyze microscopy formats, automatically extracting structural metadata and assessing image data quality for scientific reporting.

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