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Agent Performance Optimizer

Checked
ruvnet·vlatest·agents·MIT

Ruvnet's agent-performance-optimizer dynamically adjusts agent parameters to maximize efficiency and task completion rates.

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 ruvnet/ruflo --skill agent-performance-optimizer --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/agent-performance-optimizer/SKILL.md
Scanned
1 file, 363 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

The Agent Performance Optimizer skill analyzes agent interactions and suggests improvements to enhance efficiency and output quality. It identifies areas where an agent might be struggling, such as repetitive tasks or inefficient prompt structures. The goal is to refine agent behavior through targeted adjustments based on observed performance data.

When to use it

  • Debugging slow agents: If an agent takes too long to complete tasks, this skill can help pinpoint bottlenecks and suggest optimizations.
  • Improving output quality: Identify patterns in low-quality outputs and receive recommendations for prompt refinement or task decomposition.
  • Reducing repetitive actions: Discover redundant steps within an agent's workflow and streamline processes for increased efficiency.
  • Optimizing complex workflows: When dealing with multi-step agents, this skill can help analyze each stage to ensure optimal performance.

Key capabilities

  • Interaction analysis
  • Performance bottleneck identification
  • Prompt refinement suggestions
  • Workflow optimization recommendations

Example prompts

  • "Analyze the last 10 interactions of my agent and suggest improvements."
  • "Identify any repetitive actions in this agent's workflow."
  • "How can I improve the quality of responses from my agent when it’s summarizing documents?"

Tips & gotchas

This skill requires access to the agent's interaction history for analysis. Results are most effective with agents performing complex or multi-step tasks, as simpler agents may not reveal significant optimization opportunities.