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Embeddings

Checked
ruvnet·vlatest·ai-ml·MIT

Generates vector representations (embeddings) of text, enabling semantic similarity searches and powerful AI applications like clustering & recommendation.

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 ruvnet/ruflo --skill embeddings --full-depth -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

ruvnet

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 ruvnet/claude-flow at 49da6ecc44.

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

Commit
49da6ecc44
Manifest
.agents/skills/embeddings/SKILL.md
Scanned
1 file, 60 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 generates vector embeddings from text. Embeddings represent the semantic meaning of text as numerical vectors, allowing for tasks like similarity comparisons and information retrieval. It's particularly useful when you need to understand relationships between pieces of text beyond simple keyword matching.

When to use it

  • Semantic Search: Find documents or passages similar in meaning to a user query.
  • Clustering: Group related documents together based on their content.
  • Recommendation Systems: Suggest relevant items (e.g., articles, products) based on the semantic similarity of descriptions.
  • Anomaly Detection: Identify unusual text entries that deviate significantly from the norm in a dataset.

Key capabilities

  • Generates vector embeddings for textual input.
  • Represents text as numerical vectors.
  • Facilitates semantic comparison between texts.

Example prompts

  • "Generate an embedding for this sentence: 'The quick brown fox jumps over the lazy dog.'"
  • "Create embeddings for these three paragraphs and tell me which are most similar."
  • “What is the embedding of this product description?”

Tips & gotchas

This skill requires a text input. The quality of the generated embeddings depends on the clarity and context of the provided text.