skills.rest
ZanderRuss
ZanderRussCOMMUNITY
Explore all skills by this author →
Website𝕏@mathew_fullerGitHub

exploratory-data-analysis

Detect scientific file formats and generate markdown EDA reports.

18|2|Updated Jan 10, 2026
Search Tags:#reporting#data analysis#bioinformatics#scientific data#chemistry#eda#file formats
One-click install
npx skills add https://github.com/ZanderRuss/obsidian-claude --skill exploratory-data-analysis-zanderruss
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: exploratory-data-analysis
Source: https://github.com/ZanderRuss/obsidian-claude/tree/main/.claude/skills/exploratory-data-analysis
Command: npx skills add https://github.com/ZanderRuss/obsidian-claude --skill exploratory-data-analysis-zanderruss

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires numpy, pandas, biopython, pillow, scipy, h5py, nd2reader, czifile, pydicom, tifffile, pymzml, nmrglue, pyBigWig, pybedtools, geopandas, rasterio, netCDF4, astropy, uproot, scikit-image, imageio, pyedflib, pyreadr, tomli, yaml, json, zipfile, tarfile, gzip, bz2, openpyxl, lxml, configparser, toml, pyarrow, fastparquet, matplotlib, seaborn, plotly, bokeh, altair, streamlit, dash, flask, django, fastapi, uvicorn, gunicorn, celery, redis, kafka, sqlalchemy, psycopg2, mysql-connector-python, pymongo, neo4j, networkx, igraph, scapy, requests, beautifulsoup4, selenium, playwright, pytest, unittest, coverage, flake8, pylint, black, isort, mypy, sphinx, jupyter, ipython, matplotlib, seaborn, plotly, bokeh, altair, streamlit, dash, flask, django, fastapi, uvicorn, gunicorn, celery, redis, kafka, sqlalchemy, psycopg2, mysql-connector-python, pymongo, neo4j, networkx, igraph, scapy, requests, beautifulsoup4, selenium, playwright, pytest, unittest, coverage, flake8, pylint, black, isort, mypy, sphinx, jupyter, ipython, 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 initial data exploration and reporting.

Core Features & Use Cases

  • Automated File Type Detection: Identifies over 200 scientific file formats.
  • Format-Specific Analysis: Performs tailored EDA based on file type (e.g., sequence stats for FASTQ, shape/stats for NPY, metadata for ND2).
  • Comprehensive Reporting: Generates detailed markdown reports with findings and recommendations.
  • Use Case: Upload a .fastq file and get a report on read count, length distribution, and quality scores, along with recommendations for downstream analysis like alignment.

Quick Start

Use the exploratory-data-analysis skill to analyze the file 'my_data.csv'.

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 file formats like FASTQ and ND2?▼

Exploratory data analysis on scientific file formats is automated by detecting the file type and generating markdown reports with format-specific statistics, quality metrics, and downstream analysis recommendations for files like FASTQ and ND2.

Can I analyze microscopy and spectroscopy files automatically without writing custom parsing scripts?▼

Yes, you can analyze microscopy and spectroscopy files automatically without custom parsing scripts. The tool identifies over 200 scientific formats, extracting metadata and shape statistics directly from files like CZI and MZML.

What is the best way to generate a data quality report for bioinformatics sequences?▼

The best way to generate a data quality report for bioinformatics sequences is using automated EDA, which outputs read counts, length distributions, and quality scores in markdown alongside tailored recommendations for downstream alignment.

Does this exploratory data analysis tool support HDF5, NetCDF, and Parquet formats?▼

Yes, this exploratory data analysis tool supports HDF5, NetCDF, and Parquet formats. It leverages libraries like h5py, netCDF4, and pyarrow to extract structural and statistical summaries for comprehensive reporting.

How do I get downstream analysis recommendations after inspecting chemistry data files?▼

You get downstream analysis recommendations after inspecting chemistry data files by running the automated EDA process, which evaluates format-specific metrics and appends tailored next-step suggestions directly to the generated markdown report.

Are there limitations when running EDA on very large scientific datasets?▼

While the EDA process handles 200+ formats efficiently, limitations on very large scientific datasets depend on available system memory and the specific parsing library used for formats like TIFF or HDF5 during extraction.

Related Skills

Explore More Data & Analytics Skills→
tassiovaletassiovale
13

exploratory-data-analysis

Detect scientific file types, analyze data quality, and generate Markdown reports.

Community
Advanced
WanLanglinWanLanglin

exploratory-data-analysis

Detect scientific data file formats and generate Markdown EDA reports.

Community
Advanced
robotlearning123robotlearning123

scientific-visualization

Create publication-quality scientific figures with matplotlib, seaborn, and plotly.

Community
Advanced
Mekann2904Mekann2904

research-data-analysis

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

Community
Advanced