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Workers Performance

Checked
secondsky·vlatest·backend·MIT

Analyze worker task completion rates, identify bottlenecks, and suggest improvements to boost overall team productivity.

Repository

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

Installs under a different name. Its SKILL.md names it cloudflare-workers-performance, not the name it is listed under, so the command below uses that name.
  1. 1

    Run this in your project

    terminal
    $ npx skills add secondsky/claude-skills --skill cloudflare-workers-performance -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

secondsky

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

    Only contacts known or declared hosts: www.w3.org.

    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.

    Hosts contacted

    www.w3.orgstandards

  • 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 secondsky/claude-skills at 8837836131.

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

Commit
8837836131
Manifest
plugins/cloudflare-workers/skills/cloudflare-workers-performance/SKILL.md
Scanned
11 files, 4,540 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 enables AI agents to execute performance-related tasks within a Workers environment, likely optimizing runtime efficiency or managing worker lifecycle events. It provides specialized functionality for enhancing the operational speed and resource utilization of serverless functions.

When to use it

  • Optimizing cold start times for high-frequency API endpoints.
  • Managing dynamic scaling thresholds based on real-time traffic loads.
  • Tuning memory limits to reduce garbage collection overhead.
  • Implementing custom metrics to monitor worker throughput and latency.

Key capabilities

  • Runtime performance optimization features.
  • Worker lifecycle management tools.
  • Resource allocation tuning mechanisms.
  • Efficiency monitoring utilities.

Example prompts

  • "Optimize the cold start time for my image processing worker."
  • "Adjust memory limits to reduce garbage collection pauses during peak traffic."
  • "Set up custom metrics to track worker throughput and latency in real-time."

Tips & gotchas

Ensure your Workers environment has sufficient resources allocated before applying performance optimizations, as aggressive tuning may impact stability. Monitor system logs closely after changes to verify that efficiency gains do not introduce new bottlenecks.