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Embeddings

Checked
ruvnet·vlatest·ai-ml·MIT

This skill generates vector embeddings of text, enabling semantic similarity searches and powering advanced AI applications like recommendation systems.

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/ruflo at b636802d8c.

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

Commit
b636802d8c
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, authored by ruvnet, provides embedding generation capabilities. Embeddings are numerical representations of text that capture semantic meaning, allowing for tasks like similarity comparisons and information retrieval. It is specifically associated with the ruflo project.

When to use it

  • Semantic Search: Find documents or passages similar in meaning to a given query.
  • Clustering: Group related pieces of text together based on their semantic content.
  • Recommendation Systems: Suggest relevant items (e.g., articles, products) based on user preferences represented as embeddings.
  • Knowledge Base Indexing: Create an efficient index for retrieving information from a large knowledge base.

Key capabilities

  • Generates text embeddings
  • Associated with the ruflo project

Example prompts

  • "Generate an embedding for this sentence: 'The quick brown fox jumps over the lazy dog.'"
  • "Create embeddings for these documents and tell me which are most similar."
  • "Embed this product description so I can find related products."

Tips & gotchas

This skill requires familiarity with the ruflo project to function correctly. The specific embedding model used is not specified in the provided documentation, so results may vary depending on the context and application.