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Qdrant Vector Search

Checked
davila7·vlatest·ai-ml·MIT

Efficiently searches Qdrant vector databases using davila7's tool for semantic similarity and fast retrieval.

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 davila7/claude-code-templates --skill qdrant-vector-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

davila7

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 davila7/claude-code-templates at 79182c51fb.

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

Commit
79182c51fb
Manifest
cli-tool/components/skills/ai-research/rag-qdrant/SKILL.md
Scanned
3 files, 1,613 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

The qdrant-vector-search skill enables AI agents to perform efficient similarity searches using vector embeddings. It leverages the Qdrant vector database, allowing for fast and accurate retrieval of data based on semantic or numerical similarity.

When to use it

  • Searching through large collections of text or image embeddings for similar content.
  • Building recommendation systems that rely on vector-based similarity.
  • Enhancing search functionality in AI applications with high-dimensional data.
  • Integrating vector search capabilities into custom workflows requiring fast query responses.

Key capabilities

  • Supports efficient similarity searches using vector embeddings.
  • Integrates with Qdrant for scalable and performant vector storage and retrieval.
  • Enables semantic search by comparing vectors derived from text or other data types.

Example prompts

  • "Find the most similar documents to this query using vector search."
  • "Retrieve images that are visually similar to the provided image embedding."
  • "Search for products in the database that match this user's preferences based on embeddings."

Tips & gotchas

  • Ensure your data is properly embedded and indexed in Qdrant before performing searches.
  • Be mindful of vector dimensionality and similarity metrics when configuring search parameters.