exploratory-data-analysis
Perform comprehensive exploratory data analysis on scientific data files across 200+ file formats.
Skill by beita6969
Chalk Labs
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Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.
npx agentmarket install skill/beita6969-exploratory-data-analysis
Asks which of your tools to add it to (Claude Code, Codex, Cursor, VS Code, Windsurf, Gemini CLI, Claude Desktop) and shows the change first. See https://agentmart.dev/cli.
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About
Use cases: documents, data, writing, research
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What's inside
- Skill 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
- Metadata
- skill-author: K-Dense Inc.
- Instructions
- 439 lines in SKILL.md · view on GitHub
Agent Skills spec issues
- files: referenced but missing: references/chemistry_molecular_formats.md, references/bioinformatics_genomics_formats.md, references/microscopy_imaging_formats.md, references/spectroscopy_analytical_formats.md, references/proteomics_metabolomics_formats.md
Files (1)
- SKILL.md
SKILL.md (first lines)
# 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:
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Signals
- Stars
- 1,019
- Forks
- 109
- Open issues
- 1
- Last commit (this folder)
- Mar 12, 2026 (7 months ago)
From GitHub (fetched Oct 24, 2026).
Details
- License
- MIT
- Repository
- github.com/beita6969/ScienceClaw
- Website
- scienceclaw.science
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
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exploratory-data-analysis skill. Agent Mart. https://agentmart.dev/skills/beita6969-exploratory-data-analysis (last updated 2026-10-24)