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Google Gemini Embeddings

Checked
jackspace·vlatest·ai-ml·MIT

Generates vector embeddings from text using Google Gemini, enabling semantic search, similarity analysis, and powerful AI applications.

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 jackspace/claudeskillz --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

jackspace

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 jackspace/claudeskillz at aef4e299d1.

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

Commit
aef4e299d1
Manifest
skills/google-gemini-embeddings/SKILL.md
Scanned
3 files, 1,213 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 allows AI agents to generate embeddings using Google's Gemini models. Embeddings are numerical representations of text that capture semantic meaning, enabling tasks like similarity search and clustering. It leverages the power of Gemini for high-quality vector representations suitable for various NLP applications.

When to use it

  • Semantic Search: Find documents or passages similar in meaning to a given query.
  • Clustering: Group related text documents together based on their content.
  • Recommendation Systems: Recommend items (e.g., articles, products) based on the similarity of their descriptions.
  • Anomaly Detection: Identify unusual or outlier text entries that deviate from typical patterns.

Key capabilities

  • Generates embeddings using Google Gemini models.
  • Provides numerical vector representations of text.
  • Supports various input 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: 'A comfortable and stylish running shoe with excellent cushioning.'"
  • "What is the embedding for this customer review?"

Tips & gotchas

  • Ensure your AI agent has access to Google Gemini API keys.
  • Longer input texts may incur higher costs depending on the specific Gemini model used.