exploratory-data-analysis

name	exploratory-data-analysis
description	Inspect scientific data and generate a Markdown structure-and-quality report. Use when triaging tabular, array, sequence, HDF5, JSON, or raster files before downstream analysis.

Exploratory Data Analysis

Overview

Inspect scientific files before downstream analysis. The bundled script recognizes more than 100 simple and compound suffixes and writes a bounded Markdown report. It performs content-level analysis only for the common formats listed below; other recognized formats receive file metadata and a reference-catalog entry.

The six reference files contain 239 format entries. Some entries describe the same suffix in different domain contexts, so this is not a count of unique formats or implemented parsers.

Bundled content parsers:

• NumPy arrays (.npy, .npz), CSV/TSV samples, JSON, and HDF5
• FASTA and FASTQ, including common gzip-compressed suffixes
• TIFF/OME-TIFF, PNG, and JPEG raster images
• Reference-only metadata for every other recognized suffix
• Representative streaming analyzers for PDB/SDF/SMILES, MGF/mzML/mzXML, and mzTab families; proprietary binary formats remain reference-only unless their project environment supplies a reader.

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 structure-and-quality report before analysis
• User wants to assess data quality or completeness
• User asks what type of analysis is appropriate for a file

Quick Reference

Task	Action
Unknown file	Detect extension and load the matching reference file before analyzing content.
Tabular data	Summarize dimensions, types, missingness, ranges, outliers, duplicates, and candidate keys.
Sequence data	Count records, length distribution, GC content, quality scores when available, and format issues.
Imaging or arrays	Report shape, channels/axes, dtype, value range, metadata, and scale/calibration when present.
Final report	Write a concise Markdown EDA report with findings, caveats, and next analysis options.

Supported File Categories

The reference catalog is organized into six categories. These entries guide custom analysis; they do not imply that eda_analyzer.py parses every listed format.

1. Chemistry and Molecular Formats (43 reference entries)

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 (44 reference entries)

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 (41 reference entries)

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 (43 reference entries)

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 (36 reference entries)

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 (32 reference entries)

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

Instructions

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 bundled script for its supported parsers, or implement a domain-specific analysis after reading the relevant reference entry.

Option A: Run the analyzer from the checkout

uv run skills/exploratory-data-analysis/scripts/eda_analyzer.py <filepath> [output.md]

Option B: Run the installed analyzer

uv run ~/.agents/skills/exploratory-data-analysis/scripts/eda_analyzer.py <filepath> [output.md]

PEP 723 metadata in the script creates an isolated environment with the libraries used by its content parsers.

Option C: 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 the Report

The bundled script reports file metadata, the matching reference entry, sampled or full content statistics, parser errors, and format-level follow-up options. Label every sample explicitly. For a custom analysis, use 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 short entries 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

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:

### .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

Input Requirements

• One or more local scientific data files.
• Permission to read the files and enough disk/RAM for the requested inspection.
• Domain context when available, such as assay type, organism, instrument, or expected sample count.
• Optional output path for the Markdown report.

Output

• Markdown EDA report next to the input file or at the requested output path.
• Basic file metadata: path, size, modified time, detected format, and relevant parser.
• Data structure summary, quality observations, likely issues, and downstream recommendations.
• Any generated figures, tables, or temporary summaries needed to support the report.

Quality Gates

• File type detection and selected reference file are stated.
• The report distinguishes observed facts from downstream recommendations.
• Missing values, malformed records, parser failures, or unreadable sections are reported explicitly.
• Large-file sampling is labeled as sampling and does not imply full-file statistics.
• Suggested downstream analyses match the detected format and available metadata.
• NumPy .npy inspection uses memory mapping, FASTA uses full-file streaming, FASTQ is an explicitly bounded streaming sample, and each implemented format family has a fixture.

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

   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. Cover the evidence: Include the observations needed to choose the next 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

# 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

# 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

# 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

Custom analysis of formats outside the bundled parsers may require specialized libraries:

Problem: Import error when trying to read a file

Solution: Add the parser to the project environment with uv, or run a one-off command with uv run --with.

uv run --with biopython python analysis.py

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:

# From this repository
uv run skills/exploratory-data-analysis/scripts/eda_analyzer.py data.csv

# From an installed skill, with an explicit output file
uv run ~/.agents/skills/exploratory-data-analysis/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 performs content analysis for NumPy, CSV/TSV, JSON, HDF5, FASTA/FASTQ, and common raster images. It emits a reference_only scope for recognized formats without a bundled parser.

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: Bounded analyzer for the supported common formats

references/

• chemistry_molecular_formats.md: 43 chemistry/molecular reference entries
• bioinformatics_genomics_formats.md: 44 bioinformatics reference entries
• microscopy_imaging_formats.md: 41 imaging reference entries
• spectroscopy_analytical_formats.md: 43 spectroscopy reference entries
• proteomics_metabolomics_formats.md: 36 omics reference entries
• general_scientific_formats.md: 32 general-data reference entries

assets/

• report_template.md: Markdown template for EDA reports