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Performance Load Testing

Checked
kentoshimizu·vlatest·testing·Apache-2.0

Helps with performance optimization, testing as part of testing, debugging, and quality assurance 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 kentoshimizu/sw-agent-skills --skill performance-load-testing -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

kentoshimizu

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 kentoshimizu/sw-agent-skills at 5b7bafb626.

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

Commit
5b7bafb626
Manifest
skills/performance-load-testing/SKILL.md
Scanned
4 files, 91 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

This skill allows AI agents to simulate user load on web applications and APIs. It can generate realistic traffic patterns, measure response times under stress, and identify performance bottlenecks. The agent can then report these findings in a structured format for analysis and optimization.

When to use it

  • Pre-release testing: Ensure a new feature or application update can handle expected user load before launch.
  • Capacity planning: Determine the infrastructure needed to support anticipated growth in users.
  • Performance troubleshooting: Identify the root cause of slow response times or system instability under load.
  • API endpoint validation: Verify API endpoints meet performance SLAs (Service Level Agreements).

Key capabilities

  • Simulates concurrent user requests
  • Measures response latency and throughput
  • Generates configurable traffic patterns
  • Reports results in a structured format

Example prompts

  • "Run a load test on https://example.com with 100 simulated users for 60 seconds."
  • "Simulate 50 concurrent users accessing the /api/products endpoint and report average response time."
  • “Create a ramp-up load test, starting with 10 users and increasing to 200 over 5 minutes on https://myapp.com. ”

Tips & gotchas

The accuracy of the results depends heavily on realistic configuration of simulated user behavior; consider providing details about typical user actions when prompting the agent. Ensure sufficient computational resources are available for both the AI agent and the target system being tested.