菜单
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。
默认使用会先检查来源的 Prompt；你也可以切换为直接命令，或下载本地副本。
决定是否安装前，请先阅读 SKILL.md，以及 SkillsMP 当前展示的配套文件。
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt；运行前请先检查来源。
npx skills add https://github.com/minicoohei/ai-agent-camp --skill exploratory-data-analysis命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地？可下载 SkillsMP 当前能够提供的文件。
基于 SOC 职业分类
正在显示 SKILL.md
| 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"] |
「データ分析」「EDA」「ファイル解析」「データ探索」「CSV分析」
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 add 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