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Recommendation Engine

Checked
aj-geddes·vlatest·ai-ml·MIT

This Recommendation Engine suggests relevant content or products based on user input, boosting engagement and personalization.

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 Engine, 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 Engine" -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-engine/SKILL.md
Scanned
3 files, 395 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 Recommendation Engine skill enables AI agents to analyze user preferences, historical data, and contextual constraints to generate tailored suggestions for products, content, or services. It processes complex input parameters to simulate the logic of collaborative filtering or content-based matching systems.

When to use it

  • Curating personalized reading lists based on a user's past book reviews and genre interests.
  • Suggesting relevant software tools during a workflow planning session for a specific project type.
  • Generating movie or music recommendations by analyzing mood descriptors and viewing history.
  • Identifying potential candidates for a job role by matching resume keywords against required skill sets.

Key capabilities

  • Analyzes explicit user preferences and implicit behavioral patterns to drive suggestions.
  • Generates diverse recommendation lists based on weighted criteria provided in the prompt.
  • Adapts output format (e.g., ranked list, summary table) based on specific agent instructions.

Example prompts

  • "Act as a recommendation engine for indie films. Suggest 5 movies similar to 'Everything Everywhere All At Once' focusing on sci-fi elements and multiverse themes."
  • "Recommend three open-source project management tools suitable for a remote team of five developers who prioritize security and low cost."
  • "Based on my history of reading hardcover fiction with strong female protagonists, suggest 4 upcoming novels to add to my library."

Tips & gotchas

Ensure you provide sufficient context about the user's specific tastes or constraints; vague inputs will yield generic results. While this skill mimics recommendation logic, it relies entirely on the information provided in the prompt and does not access real-time external databases unless connected via other tools.