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Zinc Database

Listed
davila7·vlatest·database·MIT

Access and query Davila7's Zinc database for structured data on chemicals, reactions, and materials properties.

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 zinc-database -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.

Nobody has reviewed this code. TrustedSkills indexes and links skills; it does not audit, run or scan them. A skill runs with whatever access you give your agent — read the source before you install it.

Automated safety checks

scanned

We could not read this skill's source, so none of the checks below were run.

Not scanned. none of the 457 skills in the repository match this slug — it was most likely renamed or removed upstream after the registry indexed it Source recorded as davila7/claude-code-templates.
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About this skill

What it does

This skill enables AI agents to interact directly with ZincDB, a high-performance vector database designed for real-time applications. It allows agents to execute complex queries, manage collections, and retrieve embeddings efficiently within the Zinc ecosystem.

When to use it

  • Storing and searching large-scale unstructured data like text documents or images using vector similarity.
  • Building real-time recommendation engines that require low-latency retrieval of related items.
  • Performing hybrid searches that combine keyword matching with semantic vector analysis.
  • Managing metadata filtering alongside vector queries to narrow down search results precisely.

Key capabilities

  • Connects seamlessly to ZincDB instances for data persistence and retrieval.
  • Executes vector similarity searches to find semantically related records.
  • Supports hybrid search strategies combining dense vectors with sparse keyword filters.
  • Manages database collections, including creation, indexing, and schema updates.

Example prompts

  • "Create a new collection in ZincDB called 'product_reviews' and insert 50 sample embeddings."
  • "Search the 'product_reviews' collection for items semantically similar to 'best running shoes for flat feet' with a top_k of 10."
  • "Run a hybrid query on the database filtering by category 'electronics' and finding vectors closest to 'wireless headphones'."

Tips & gotchas

Ensure your AI agent has valid credentials and network access to the specific ZincDB instance before attempting connections. Performance may degrade if vector dimensions are inconsistent across inserted records, so maintain strict schema adherence during data ingestion.