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Performance Testing Review Multi Agent Review

Listed
sickn33·vlatest·agents·MIT

Helps with performance optimization, testing, code review as part of orchestrating AI agents and multi-step workflows workflows.

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 sickn33/agentic-awesome-skills --skill performance-testing-review-multi-agent-review -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

sickn33

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 failed 1 of 6 checks. A failure is not proof of bad intent; it means something in the code needs a human to look at it before you run it.

  • Declares what it does: failed

    No SKILL.md was found in the skill's directory.

    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 sickn33/antigravity-awesome-skills at 5a4cdd9df8.

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

Commit
5a4cdd9df8
Manifest
skills/performance-testing-review-multi-agent-review/SKILL.md
Scanned
0 files
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 orchestrates a multi-agent workflow to conduct comprehensive performance testing reviews, leveraging specialized agents to analyze system metrics and generate actionable insights. It automates the evaluation of application stability and speed under load by coordinating distinct AI roles for data interpretation and reporting.

When to use it

  • You need an automated audit of your API's latency and throughput before a major deployment.
  • Your team requires diverse perspectives on performance bottlenecks from multiple specialized agents simultaneously.
  • You want to reduce manual analysis time when reviewing complex load testing results.
  • You are preparing a technical report that demands structured, multi-faceted validation of system health.

Key capabilities

  • Coordinates multiple AI agents to perform simultaneous analysis of performance data.
  • Generates detailed review reports highlighting specific bottlenecks and optimization opportunities.
  • Evaluates system stability and response times under simulated or real load conditions.
  • Synthesizes findings from different agent viewpoints into a unified conclusion.

Example prompts

  • "Run a full multi-agent review on our latest load test results for the checkout service."
  • "Analyze these latency spikes using the performance testing review workflow and suggest fixes."
  • "Generate a performance health report by deploying the multi-agent review team to our staging environment logs."

Tips & gotchas

Ensure your input data includes clear metrics like response times, error rates, and resource utilization for accurate agent analysis. This skill is best suited for post-test evaluation rather than real-time monitoring during active load generation.