/exploratory-data-analysis

name: exploratory-data-analysis
description: "200以上のファイル形式に対応した探索的データ分析（EDA）スキル。 「データを分析して」「EDAして」「ファイルの中身を調べて」等のリクエストで発動。 ファイル自動検出、品質評価、統計サマリー、可視化推奨を含むレポート生成。"
license: MIT license
metadata:
    skill-author: K-Dense Inc.
source: github.com/K-Dense-AI/claude-scientific-skills@main
triggers:
  - exploratory-data-analysis
  - EDA
  - データ探索
  - 探索的データ分析
  - ファイル解析
  - データ品質チェック
  - データプロファイリング

トリガーワード

「データ分析」「EDA」「ファイル解析」「データ探索」「CSV分析」

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**

# 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 Comp