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data-explore

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Senior-level EDA to uncover key insights

Data & Analytics#profiling#exploratory data analysis#eda#data-quality#outlier-detection#timeseries-analysis
Authormutsumi-yamamoto
Version1.0.0
Installs0

System Documentation

What problem does it solve?

This skill performs senior-level exploratory data analysis to quickly identify data quality issues, distributional properties, correlations, outliers, and time-series characteristics so analysts can decide data-cleaning and modeling priorities.

Core Features & Use Cases

  • Automated profiling: Generates comprehensive profiling reports and extended summary statistics including percentiles, skewness, kurtosis, and normality tests.
  • Visualization & diagnostics: Produces histograms, KDE, QQ plots, boxplots, correlation heatmaps, missingness matrices, STL decomposition, ACF/PACF plots, and target distribution visualizations.
  • Outlier & missingness analysis: Detects univariate and multivariate outliers (IQR, IsolationForest, LOF), assesses missingness patterns (MCAR/MAR/MNAR), computes mutual information and Cramér's V for categorical relationships.

Quick Start

Run a senior-level exploratory data analysis on the project's dataset and save the generated reports and visualizations to the data/docs/ folder and record the summary in analysis_context.md.

Dependency Matrix

Required Modules

None required

Components

Standard package

💻 Claude Code Installation

Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.

Please help me install this Skill:
Name: data-explore
Download link: https://github.com/mutsumi-yamamoto/claude-data-analysis-marketplace/archive/main.zip#data-explore

Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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