exploratory-data-analysis
Automated analysis of 200+ scientific data formats with format-specific insights and quality assessment.
- Supports six major scientific domains: chemistry, bioinformatics, microscopy, spectroscopy, proteomics, and metabolomics, with 60+ formats per category
- Auto-detects file type and generates detailed markdown reports including metadata extraction, statistical summaries, data quality metrics, and visualization recommendations
- Includes reference documentation for each format covering typical data content, Python libraries for reading, and domain-specific analysis approaches
- Provides downstream analysis suggestions and preprocessing recommendations tailored to each file type and detected data characteristics
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
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