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Found 11 Skills
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.
Analyze datasets to discover patterns, anomalies, and relationships. Use when exploring data files, generating statistical summaries, checking data quality, or creating visualizations. Supports CSV, Excel, JSON, Parquet, and more.
EDA toolkit. Analyze CSV/Excel/JSON/Parquet files, statistical summaries, distributions, correlations, outliers, missing data, visualizations, markdown reports, for data profiling and insights.
This skill should be used when working with CSV files to create interactive data visualizations, generate statistical plots, analyze data distributions, create dashboards, or perform automatic data profiling. It provides comprehensive tools for exploratory data analysis using Plotly for interactive visualizations.
Statistical visualization. Scatter, box, violin, heatmaps, pair plots, regression, correlation matrices, KDE, faceted plots, for exploratory analysis and publication figures.
A Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics. Great for exploring relationships between variables and visualizing distributions. Use for statistical data visualization, exploratory data analysis (EDA), relationship plots, distribution plots, categorical comparisons, regression visualization, heatmaps, cluster maps, and creating publication-quality statistical graphics from Pandas DataFrames.
Use this skill when performing exploratory data analysis, statistical testing, data visualization, or building predictive models. Triggers on EDA, pandas, matplotlib, seaborn, hypothesis testing, A/B test analysis, correlation, regression, feature engineering, and any task requiring data analysis or statistical inference.
Exploratory Data Analysis skill for CSV and parquet datasets with deterministic profiling, drift/anomaly scans, contract generation and validation, and optional memory writeback into skill-system-memory. The implementation is Polars-first (lazy scan for large files and early `--sample` head), includes high-cardinality guards for profile/importance/contract flows, and supports categorical correlation with Cramer's V. Use when building or reviewing tabular fraud/risk/data-quality workflows, profiling new datasets, checking leakage or drift, or saving/validating data contracts.
Best practices for doing quick exploratory data analysis with minimal code and a Pandas .plot like API using HoloViews hvPlot.
EDA, dashboards, Matplotlib, Seaborn, Plotly, and BI tools. Use for creating visualizations, exploratory analysis, or dashboards.
Data analysis expert for statistics, visualization, pandas, and exploration