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Automatically detects and analyzes 200+ scientific file formats (chemistry, bioinformatics, microscopy, etc.), generating detailed markdown reports with quality metrics and downstream analysis recommendations.
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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.
assets/report_template.mdreferences/bioinformatics_genomics_formats.mdreferences/chemistry_molecular_formats.mdreferences/general_scientific_formats.mdreferences/microscopy_imaging_formats.mdreferences/proteomics_metabolomics_formats.mdreferences/spectroscopy_analytical_formats.mdscripts/eda_analyzer.pyAutomatically detects and analyzes 200+ scientific data file formats, generating detailed markdown reports with format-specific metadata, quality metrics, and downstream analysis recommendations.
Detects 200+ scientific file formats and performs format-specific exploratory data analysis including metadata extraction, quality assessment, stats, and reports for chemistry, bioinformatics, microscopy data.
Performs exploratory data analysis on scientific files: detects 200+ formats, categorizes by type (tabular, sequence, image, spectral, structural, omics), assesses quality, generates reports. For initial data exploration.
Share bugs, ideas, or general feedback.
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:
Use this skill when:
The skill has comprehensive coverage of scientific file formats organized into six major categories:
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
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
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
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
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
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
When a user provides a file path, first identify the file type:
Example:
User: "Analyze data.fastq"
→ Extension: .fastq
→ Category: bioinformatics_genomics
→ Format: FASTQ Format (sequence data with quality scores)
→ Reference: references/bioinformatics_genomics_formats.md
Based on the file type, read the corresponding reference file to understand:
Search the reference file for the specific extension (e.g., search for "### .fastq" in bioinformatics_genomics_formats.md).
Use the scripts/eda_analyzer.py script OR implement custom analysis:
Option A: Use the analyzer script
# 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):
For sequence data (FASTA, FASTQ):
For images (TIFF, ND2, CZI):
For arrays (NPY, HDF5):
Create a markdown report with the following sections:
Title and Metadata
Basic Information
File Type Details
Data Analysis
Key Findings
Recommendations
Use assets/report_template.md as a guide for report structure.
Save the markdown report with a descriptive filename:
{original_filename}_eda_report.mdexperiment_data.fastq → experiment_data_eda_report.mdEach reference file contains comprehensive information for dozens of file types. To find information about a specific format:
Each format entry includes:
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
Reference files are large (10,000+ words each). To efficiently use them:
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)
Extract relevant sections: Don't load entire reference files into context unnecessarily
Cache format info: If analyzing multiple files of the same type, reuse the format information
# 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
# 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
# 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
Many scientific formats require specialized libraries:
Problem: Import error when trying to read a file
Solution: Provide clear installation instructions
try:
from Bio import SeqIO
except ImportError:
print("Install Biopython: uv pip install biopython")
Common requirements by category:
biopython, pysam, pyBigWigrdkit, mdanalysis, cclibtifffile, nd2reader, aicsimageio, pydicomnmrglue, pymzml, pyteomicspandas, numpy, h5py, scipyIf a file extension is not in the references:
For very large files:
The scripts/eda_analyzer.py can be used directly:
# 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.
When analyzing multiple related files:
For data quality assessment:
Based on data characteristics, recommend:
eda_analyzer.py: Comprehensive analysis script that can be run directly or importedchemistry_molecular_formats.md: 60+ chemistry/molecular file formatsbioinformatics_genomics_formats.md: 50+ bioinformatics formatsmicroscopy_imaging_formats.md: 45+ imaging formatsspectroscopy_analytical_formats.md: 35+ spectroscopy formatsproteomics_metabolomics_formats.md: 30+ omics formatsgeneral_scientific_formats.md: 30+ general formatsreport_template.md: Comprehensive markdown template for EDA reports