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Feature Engineering

Checked
aj-geddes·vlatest·data·MIT

This skill automatically generates new features from existing data to improve model performance and accuracy for better insights.

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 Feature Engineering, 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 "Feature Engineering" -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/feature-engineering/SKILL.md
Scanned
3 files, 320 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

The Feature Engineering skill enables AI agents to transform raw data into meaningful numerical features, enhancing model performance and interpretability. It automates the creation of new variables from existing ones through mathematical operations, domain knowledge, or automated selection techniques.

When to use it

  • You have raw datasets that require transformation before feeding them into machine learning models.
  • Your current model performance is suboptimal due to poor data representation or missing patterns.
  • You need to reduce dimensionality or handle categorical variables effectively.
  • You want to automate the iterative process of feature creation and selection.

Key capabilities

  • Generate new features from existing columns using mathematical transformations.
  • Select the most relevant features based on statistical metrics or model importance.
  • Handle missing values and outliers through imputation or removal strategies.
  • Encode categorical data into numerical formats suitable for algorithms.
  • Apply domain-specific logic to create context-aware engineered variables.

Example prompts

  • "Create polynomial features from the 'age' column in my dataset to capture non-linear relationships."
  • "Select the top 10 most important features for predicting customer churn based on correlation analysis."
  • "Encode the 'region' categorical variable using one-hot encoding and handle missing values in the 'income' field."

Tips & gotchas

Ensure your raw data is clean and properly formatted before applying feature engineering techniques. Over-engineering can lead to overfitting, so validate new features against a holdout dataset to confirm they improve generalization rather than just fitting noise.