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Regression Modeling

Checked
aj-geddes·vlatest·testing·MIT

Predict continuous values like sales or temperature using statistical models built from provided data.

Repository

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

Installs under a different name. Its SKILL.md names it Regression Modeling, not the name it is listed under, so the command below uses that name.
  1. 1

    Run this in your project

    terminal
    $ npx skills add aj-geddes/useful-ai-prompts --skill "Regression Modeling" -a claude-code

  2. Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

aj-geddes

Taken from the source listing. We haven't verified who controls this account.

Install target

Live repository

Not pinned. Installing fetches whatever the repository holds at the time you run the command.

No human has reviewed this code. The checks below are a static scan of the files at one commit — nobody has run this skill or judged whether it works. A skill runs with whatever access you give your agent, so read the source before you install it.

Automated safety checks

scanned

This skill passed all 6 checks. We read its files at one commit and looked for the things that make a skill dangerous to run — not for whether it works.

  • Declares what it does: passed

    SKILL.md parses and declares a name and a description.

    SKILL.md exists, its frontmatter parses, and it declares a name and a description.

  • No undeclared network calls: passed

    Makes no network calls.

    Every host the skill dials — from its scripts, its config endpoints or the commands it tells the agent to run — is either well-known package and API infrastructure or declared in its own frontmatter. Links in documentation are not counted.

  • No obfuscated payloads: passed

    No encoded or obfuscated payloads.

    No base64, hex or compressed blob is decoded and then executed, and no encoded shell commands.

  • No credential access: passed

    Reads no keys, tokens, keychains or credential files.

    Neither the code nor the SKILL.md instructions read SSH keys, cloud credentials, keychains, token stores, browser cookie databases or dotfiles that hold secrets.

  • No pipe-to-shell installers: passed

    No pipe-to-shell or download-and-run installers.

    No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.

  • Pinned to a commit: passed

    Scanned aj-geddes/useful-ai-prompts at 3f5182cfd7.

    The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.

Commit
3f5182cfd7
Manifest
skills/regression-modeling/SKILL.md
Scanned
3 files, 319 lines
This is a static scan, not a review. It reads the skill's files; it does not run them, and it cannot tell you whether the skill is any good or whether the code does what its description says. A skill runs with whatever access you give your agent.
What each check looks for →

About this skill

What it does

This skill enables AI agents to perform statistical regression analysis on datasets, identifying relationships between variables and predicting outcomes based on historical data. It automates the process of fitting mathematical models to data points to minimize errors and validate hypotheses.

When to use it

  • Analyzing sales trends over time to forecast future revenue based on marketing spend.
  • Determining how temperature and humidity affect crop yield in agricultural research.
  • Evaluating the impact of specific variables, such as interest rates, on housing prices.
  • Validating scientific experiments by checking if observed data aligns with theoretical linear or non-linear models.

Key capabilities

  • Fits various regression models (linear, polynomial) to provided datasets.
  • Calculates key statistical metrics like R-squared and p-values to assess model fit.
  • Identifies significant predictors and quantifies their impact on the target variable.
  • Generates visualizations of data points alongside fitted regression lines.

Example prompts

  • "Run a linear regression on this CSV file containing hours studied versus exam scores."
  • "Fit a polynomial regression model to predict stock prices based on the last 12 months of trading data."
  • "Analyze these sensor readings to determine if there is a significant correlation between pressure and temperature."

Tips & gotchas

Ensure your dataset is clean and free of outliers before running analysis, as regression models are sensitive to extreme values. Always interpret the resulting coefficients in the context of your specific domain rather than relying solely on statistical significance.