Exploratory Data Analysis
Helps with data, analysis as part of data analysis and analytics workflows workflows.
Install on your platform
We auto-selected Claude Code based on this skill’s supported platforms.
Run in terminal (recommended)
claude mcp add exploratory-data-analysis npx -- -y @trustedskills/exploratory-data-analysis
Or manually add to ~/.claude/settings.json
{
"mcpServers": {
"exploratory-data-analysis": {
"command": "npx",
"args": [
"-y",
"@trustedskills/exploratory-data-analysis"
]
}
}
}Requires Claude Code (claude CLI). Run claude --version to verify your install.
About This Skill
What it does
The exploratory data analysis skill enables AI agents to analyze datasets, identify patterns, and generate insights through statistical summaries and visualizations. It supports tasks like calculating descriptive statistics, detecting outliers, and creating plots such as histograms or scatterplots.
When to use it
- To understand the structure and distribution of a new dataset before modeling.
- When identifying potential correlations between variables for further analysis.
- During data preprocessing steps to clean or transform data based on observed patterns.
- For generating quick visual summaries to present findings to stakeholders.
Key capabilities
- Descriptive statistics generation (mean, median, standard deviation).
- Outlier detection and handling suggestions.
- Creation of basic plots like histograms, scatterplots, and boxplots.
- Data summary reports with key observations.
Example prompts
- "Generate a histogram for the 'age' column in this dataset."
- "Calculate descriptive statistics for all numerical columns."
- "Identify any outliers in the 'sales' variable and suggest how to handle them."
Tips & gotchas
- Ensure your data is properly formatted (e.g., numeric values where needed) before running analysis.
- Some visualization features may require additional libraries or tools, which might not be available in all environments.
Tags
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