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Agentdb Vector Search

Checked
ovachiever·vlatest·ai-ml

Quickly retrieves relevant data from a vector database using semantic search queries for enhanced insights.

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 AgentDB Vector 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 ovachiever/droid-tings --skill "AgentDB 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

ovachiever

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 ovachiever/droid-tings at 7acd12a754.

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

Commit
7acd12a754
Manifest
skills/agentdb-vector-search/SKILL.md
Scanned
1 file, 300 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 search and retrieve information from a vector database called AgentDB. It allows for semantic searches, finding results based on meaning rather than exact keyword matches. The agent can leverage this capability to answer questions or perform tasks requiring knowledge stored within the AgentDB.

When to use it

  • Knowledge Retrieval: An agent needs to answer user questions about a specific domain where information is stored in AgentDB (e.g., company policies, product documentation).
  • Content Discovery: An agent can help users find relevant content within a large collection of documents indexed in AgentDB.
  • Research Assistance: The agent can be used to quickly gather and synthesize information from various sources residing in AgentDB for research purposes.
  • Task Automation: Trigger actions or workflows based on the results retrieved from AgentDB, such as updating records or sending notifications.

Key capabilities

  • Semantic search within AgentDB
  • Retrieval of relevant data based on meaning
  • Integration with external knowledge sources (AgentDB)

Example prompts

  • "Find documents in AgentDB related to 'remote work policies'."
  • "What are the key features of product X, according to information stored in AgentDB?"
  • "Search AgentDB for articles discussing the impact of AI on marketing."

Tips & gotchas

  • AgentDB requires a separate account and setup. Ensure your agent has proper credentials to access it.
  • The quality of search results depends heavily on how well the data is indexed within AgentDB.