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Agent skill

Exploratory Data Analysis

by spacering-net in spacering-net/codeg

Perform comprehensive exploratory data analysis on scientific data files across 200+ file formats.

MITAuto-check passedData & Analytics

Install Exploratory Data Analysis

skills CLI
$ npx skills add spacering-net/codeg --skill exploratory-data-analysis -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install spacering-net/codeg exploratory-data-analysis --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/spacering-net/codeg.git skills-src && mkdir -p .claude/skills && cp -r skills-src/src-tauri/science/skills/exploratory-data-analysis .claude/skills/exploratory-data-analysis && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
exploratory-data-analysis
GitHub stars
3.8k
Used in
15 other repos
Token cost
~3.6k tokens
SKILL.md length
1,267 words
Files
9 (incl. scripts, references, assets)
Skills in repo
9
Repo updated
First seen
Licence
MIT

At a glance

Perform comprehensive exploratory data analysis on scientific data files across 200+ file formats.

  • Works in 11 steps: Chemistry and Molecular Formats (60+… → Bioinformatics and Genomics Formats (50+… → Microscopy and Imaging Formats (45+… → …
  • Tasks that involve Data analysis
  • SKILL.md covers Overview, When to Use This Skill, Supported File Categories and Workflow, plus 4 more sections
  • Runs Python scripts from its folder; calls python

What it does

Exploratory Data Analysis is an agent skill from spacering-net/codeg. Perform comprehensive exploratory data analysis on scientific data files across 200+ file formats. This skill should be used when analyzing any scientific data file to understand its structure, content, quality, and characteristics. Automatically detects file type and generates detailed markdown reports with format-specific analysis, quality metrics, and downstream analysis recommendations. Covers chemistry, bioinformatics, microscopy, spectroscopy, proteomics, metabolomics, and general scientific data formats.

Its SKILL.md is about 3.6k tokens, which your agent loads only when the skill is triggered. The skill folder holds 11 other files, including scripts, reference files and assets (for example `assets/report_template.md`, `references/bioinformatics_genomics_formats.md` and `references/chemistry_molecular_formats.md`).

It sits in Data & Analytics, covering Data analysis and Bioinformatics. The repository describes itself as: Collaborative multi-agent AI coding workspace: aggregate sessions from Claude Code, Codex, OpenCode, Pi, Grok Build, etc. Desktop app, self-hosted server, or Docker. The licence is MIT.

When your agent uses it

  • Tasks that involve Data analysis
  • Tasks that involve Bioinformatics

Example prompts

  • “/exploratory-data-analysis”

Requirements

  • Python 3

Workflow steps

11 steps, taken from the step headings in SKILL.md.

  1. Chemistry and Molecular Formats (60+ extensions)
  2. Bioinformatics and Genomics Formats (50+ extensions)
  3. Microscopy and Imaging Formats (45+ extensions)
  4. Spectroscopy and Analytical Chemistry Formats (35+ extensions)
  5. Proteomics and Metabolomics Formats (30+ extensions)
  6. General Scientific Data Formats (30+ extensions)
  7. File Type Detection
  8. Load Format-Specific Information
  9. Perform Data Analysis
  10. Generate Comprehensive Report
  11. Save Report

What it can do on your machine

Read from SKILL.md and the folder at commit fe9fa63. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 1 file in scripts/ (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • python

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Exploratory Data Analysis loads about 3.6k tokens when it runs, and up to ~31k if it reads all its reference files. Until then it costs about 136 tokens; SKILL.md has 1,267 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~136
When it runs · the whole SKILL.md, loaded when a task matches
~3.6k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~31k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from spacering-net/codeg at commit fe9fa63, republished under its MIT licence (© spacering-net). 1,267 words, ~3,581 tokens.

Download SKILL.mdSave it as .claude/skills/exploratory-data-analysis/SKILL.md (or your agent's skills folder). This skill also uses 8 other files; get the full folder from GitHub.
name
exploratory-data-analysis
description
Perform comprehensive exploratory data analysis on scientific data files across 200+ file formats. This skill should be used when analyzing any scientific data file to understand its structure, content, quality, and characteristics. Automatically detects file type and generates detailed markdown reports with format-specific analysis, quality metrics, and downstream analysis recommendations. Covers chemistry, bioinformatics, microscopy, spectroscopy, proteomics, metabolomics, and general scientific data formats.
license
MIT license
metadata.version
1.0
metadata.skill-author
K-Dense Inc.

Exploratory Data Analysis

Overview

Perform comprehensive exploratory data analysis (EDA) on scientific data files across multiple domains. This skill provides automated file type detection, format-specific analysis, data quality assessment, and generates detailed markdown reports suitable for documentation and downstream analysis planning.

Key Capabilities:

  • Automatic detection and analysis of 200+ scientific file formats
  • Comprehensive format-specific metadata extraction
  • Data quality and integrity assessment
  • Statistical summaries and distributions
  • Visualization recommendations
  • Downstream analysis suggestions
  • Markdown report generation

When to Use This Skill

Use this skill when:

  • User provides a path to a scientific data file for analysis
  • User asks to "explore", "analyze", or "summarize" a data file
  • User wants to understand the structure and content of scientific data
  • User needs a comprehensive report of a dataset before analysis
  • User wants to assess data quality or completeness
  • User asks what type of analysis is appropriate for a file

Supported File Categories

The skill has comprehensive coverage of scientific file formats organized into six major categories:

1. Chemistry and Molecular Formats (60+ extensions)

Structure files, computational chemistry outputs, molecular dynamics trajectories, and chemical databases.

File types include: .pdb, .cif, .mol, .mol2, .sdf, .xyz, .smi, .gro, .log, .fchk, .cube, .dcd, .xtc, .trr, .prmtop, .psf, and more.

Reference file: references/chemistry_molecular_formats.md

2. Bioinformatics and Genomics Formats (50+ extensions)

Sequence data, alignments, annotations, variants, and expression data.

File types include: .fasta, .fastq, .sam, .bam, .vcf, .bed, .gff, .gtf, .bigwig, .h5ad, .loom, .counts, .mtx, and more.

Reference file: references/bioinformatics_genomics_formats.md

3. Microscopy and Imaging Formats (45+ extensions)

Microscopy images, medical imaging, whole slide imaging, and electron microscopy.

File types include: .tif, .nd2, .lif, .czi, .ims, .dcm, .nii, .mrc, .dm3, .vsi, .svs, .ome.tiff, and more.

Reference file: references/microscopy_imaging_formats.md

4. Spectroscopy and Analytical Chemistry Formats (35+ extensions)

NMR, mass spectrometry, IR/Raman, UV-Vis, X-ray, chromatography, and other analytical techniques.

File types include: .fid, .mzML, .mzXML, .raw, .mgf, .spc, .jdx, .xy, .cif (crystallography), .wdf, and more.

Reference file: references/spectroscopy_analytical_formats.md

5. Proteomics and Metabolomics Formats (30+ extensions)

Mass spec proteomics, metabolomics, lipidomics, and multi-omics data.

File types include: .mzML, .pepXML, .protXML, .mzid, .mzTab, .sky, .mgf, .msp, .h5ad, and more.

Reference file: references/proteomics_metabolomics_formats.md

6. General Scientific Data Formats (30+ extensions)

Arrays, tables, hierarchical data, compressed archives, and common scientific formats.

File types include: .npy, .npz, .csv, .xlsx, .json, .hdf5, .zarr, .parquet, .mat, .fits, .nc, .xml, and more.

Reference file: references/general_scientific_formats.md

Workflow

Step 1: File Type Detection

When a user provides a file path, first identify the file type:

  1. Extract the file extension
  2. Look up the extension in the appropriate reference file
  3. Identify the file category and format description
  4. Load format-specific information

Example:

User: "Analyze data.fastq"
→ Extension: .fastq
→ Category: bioinformatics_genomics
→ Format: FASTQ Format (sequence data with quality scores)
→ Reference: references/bioinformatics_genomics_formats.md
Step 2: Load Format-Specific Information

Based on the file type, read the corresponding reference file to understand:

  • Typical Data: What kind of data this format contains
  • Use Cases: Common applications for this format
  • Python Libraries: How to read the file in Python
  • EDA Approach: What analyses are appropriate for this data type

Search the reference file for the specific extension (e.g., search for "### .fastq" in bioinformatics_genomics_formats.md).

Step 3: Perform Data Analysis

Use the scripts/eda_analyzer.py script OR implement custom analysis:

Option A: Use the analyzer script

python
# The script automatically:
# 1. Detects file type
# 2. Loads reference information
# 3. Performs format-specific analysis
# 4. Generates markdown report

python scripts/eda_analyzer.py <filepath> [output.md]

Option B: Custom analysis in the conversation Based on the format information from the reference file, perform appropriate analysis:

For tabular data (CSV, TSV, Excel):

  • Load with pandas
  • Check dimensions, data types
  • Analyze missing values
  • Calculate summary statistics
  • Identify outliers
  • Check for duplicates

For sequence data (FASTA, FASTQ):

  • Count sequences
  • Analyze length distributions
  • Calculate GC content
  • Assess quality scores (FASTQ)

For images (TIFF, ND2, CZI):

  • Check dimensions (X, Y, Z, C, T)
  • Analyze bit depth and value range
  • Extract metadata (channels, timestamps, spatial calibration)
  • Calculate intensity statistics

For arrays (NPY, HDF5):

  • Check shape and dimensions
  • Analyze data type
  • Calculate statistical summaries
  • Check for missing/invalid values
Step 4: Generate Comprehensive Report

Create a markdown report with the following sections:

Required Sections:

  1. Title and Metadata

    • Filename and timestamp
    • File size and location

  2. Basic Information

    • File properties
    • Format identification

  3. File Type Details

    • Format description from reference
    • Typical data content
    • Common use cases
    • Python libraries for reading

  4. Data Analysis

    • Structure and dimensions
    • Statistical summaries
    • Quality assessment
    • Data characteristics

  5. Key Findings

    • Notable patterns
    • Potential issues
    • Quality metrics

  6. Recommendations

    • Preprocessing steps
    • Appropriate analyses
    • Tools and methods
    • Visualization approaches
Template Location

Use assets/report_template.md as a guide for report structure.

Step 5: Save Report

Save the markdown report with a descriptive filename:

  • Pattern: {original_filename}_eda_report.md
  • Example: experiment_data.fastq → experiment_data_eda_report.md

Detailed Format References

Each reference file contains comprehensive information for dozens of file types. To find information about a specific format:

  1. Identify the category from the extension
  2. Read the appropriate reference file
  3. Search for the section heading matching the extension (e.g., "### .pdb")
  4. Extract the format information
Show full SKILL.md (507 more words)Show less
Reference File Structure

Each format entry includes:

  • Description: What the format is
  • Typical Data: What it contains
  • Use Cases: Common applications
  • Python Libraries: How to read it (with code examples)
  • EDA Approach: Specific analyses to perform

Example lookup:

markdown
### .pdb - Protein Data Bank
**Description:** Standard format for 3D structures of biological macromolecules
**Typical Data:** Atomic coordinates, residue information, secondary structure
**Use Cases:** Protein structure analysis, molecular visualization, docking
**Python Libraries:**
- `Biopython`: `Bio.PDB`
- `MDAnalysis`: `MDAnalysis.Universe('file.pdb')`
**EDA Approach:**
- Structure validation (bond lengths, angles)
- B-factor distribution
- Missing residues detection
- Ramachandran plots

Best Practices

Reading Reference Files

Reference files are large (10,000+ words each). To efficiently use them:

  1. Search by extension: Use grep to find the specific format

    python
    import re
with open('references/chemistry_molecular_formats.md', 'r') as f:
    content = f.read()
    pattern = r'### \.pdb[^#]*?(?=###|\Z)'
    match = re.search(pattern, content, re.IGNORECASE | re.DOTALL)

  2. Extract relevant sections: Don't load entire reference files into context unnecessarily

  3. Cache format info: If analyzing multiple files of the same type, reuse the format information

Data Analysis
  1. Sample large files: For files with millions of records, analyze a representative sample
  2. Handle errors gracefully: Many scientific formats require specific libraries; provide clear installation instructions
  3. Validate metadata: Cross-check metadata consistency (e.g., stated dimensions vs actual data)
  4. Consider data provenance: Note instrument, software versions, processing steps
Report Generation
  1. Be comprehensive: Include all relevant information for downstream analysis
  2. Be specific: Provide concrete recommendations based on the file type
  3. Be actionable: Suggest specific next steps and tools
  4. Include code examples: Show how to load and work with the data

Examples

Example 1: Analyzing a FASTQ file
python
# User provides: "Analyze reads.fastq"

# 1. Detect file type
extension = '.fastq'
category = 'bioinformatics_genomics'

# 2. Read reference info
# Search references/bioinformatics_genomics_formats.md for "### .fastq"

# 3. Perform analysis
from Bio import SeqIO
sequences = list(SeqIO.parse('reads.fastq', 'fastq'))
# Calculate: read count, length distribution, quality scores, GC content

# 4. Generate report
# Include: format description, analysis results, QC recommendations

# 5. Save as: reads_eda_report.md
Example 2: Analyzing a CSV dataset
python
# User provides: "Explore experiment_results.csv"

# 1. Detect: .csv → general_scientific

# 2. Load reference for CSV format

# 3. Analyze
import pandas as pd
df = pd.read_csv('experiment_results.csv')
# Dimensions, dtypes, missing values, statistics, correlations

# 4. Generate report with:
# - Data structure
# - Missing value patterns
# - Statistical summaries
# - Correlation matrix
# - Outlier detection results

# 5. Save report
Example 3: Analyzing microscopy data
python
# User provides: "Analyze cells.nd2"

# 1. Detect: .nd2 → microscopy_imaging (Nikon format)

# 2. Read reference for ND2 format
# Learn: multi-dimensional (XYZCT), requires nd2reader

# 3. Analyze
from nd2reader import ND2Reader
with ND2Reader('cells.nd2') as images:
    # Extract: dimensions, channels, timepoints, metadata
    # Calculate: intensity statistics, frame info

# 4. Generate report with:
# - Image dimensions (XY, Z-stacks, time, channels)
# - Channel wavelengths
# - Pixel size and calibration
# - Recommendations for image analysis

# 5. Save report

Troubleshooting

Missing Libraries

Many scientific formats require specialized libraries:

Problem: Import error when trying to read a file

Solution: Provide clear installation instructions

python
try:
    from Bio import SeqIO
except ImportError:
    print("Install Biopython: uv pip install biopython")

Common requirements by category:

  • Bioinformatics: biopython, pysam, pyBigWig
  • Chemistry: rdkit, mdanalysis, cclib
  • Microscopy: tifffile, nd2reader, aicsimageio, pydicom
  • Spectroscopy: nmrglue, pymzml, pyteomics
  • General: pandas, numpy, h5py, scipy
Unknown File Types

If a file extension is not in the references:

  1. Ask the user about the file format
  2. Check if it's a vendor-specific variant
  3. Attempt generic analysis based on file structure (text vs binary)
  4. Provide general recommendations
Large Files

For very large files:

  1. Use sampling strategies (first N records)
  2. Use memory-mapped access (for HDF5, NPY)
  3. Process in chunks (for CSV, FASTQ)
  4. Provide estimates based on samples

Script Usage

The scripts/eda_analyzer.py can be used directly:

bash
# Basic usage
python scripts/eda_analyzer.py data.csv

# Specify output file
python scripts/eda_analyzer.py data.csv output_report.md

# The script will:
# 1. Auto-detect file type
# 2. Load format references
# 3. Perform appropriate analysis
# 4. Generate markdown report

The script supports automatic analysis for many common formats, but custom analysis in the conversation provides more flexibility and domain-specific insights.

Advanced Usage

Multi-File Analysis

When analyzing multiple related files:

  1. Perform individual EDA on each file
  2. Create a summary comparison report
  3. Identify relationships and dependencies
  4. Suggest integration strategies
Quality Control

For data quality assessment:

  1. Check format compliance
  2. Validate metadata consistency
  3. Assess completeness
  4. Identify outliers and anomalies
  5. Compare to expected ranges/distributions
Preprocessing Recommendations

Based on data characteristics, recommend:

  1. Normalization strategies
  2. Missing value imputation
  3. Outlier handling
  4. Batch correction
  5. Format conversions

Resources

scripts/
  • eda_analyzer.py: Comprehensive analysis script that can be run directly or imported
references/
  • chemistry_molecular_formats.md: 60+ chemistry/molecular file formats
  • bioinformatics_genomics_formats.md: 50+ bioinformatics formats
  • microscopy_imaging_formats.md: 45+ imaging formats
  • spectroscopy_analytical_formats.md: 35+ spectroscopy formats
  • proteomics_metabolomics_formats.md: 30+ omics formats
  • general_scientific_formats.md: 30+ general formats
assets/
  • report_template.md: Comprehensive markdown template for EDA reports

© spacering-net, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 8 other files (scripts, references, assets) in src-tauri/science/skills/exploratory-data-analysis of spacering-net/codeg.

  • SKILL.md
  • assets/report_template.md
  • references/bioinformatics_genomics_formats.md
  • references/chemistry_molecular_formats.md
  • references/general_scientific_formats.md
  • references/microscopy_imaging_formats.md
  • references/proteomics_metabolomics_formats.md
  • references/spectroscopy_analytical_formats.md
  • scripts/eda_analyzer.py

Open the folder on GitHubat commit fe9fa63

Used in 15 other repositories

We found 28 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 15 other GitHub owners. This page covers the copy in spacering-net/codeg, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Exploratory Data Analysis next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

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Categories

Questions about Exploratory Data Analysis

What does Exploratory Data Analysis do?

Perform comprehensive exploratory data analysis on scientific data files across 200+ file formats. Exploratory Data Analysis is an agent skill from spacering-net/codeg. Perform comprehensive exploratory data analysis on scientific data files across 200+ file formats.

When should I use Exploratory Data Analysis?

Exploratory Data Analysis fits situations like: tasks that involve Data analysis; tasks that involve Bioinformatics.

How do I install Exploratory Data Analysis in Claude Code?

Run `npx skills add spacering-net/codeg --skill exploratory-data-analysis -a claude-code`. Or copy the skill folder (src-tauri/science/skills/exploratory-data-analysis in spacering-net/codeg) into .claude/skills/exploratory-data-analysis in your project. Claude Code loads it when a task matches its description.

How do I install Exploratory Data Analysis in Codex?

Run `npx skills add spacering-net/codeg --skill exploratory-data-analysis -a codex`. Or copy the skill folder (src-tauri/science/skills/exploratory-data-analysis in spacering-net/codeg) into .agents/skills/exploratory-data-analysis in your project. Codex loads it when a task matches its description.

Can I use Exploratory Data Analysis in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add spacering-net/codeg --skill exploratory-data-analysis -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/exploratory-data-analysis, .gemini/skills/exploratory-data-analysis, .github/skills/exploratory-data-analysis and .opencode/skills/exploratory-data-analysis in your project.

What does Exploratory Data Analysis need to run?

Going by SKILL.md and its folder, Exploratory Data Analysis needs Python for the scripts in its folder and the command-line tools its instructions call (python). Our summary lists: Python 3.

Does Exploratory Data Analysis access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Exploratory Data Analysis safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Exploratory Data Analysis use?

Exploratory Data Analysis is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Exploratory Data Analysis use?

About 3.6k tokens (SKILL.md is roughly 14k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 28k tokens, read only when the agent opens those files.

What are the alternatives to Exploratory Data Analysis?

Skills that share tags, products or a category with Exploratory Data Analysis: Pyopenms (davila7/claude-code-templates, 32k stars), Gwas Database (davila7/claude-code-templates, 32k stars), Bioconductor Biomart (bioMate-AI/biomate-bioconductor-kb, 804 stars) and Bio Data Visualization Manhattan Qq Locuszoom (GPTomics/bioSkills, 1.2k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Exploratory Data Analysis?

spacering-net (a GitHub organization) maintains it in spacering-net/codeg, which has 3,833 GitHub stars. The repository holds 9 skills in this directory. The repository was last updated on October 7, 2026.

Source: spacering-net/codeg on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.