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Grepai Embeddings Openai

Checked
yoanbernabeu·vlatest·ai-ml·MIT

Generates OpenAI embeddings from text data using grepai's infrastructure for efficient vector representation and similarity search.

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 yoanbernabeu/grepai-skills --skill grepai-embeddings-openai -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

yoanbernabeu

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

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    No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.

  • Pinned to a commit: passed

    Scanned yoanbernabeu/grepai-skills at 382d40261c.

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

Commit
382d40261c
Manifest
skills/embeddings/grepai-embeddings-openai/SKILL.md
Scanned
1 file, 259 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.
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About this skill

What it does

This skill enables AI agents to generate text embeddings using OpenAI's embedding models, converting raw text into numerical vectors for semantic analysis. It facilitates tasks like finding similar documents, clustering data, and measuring semantic similarity between different pieces of content.

When to use it

  • Semantic Search: Retrieving relevant documents from a large corpus based on meaning rather than exact keyword matches.
  • Document Clustering: Automatically grouping unstructured text data into meaningful categories without predefined labels.
  • Similarity Scoring: Quantifying how closely related two different texts or queries are for recommendation systems.
  • Data Preprocessing: Converting natural language inputs into a format suitable for downstream machine learning algorithms.

Key capabilities

  • Generates high-dimensional vector representations of text input.
  • Integrates directly with OpenAI's embedding API infrastructure.
  • Supports various text lengths within standard model limits.
  • Returns numerical arrays optimized for distance calculations.

Example prompts

  • "Convert this customer support ticket into a vector embedding for similarity search."
  • "Generate embeddings for these 10 product descriptions to cluster them by category."
  • "Calculate the semantic similarity score between my query and this document snippet using OpenAI embeddings."

Tips & gotchas

Ensure your text input adheres to OpenAI's token limits before sending requests, as exceeding these can cause errors or truncation. For best results in clustering tasks, normalize the resulting vectors to unit length to ensure distance metrics reflect semantic similarity rather than magnitude differences.