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Google Gemini Embeddings

Checked
ovachiever·vlatest·ai-ml

This skill generates Google Gemini embeddings for your text data, enabling semantic search and powerful AI applications like similarity analysis.

Repository

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

  1. 1

    Run this in your project

    terminal
    $ npx skills add ovachiever/droid-tings --skill google-gemini-embeddings -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

ovachiever

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

    Only contacts known or declared hosts: generativelanguage.googleapis.com.

    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.

    Hosts contacted

    generativelanguage.googleapis.commodel provider

  • 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 ovachiever/droid-tings at 7acd12a754.

    The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.

Commit
7acd12a754
Manifest
skills/google-gemini-embeddings/SKILL.md
Scanned
16 files, 4,549 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 leverages Google Gemini embeddings to generate vector representations of text. These embeddings can be used for semantic search, similarity comparisons between texts, and clustering related documents. It allows AI agents to understand the meaning behind text rather than just keywords.

When to use it

  • Semantic Search: Find relevant documents or information based on the meaning of a query, even if the exact words don't match.
  • Content Recommendation: Suggest similar articles or products to users based on their browsing history or preferences.
  • Duplicate Detection: Identify near-duplicate content across a large dataset of text.
  • Clustering Documents: Group related documents together for better organization and analysis.

Key capabilities

  • Generates Google Gemini embeddings from text input.
  • Provides vector representations suitable for similarity comparisons.
  • Supports various text lengths.

Example prompts

  • "Generate an embedding for the following sentence: 'The quick brown fox jumps over the lazy dog.'"
  • "Create a vector representation of this product description."
  • "Embed these customer reviews to find similar feedback."

Tips & gotchas

  • Gemini embeddings are sensitive to nuances in language. Experiment with different prompts and text variations to achieve desired results.