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Gemini Embeddings

Listed
akrindev·vlatest·ai-ml

Generates vector representations (embeddings) of text using Gemini, enabling semantic search and similarity analysis for 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 akrindev/google-studio-skills --skill 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

akrindev

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 failed 1 of 6 checks. A failure is not proof of bad intent; it means something in the code needs a human to look at it before you run it.

  • No undeclared network calls: failed

    Contacts 5 hosts that are neither well-known infrastructure nor declared in the skill's frontmatter: dotenvx.com, feross.org, opencollective.com, paypal.me, www.patreon.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

    github.comsource hostwww.patreon.comundeclaredfeross.orgundeclareddotenvx.comundeclaredpaypal.meundeclaredopencollective.comundeclared

    What it found (3 of 8)

    • skills/gemini-embeddings/scripts/package-lock.json:192contacts www.patreon.com
      "url": "https://www.patreon.com/feross"
    • skills/gemini-embeddings/scripts/package-lock.json:705contacts www.patreon.com
      "url": "https://www.patreon.com/feross"
    • skills/gemini-embeddings/scripts/package-lock.json:196contacts feross.org
      "url": "https://feross.org/support"

  • 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 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 akrindev/google-studio-skills at aeec3b863d.

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

Commit
aeec3b863d
Manifest
skills/gemini-embeddings/SKILL.md
Scanned
4 files, 1,462 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's Gemini models to generate embeddings, which are numerical representations of text. These embeddings can be used for semantic search, similarity comparisons between texts, and as input features for machine learning models. It allows you to convert text into a vector format suitable for various AI tasks.

When to use it

  • Semantic Search: Find documents or passages that are conceptually similar to a given query, even if they don't share the same keywords.
  • Content Clustering: Group related articles or pieces of content together based on their meaning.
  • Recommendation Systems: Suggest items (e.g., products, videos) to users based on the similarity between item descriptions.
  • Anomaly Detection: Identify unusual text entries that deviate significantly from the norm in a dataset.

Key capabilities

  • Generates embeddings using Google's Gemini models.
  • Converts text input into vector representations.
  • Supports various text lengths for embedding generation.

Example prompts

  • "Generate an embedding for this sentence: 'The quick brown fox jumps over the lazy dog.'"
  • "Create an embedding of this product description: 'High-performance running shoes with breathable mesh upper.'"
  • “Embed these two sentences and tell me their similarity score.”

Tips & gotchas

  • Gemini embeddings are sensitive to nuances in language; ensure your input text is clear and representative of the intended meaning.
  • Longer texts may require more processing time and resources.