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Agentdb Vector Search

Checked
proffesor-for-testing·vlatest·testing·MIT

Helps with testing, search as part of testing, debugging, and quality assurance workflows.

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 proffesor-for-testing/agentic-qe --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

proffesor-for-testing

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 proffesor-for-testing/agentic-qe at 124d8c735e.

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

Commit
124d8c735e
Manifest
.claude/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

AgentDB Vector Search

What it does

This skill enables AI agents to perform vector-based searches within the AgentDB database, allowing for semantic retrieval of stored knowledge. It facilitates efficient querying of unstructured data by converting inputs into vector embeddings for comparison against indexed records.

When to use it

  • Retrieving specific documentation or context from a large corpus when exact keyword matching fails.
  • Finding related test cases or bug reports based on semantic similarity rather than rigid filters.
  • Querying dynamic knowledge bases where the relationship between data points is conceptual rather than hierarchical.
  • Enhancing RAG (Retrieval-Augmented Generation) pipelines with precise vector lookup capabilities.

Key capabilities

  • Semantic search execution against AgentDB collections.
  • Vector embedding generation and comparison for fuzzy matching.
  • Integration with agentic workflows requiring contextual data retrieval.
  • Support for querying unstructured text data efficiently.

Example prompts

  • "Search AgentDB for test cases related to 'user authentication failures' using semantic similarity."
  • "Retrieve the most relevant documentation snippets about API rate limiting from our vector store."
  • "Find similar bug reports based on the description of a memory leak issue in module X."

Tips & gotchas

Ensure your data is properly indexed as vectors before attempting queries, as unindexed text will not yield results. Performance may degrade with extremely large datasets if no filtering or chunking strategies are applied beforehand.