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Embedding Search

Checked
winsorllc·vlatest·ai-ml

Find relevant documents or data points based on semantic similarity using vector embeddings powered by winsorllc.

Repository

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

Installs under a different name. Its SKILL.md names it Embedding Search, not the name it is listed under, so the command below uses that name.
  1. 1

    Run this in your project

    terminal
    $ npx skills add winsorllc/upgraded-carnival --skill "Embedding Search" -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

winsorllc

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 winsorllc/upgraded-carnival at 8fd52c772f.

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

Commit
8fd52c772f
Manifest
.pi/skills/embedding-search/SKILL.md
Scanned
2 files, 410 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 enables AI agents to perform semantic searches using embeddings. It allows for finding relevant information based on meaning rather than keyword matches, improving search accuracy and recall. The agent can retrieve documents or data points that are conceptually similar to a given query, even if they don't share the same words.

When to use it

  • Knowledge Base Search: Quickly find relevant articles or documentation within a large internal knowledge base.
  • Product Recommendation: Suggest products based on user descriptions of desired features or benefits.
  • Content Discovery: Help users discover related content based on an article they are currently reading.
  • Question Answering: Retrieve answers to complex questions that require understanding the context and meaning behind the query.

Key capabilities

  • Semantic search using embeddings
  • Retrieval of conceptually similar information
  • Improved search accuracy compared to keyword-based methods

Example prompts

  • "Find documents related to 'customer churn prevention strategies'."
  • "What products are similar to a lightweight, waterproof hiking backpack?"
  • "I'm looking for articles about improving team collaboration."

Tips & gotchas

The effectiveness of this skill depends on the quality and relevance of the underlying embeddings. Ensure your data is properly vectorized and indexed for optimal search results.