Skip to content
TrustedSkillsbeta
Back to all skills

Grepai Storage Qdrant

Checked
yoanbernabeu·vlatest·ai-ml·MIT

Stores and retrieves vector embeddings in a Qdrant database, enabling semantic search and similarity analysis.

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-storage-qdrant -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

    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 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/storage/grepai-storage-qdrant/SKILL.md
Scanned
1 file, 319 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 Qdrant, a vector database. It allows AI agents to store and retrieve embeddings (vector representations of data) for similarity search and other applications. The agent can perform operations like adding vectors, searching for nearest neighbors, and managing datasets within the Qdrant environment.

When to use it

  • Semantic Search: Build an application where users can search a knowledge base using natural language queries that are converted into embeddings.
  • Recommendation Systems: Power personalized recommendations by finding items similar to those a user has previously interacted with, based on vector similarity.
  • Chatbot Memory: Store and retrieve past conversation turns as embeddings to maintain context in long-running chatbot sessions.
  • Image/Video Retrieval: Index images or videos using their visual features (embeddings) for efficient content retrieval.

Key capabilities

  • Vector storage and indexing
  • Nearest neighbor search
  • Dataset management (creation, deletion, updates)
  • API integration with Qdrant

Example prompts

  • "Add this text to the 'documents' collection: [text content]"
  • "Find the 5 most similar vectors to this embedding: [embedding vector]"
  • "Create a new dataset called 'product_descriptions'"

Tips & gotchas

  • Requires an active Qdrant instance and API key for authentication. Ensure your agent has the correct credentials configured.