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Upstash Vector Db Skills

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gocallum·vlatest·ai-ml

This skill integrates Upstash with Vector DBs for efficient similarity search and retrieval of vector embeddings, boosting AI applications.

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 gocallum/nextjs16-agent-skills --skill upstash-vector-db-skills -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

gocallum

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 gocallum/nextjs16-agent-skills at 531ee5767d.

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

Commit
531ee5767d
Manifest
skills/upstash-vector-db-skills/SKILL.md
Scanned
1 file, 242 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 interact with Upstash Vector, a serverless vector database designed for embedding storage and retrieval. It allows agents to perform semantic searches, manage vector collections, and store high-dimensional data efficiently within a Next.js environment.

When to use it

  • Storing and retrieving embeddings for RAG (Retrieval-Augmented Generation) applications.
  • Performing similarity searches on unstructured text or image data.
  • Building recommendation systems that rely on vector proximity.
  • Managing metadata alongside vector data for filtered queries.

Key capabilities

  • Connect to Upstash Vector instances via standard SDKs.
  • Create and manage named collections for different data types.
  • Insert, update, and delete vectors with associated metadata.
  • Execute hybrid search combining keyword and vector similarity.
  • Retrieve top-k results based on cosine or dot product similarity.

Example prompts

  • "Store a new document embedding into the 'support_tickets' collection with metadata including user_id and timestamp."
  • "Search for similar customer feedback entries related to 'billing issues' and return the top 5 matches with their scores."
  • "Update an existing vector record in the 'product_catalog' collection with new embedding data and refresh its metadata fields."

Tips & gotchas

Ensure your Upstash Vector instance is properly provisioned and credentials are securely stored before initializing the connection. Be mindful of token limits when inserting large batches of vectors to avoid rate throttling or request failures.