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Agentdb Vector Search

Checked
ruvnet·vlatest·database·MIT

Quickly retrieve relevant information from your database using semantic vector search powered by ruvnet's agentdb.

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 ruvnet/ruflo --skill "AgentDB Vector Search" --full-depth -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

ruvnet

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 ruvnet/claude-flow at 49da6ecc44.

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

Commit
49da6ecc44
Manifest
.agents/skills/agentdb-vector-search/SKILL.md
Scanned
3 files, 900 lines
Copies in repo
3 — all of them scanned
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 quickly retrieve relevant information from a database using semantic vector search powered by ruvnet's AgentDB. It leverages high-performance vector database operations, boasting speeds up to 150x-12,500x faster than traditional methods. The system utilizes techniques like HNSW indexing, quantization, and delivers sub-millisecond search times (less than 100µs) for efficient information retrieval.

When to use it

  • Semantic Search: When you need to find data based on the meaning of a query rather than just keyword matches.
  • Large Datasets: Ideal for searching through large collections of documents or other data where traditional search methods are slow.
  • Rapid Prototyping: Quickly test and iterate on vector database configurations with in-memory options.
  • Knowledge Base Retrieval: Efficiently retrieve information from a knowledge base to answer user questions or inform decision-making.

Key capabilities

  • Vector-based Semantic Search: Finds data based on meaning, not just keywords.
  • High Performance: Offers significantly faster search speeds compared to traditional methods.
  • HNSW Indexing & Quantization: Optimizes for speed and memory efficiency.
  • Multiple Distance Metrics: Supports cosine similarity, Euclidean distance (L2), and dot product calculations.
  • Preset Configurations: Includes options for small (<10K vectors), medium (10K-100K vectors), and large (>100K vectors) databases.

Example prompts

  • "Find documents related to 'quantum computing breakthroughs'."
  • "Retrieve the top 5 most similar entries to [0.1, 0.2, 0.3, ...]."
  • "Search for information with a cosine similarity threshold of 0.75."

Tips & gotchas

  • Prerequisites: Requires Node.js 18+, AgentDB v1.0.7 or later (via agentic-flow or standalone), and an OpenAI API key (or custom embedding model) to generate embeddings.
  • Embedding Dimensions: Ensure the --dimension parameter during initialization matches your chosen embedding model's output size (e.g., 768 for sentence-transformers).
  • Quantization: Experiment with different quantization types to balance memory usage and search performance.