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exploratory-data-analysis

Analyze scientific data files to extract structure, quality, and format-specific metadata.

74|5|Updated Dec 10, 2025
Search Tags:#bioinformatics#microscopy#quality-control#scientific-data#file-detection#markdown-reports#exploratory-analysis
One-click install
npx skills add https://github.com/dralkh/seerai --skill exploratory-data-analysis-dralkh
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: exploratory-data-analysis
Source: https://github.com/dralkh/seerai/tree/main/skills/exploratory-data-analysis
Command: npx skills add https://github.com/dralkh/seerai --skill exploratory-data-analysis-dralkh

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

Scientific files are often hard to inspect because formats vary widely and the useful metadata is buried in binary or specialized encodings. This Skill turns those files into readable summaries so you can quickly understand what the data contains, whether it looks healthy, and what to do next.

Core Features & Use Cases

  • Automatic file type detection across scientific formats in chemistry, genomics, microscopy, spectroscopy, proteomics, and general data.
  • Format-aware exploration that extracts structure, dimensions, statistics, and quality signals from files such as FASTA, FASTQ, TIFF, HDF5, CSV, and JSON.
  • Report generation with findings, risks, and downstream recommendations for preprocessing, visualization, and follow-up analysis.
  • Use it when you need to triage a new dataset, confirm file integrity, or produce a shareable analysis summary before deeper work.

Quick Start

Ask the skill to analyze the attached scientific file and generate a comprehensive exploratory data analysis report with file identification, quality checks, and recommendations.

Frequently Asked Questions about exploratory-data-analysis

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I run exploratory data analysis on scientific files like FASTQ and HDF5?▼

To perform exploratory data analysis, attach your scientific file and request a report; the skill identifies the format and applies numpy, pandas, h5py, Pillow, or Biopython parsing to extract structure, dimensions, and quality signals.

What does file type detection and quality control look for in microscopy and genomics datasets?▼

Quality control for microscopy and genomics datasets extracts format-specific metadata, structural dimensions, and statistics to identify data integrity issues and generate preprocessing and visualization recommendations.

Can I use this skill to generate markdown reports for spectroscopy and proteomics data?▼

Yes, you can generate markdown reports for spectroscopy and proteomics data that summarize file identification findings, quality checks, and downstream analysis guidance into a shareable document.

Does exploratory analysis support format-aware parsing for FASTA, TIFF, CSV, and JSON files?▼

Exploratory analysis supports format-aware parsing for FASTA, TIFF, CSV, and JSON by using reference-backed format lookup and optional Python parsing to extract statistics and metadata.

What is the best way to triage a new scientific dataset before deeper analysis?▼

The best way to triage a new scientific dataset is to run automatic file type detection and format-aware exploration to confirm file integrity, extract structure, and produce a shareable analysis summary.

Do I need Biopython and h5py installed to parse bioinformatics and HDF5 files?▼

Yes, you need Biopython and h5py installed for optional Python parsing of bioinformatics and HDF5 files, as these dependencies enable the extraction of structure, dimensions, and format-specific metadata.

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