How does this skill help reduce cold starts?

It monitors your bundle size and flags heavy dependencies like Moment.js or Axios, suggesting lightweight native alternatives that keep worker initialization fast.

How does it handle CPU execution limits?

It identifies CPU-intensive loops or synchronous processing that might exceed the 50ms limit and suggests bounded operations or offloading patterns.

Does it support Cloudflare's specific caching mechanisms?

Absolutely. The skill detects missing caching opportunities for frequently accessed data and suggests implementation patterns for the Cloudflare Cache API.

Can it optimize slow storage operations?

Yes, it identifies sequential KV or D1 operations and provides code snippets to parallelize them using Promise.all(), significantly reducing round-trip latency.

Edge Performance Optimizer

Name: Edge Performance Optimizer
Author: hirefrank

byhirefrank

•

Cloud Infrastructure

Optimizes Cloudflare Workers performance by reducing cold starts, minimizing bundle sizes, and implementing global edge caching patterns.

The Edge Performance Optimizer is a specialized Claude Code skill designed to audit and enhance Cloudflare Workers code for maximum efficiency. It automatically identifies performance bottlenecks such as heavy dependencies (e.g., Axios or Lodash), sequential storage operations, and missing caching headers, providing immediate remediation strategies to reduce global latency and minimize CPU time. Whether you are troubleshooting high cold start times or optimizing KV/R2/D1 access patterns, this skill ensures your edge applications meet production-grade performance standards.

Key Features

01Detects sequential storage or network operations and suggests parallelization patterns

02Monitors CPU-intensive operations to prevent execution limit overages

032 GitHub stars

04Provides real-time validation of performance targets for cold starts and global latency

05Automates the implementation of edge caching via the Cloudflare Cache API

06Identifies and replaces heavy dependencies with native Web APIs to reduce bundle size

Use Cases

01Reducing cold start times for mission-critical serverless applications

02Optimizing global response times through advanced edge caching strategies

03Refactoring legacy worker code to use efficient native Web APIs instead of heavy libraries

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add hirefrank/hirefrank-marketplace edge-performance-optimizer

For use in Claude.ai and ChatGPT

Download Skill