Skip to content
TrustedSkillsbeta
Back to all skills

Agentdb Performance Optimization

Checked
ruvnet·vlatest·testing·MIT

Helps with performance optimization, optimization as part of building frontend UIs and user experiences workflows.

Repository

Install on your platform

This skill no longer installs under the name agentdb-performance-optimization. ruvnet/ruflo has no skill named "agentdb-performance-optimization" any more. It was renamed, moved or deleted upstream.

We check each listing against its upstream repository with the rules the skills CLI uses, last on 2026-10-22. We don't show a command we know will fail.

Repository install instructions

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-optimization/SKILL.md
Scanned
3 files, 1,362 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 provides comprehensive performance optimization techniques for AgentDB vector databases. It enables AI agents to significantly improve query speeds (up to 12,500x faster), reduce memory usage (by 4-32x), and enhance overall database efficiency through methods like quantization, HNSW indexing, caching strategies, and batch operations. This allows for more efficient storage and retrieval of vector data within AgentDB applications.

When to use it

  • Mobile or edge deployments: Optimize AgentDB for resource-constrained environments.
  • Large-scale vector storage: Improve performance when working with millions of vectors.
  • Real-time search: Accelerate search speeds in scenarios requiring near-instantaneous results.
  • Production applications needing high accuracy: Balance optimization gains with maintaining a high level of data precision.

Key capabilities

  • Quantization: Reduces memory usage and improves speed using binary, scalar, or product quantization techniques (offering 4x to 32x memory reduction).
  • HNSW Indexing: (Implied - used in conjunction with optimizations) Improves search efficiency.
  • Caching Strategies: Enables faster data retrieval through in-memory caching.
  • Batch Operations: Optimizes insert operations for improved performance when adding multiple vectors at once.
  • Performance Benchmarking: Provides tools to measure and track optimization improvements.

Example prompts

  • "Run a benchmark on my AgentDB database to assess current performance."
  • "Optimize my AgentDB database using binary quantization to reduce memory usage."
  • "Configure AgentDB with scalar quantization for balanced performance and accuracy."

Tips & gotchas

  • Prerequisites: Requires Node.js 18+ and AgentDB v1.0.7 or later, installed via agentic-flow.
  • Accuracy Trade-offs: Quantization methods can result in a slight decrease in data accuracy (typically between 2-7%). Choose the quantization type based on your specific needs for memory reduction vs. accuracy.
  • Memory Reduction: Binary quantization offers the greatest memory reduction (up to 32x), while scalar quantization provides a more balanced approach.