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Dimensionality Reduction

Checked
aj-geddes·vlatest·ai-ml·MIT

This skill reduces complex data by identifying key patterns, simplifying analysis and improving model performance.

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 Dimensionality Reduction, 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 "Dimensionality Reduction" -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/dimensionality-reduction/SKILL.md
Scanned
3 files, 316 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 reduces the number of features in a dataset while preserving important information. It aims to improve model efficiency, enable visualization of high-dimensional data, and simplify analysis. The skill utilizes techniques like Principal Component Analysis (PCA), t-Distributed Stochastic Neighbor Embedding (t-SNE), Uniform Manifold Approximation and Projection (UMAP), feature selection, and feature extraction.

When to use it

  • When working with datasets containing a large number of features.
  • To visualize complex data in 2D or 3D space.
  • To decrease the computational complexity and training time for machine learning models.
  • When needing to remove redundant or highly correlated features from your dataset.
  • As a preprocessing step before clustering or classification tasks.

Key capabilities

  • PCA (Principal Component Analysis): Reduces dimensionality by identifying principal components that capture most of the variance in the data.
  • t-SNE (t-Distributed Stochastic Neighbor Embedding): A technique for visualizing high-dimensional datasets by reducing them to lower dimensions while preserving local relationships between data points.
  • UMAP (Uniform Manifold Approximation and Projection): Another dimensionality reduction technique focused on visualization, similar in purpose to t-SNE.
  • Feature Selection: Selects a subset of the original features based on their importance.
  • Feature Extraction: Creates new features from existing ones.

Example prompts

  • "Reduce this dataset's dimensions using PCA and show me the explained variance ratio."
  • "Visualize this high-dimensional data in 2D using t-SNE."
  • "Apply UMAP to reduce the number of features while preserving data structure."

Tips & gotchas

  • The skill requires a dataset that can be processed with Python libraries like pandas, numpy, matplotlib and scikit-learn.
  • Dimensionality reduction techniques can sometimes distort relationships in the data; careful interpretation is needed.
  • Standardizing your data (e.g., using StandardScaler) often improves results for PCA and related methods.