Correlation Analysis
Identifies hidden relationships between datasets to reveal patterns and inform strategic decision-making.
Install on your platform
We auto-selected Claude Code based on this skill’s supported platforms.
Run in terminal (recommended)
claude mcp add correlation-analysis npx -- -y @trustedskills/correlation-analysis
Or manually add to ~/.claude/settings.json
{
"mcpServers": {
"correlation-analysis": {
"command": "npx",
"args": [
"-y",
"@trustedskills/correlation-analysis"
]
}
}
}Requires Claude Code (claude CLI). Run claude --version to verify your install.
About This Skill
The Correlation Analysis skill enables AI agents to identify and quantify relationships between variables within datasets. It processes numerical inputs to calculate correlation coefficients, revealing how changes in one metric influence another. This capability transforms raw data into actionable insights by highlighting patterns that might otherwise remain hidden.
When to use it
- Analyzing sales data to determine if marketing spend directly impacts revenue growth.
- Evaluating weather conditions against crop yield statistics for agricultural planning.
- Investigating the relationship between study hours and exam scores in educational research.
- Assessing how temperature fluctuations correlate with energy consumption levels.
Key capabilities
- Calculates Pearson correlation coefficients to measure linear relationships.
- Processes structured numerical datasets provided by the user.
- Identifies positive, negative, or neutral associations between paired variables.
- Outputs clear interpretations of statistical strength and direction.
Example prompts
- "Analyze this dataset of monthly temperatures and ice cream sales to find the correlation coefficient."
- "Calculate the relationship between hours spent studying and final exam grades provided in the CSV below."
- "Determine if there is a strong positive or negative correlation between stock market indices and interest rates."
Tips & gotchas
Ensure your input data is clean, numerical, and properly paired before analysis, as missing values can skew results. This skill focuses on linear relationships; it may not detect complex non-linear patterns without additional configuration.
Tags
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