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Agent V3 Performance Engineer

Checked
ruvnet·vlatest·agents·MIT

Optimizes application performance through automated profiling, bottleneck identification, and code improvement suggestions.

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-v3-performance-engineer --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-v3-performance-engineer/SKILL.md
Scanned
1 file, 382 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-v3-performance-engineer skill analyzes and optimizes AI agent performance. It can identify bottlenecks in workflows, suggest improvements to prompt structure for efficiency, and provide recommendations for resource allocation to reduce costs and improve response times. This skill aims to enhance the overall effectiveness and cost-efficiency of deployed AI agents.

When to use it

  • Slow Response Times: When an agent is taking too long to respond to user requests or complete tasks.
  • High Resource Consumption: If your agent's infrastructure costs are unexpectedly high.
  • Workflow Bottlenecks: To pinpoint and resolve slowdowns within complex agent workflows involving multiple steps or tools.
  • Prompt Inefficiency: When prompts are overly verbose or produce inconsistent results, leading to wasted resources.

Key capabilities

  • Workflow bottleneck analysis
  • Prompt structure optimization suggestions
  • Resource allocation recommendations
  • Cost reduction strategies
  • Response time improvement techniques

Example prompts

  • "Analyze the performance of my agent workflow for processing customer support tickets and identify any bottlenecks."
  • "Suggest improvements to this prompt: '[prompt text]' to reduce token usage and improve response quality."
  • "How can I optimize resource allocation for my agent to lower infrastructure costs without sacrificing performance?"

Tips & gotchas

This skill requires access to the agent's workflow logs or detailed task execution data. Providing comprehensive information about the agent’s configuration and environment will yield more accurate and actionable recommendations.