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Recommendation System

Checked
aj-geddes·vlatest·other·MIT

This Recommendation System suggests relevant content or products based on user preferences, boosting engagement and driving targeted suggestions.

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 Recommendation System, 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 "Recommendation System" -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/recommendation-system/SKILL.md
Scanned
3 files, 336 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 analyze user preferences and historical data to generate personalized suggestions for items, services, or content. It simulates the logic of collaborative filtering or content-based matching to curate lists tailored to specific individual needs.

When to use it

  • Curating a list of movies, books, or music based on a user's stated favorite genres and past ratings.
  • Suggesting relevant products for an e-commerce bot by analyzing a customer's purchase history.
  • Recommending potential connections or networking contacts within a professional AI workflow based on shared skills.
  • Generating study resource lists tailored to a student's current curriculum and learning pace.

Key capabilities

  • Analyzes user input to identify specific preferences and constraints.
  • Generates ranked lists of relevant items based on similarity logic.
  • Adapts suggestions dynamically as new data or feedback is provided.

Example prompts

  • "Recommend 5 sci-fi movies similar to 'Blade Runner' that have a runtime under 2 hours."
  • "Based on my history of buying ergonomic office gear, suggest three new standing desk options available in Europe."
  • "Act as a career coach and recommend 3 networking events for a software engineer specializing in AI ethics."

Tips & gotchas

Ensure you provide sufficient context about your preferences or past interactions to improve the accuracy of the generated recommendations. The quality of output depends heavily on the specificity of the criteria you define during the prompt.