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Classification Modeling

Checked
aj-geddes·vlatest·other·MIT

Builds and evaluates classification models from data to predict categories or labels with specified accuracy goals.

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 Classification 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 "Classification 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/classification-modeling/SKILL.md
Scanned
3 files, 314 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 build and evaluate classification models from data, predicting categories or labels with specified accuracy goals. It handles various classification types including binary (two classes), multiclass (more than two classes), and multi-label (multiple classes per observation). The skill utilizes common algorithms like Logistic Regression, Decision Trees, Random Forest, Gradient Boosting, SVM, and Naive Bayes to achieve these predictions.

When to use it

  • Predicting binary outcomes such as customer churn or loan default.
  • Classifying items into multiple categories, for example, product types or sentiment analysis.
  • Building credit scoring models or risk assessment systems.
  • Identifying disease diagnosis from patient data.
  • Predicting customer purchase likelihood or response to marketing campaigns.
  • Detecting fraud, anomalies, or quality defects in production systems.

Key capabilities

  • Classification Types: Supports binary, multiclass, and multi-label classification.
  • Algorithm Selection: Implements Logistic Regression, Decision Trees, Random Forest, Gradient Boosting, SVM, and Naive Bayes.
  • Evaluation Metrics: Calculates accuracy, precision, recall, F1-score, and AUC-ROC to assess model performance.
  • Python Implementation: Provides code examples using Python libraries like pandas, numpy, scikit-learn, and matplotlib for data manipulation, modeling, and visualization.

Example prompts

  • "Build a classification model to predict customer churn based on this dataset."
  • "Classify these product descriptions into categories: [list of descriptions]."
  • "Evaluate the performance of a logistic regression model using precision and recall metrics."

Tips & gotchas

  • The skill uses Python and associated libraries (scikit-learn, pandas, numpy). Ensure the agent has access to a Python environment with these dependencies installed.
  • Data standardization is performed as part of the implementation; this may impact results depending on the nature of your data.