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Clustering Analysis

Checked
aj-geddes·vlatest·ai-ml·MIT

Identifies patterns and groups similar data points together to reveal hidden structures within datasets.

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 Clustering Analysis, 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 "Clustering Analysis" -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/clustering-analysis/SKILL.md
Scanned
3 files, 310 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 clustering analysis on datasets, identifying hidden patterns and grouping similar data points together without predefined labels. It helps uncover underlying structures in unstructured or semi-structured information to reveal insights that are not immediately obvious.

When to use it

  • Segmenting customer bases based on purchasing behavior or demographic data to tailor marketing strategies.
  • Grouping similar news articles or social media posts by topic for automated content summarization.
  • Organizing large collections of images, documents, or files into logical categories based on feature similarity.
  • Detecting anomalies in network traffic logs by clustering normal vs. suspicious activity patterns.

Key capabilities

  • Automatic identification of natural groupings within raw data.
  • Reduction of complex datasets into manageable clusters for easier interpretation.
  • Visualization support to display cluster distributions and relationships.
  • Adaptability to various data types including numerical, categorical, and text-based inputs.

Example prompts

  • "Analyze this CSV file containing user transaction history and group customers into distinct segments based on spending frequency and amount."
  • "Cluster the provided list of research papers by their abstracts to identify emerging themes in current AI literature."
  • "Take these 500 product reviews and cluster them by sentiment and specific feature mentions to highlight areas for improvement."

Tips & gotchas

Ensure your input data is clean and normalized before running clustering, as noise can significantly distort group boundaries. Be aware that the number of clusters found depends heavily on the algorithm parameters; you may need to adjust settings or try different methods (e.g., K-Means vs. Hierarchical) to find the most meaningful segmentation for your specific use case.