Skip to content
TrustedSkillsbeta
Back to all skills

Embedding Service

Listed
lin-a1·vlatest·ai-ml

This Embedding Service converts text into numerical representations (embeddings) for semantic search and machine learning tasks, boosting AI performance.

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 lin-a1/skills-agent --skill embedding-service -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

lin-a1

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 1 host that is neither well-known infrastructure nor declared in the skill's frontmatter: hf-mirror.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

    hf-mirror.comundeclared

    What it found

    • services/embedding_service/Dockerfile:10contacts hf-mirror.com
      ENV HF_ENDPOINT=https://hf-mirror.com

  • 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 lin-a1/skills-agent at 22b3795060.

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

Commit
22b3795060
Manifest
services/embedding_service/SKILL.md
Scanned
4 files, 165 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 provides access to an embedding service. It allows you to convert text into numerical vector representations, also known as embeddings. These embeddings capture semantic meaning and relationships between different pieces of text, enabling tasks like similarity search and clustering.

When to use it

  • Semantic Search: Find documents or passages similar in meaning to a given query.
  • Content Clustering: Group related articles or content together based on their underlying themes.
  • Recommendation Systems: Suggest relevant items (e.g., products, articles) based on user preferences represented as embeddings.
  • Anomaly Detection: Identify unusual text entries that deviate significantly from the norm in an embedding space.

Key capabilities

  • Text to vector embedding conversion
  • Semantic similarity calculations
  • Support for various input text formats

Example prompts

  • "Generate an embedding for this sentence: 'The quick brown fox jumps over the lazy dog.'"
  • "What are the top 3 most similar embeddings to the following query: 'best Italian restaurants near me'?"
  • “Convert these product descriptions into embeddings and calculate their cosine similarity.”

Tips & gotchas

The quality of the embeddings depends on the underlying model used by the service. Consider experimenting with different models if your application requires very specific semantic nuances.