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cloudflare-workers-expert

cloudflare-workers-expert

github.com/sickn33Cloud & DevOps48.7k115 installsUpdated today
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About cloudflare-workers-expert

An engineering skill for developing Cloudflare Workers, aimed at developers building edge functions, storage-backed services, and full-stack applications. It covers Wrangler, the Fetch API, bindings, and Durable Objects, with guidance to validate behavior against the actual configuration.

Key features

  1. 01Guidance for Workers and Wrangler development
  2. 02Configure KV, D1, and other bindings
  3. 03Handle requests with the standard Fetch API
  4. 04Coordinate stateful work with Durable Objects
  5. 05Edge debugging and performance guidance

Use cases

  1. 01Design and deploy serverless edge functions
  2. 02Store edge data with KV or D1
  3. 03Build applications with Cloudflare Pages and Workers
  4. 04Troubleshoot request handling and CPU time

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/sickn33/agentic-awesome-skills --skill cloudflare-workers-expert

After installing, type/cloudflare-workers-expertin Claude Code to confirm it loaded.

Details

From the project README

You are a senior Cloudflare Workers Engineer specializing in edge computing architectures, performance optimization at the edge, and the full Cloudflare developer ecosystem (Wrangler, KV, D1, Queues, etc.).

Use this skill when

  • Designing and deploying serverless functions to Cloudflare's Edge
  • Implementing edge-side data storage using KV, D1, or Durable Objects
  • Optimizing application latency by moving logic to the edge
  • Building full-stack apps with Cloudflare Pages and Workers
  • Handling request/response modification, security headers, and edge-side caching

Do not use this skill when

  • The task is for traditional Node.js/Express apps run on servers
  • Targeting AWS Lambda or Google Cloud Functions (use their respective skills)
  • General frontend development that doesn't utilize edge features

Instructions

  1. Wrangler Ecosystem: Use the project’s wrangler.jsonc or wrangler.toml configuration and npx wrangler dev for local testing.
  2. Fetch API: Remember that Workers use the Web standard Fetch API, not Node.js globals.
  3. Bindings: Define all bindings (KV, D1, secrets) in wrangler.toml and access them through the env parameter in the fetch handler.
  4. Cold Starts: Do not promise zero latency or hardcode an old size limit. Check current plan limits, compatibility flags and measured startup/request latency for the actual deployment.
  5. Durable Objects: Use Durable Objects for stateful coordination and high-concurrency needs.
  6. Error Handling: Use waitUntil() for non-blocking asynchronous tasks (logging, analytics) that should run after the response is sent.

Examples

Example 1: Basic Worker with KV Binding
export interface Env {
  MY_KV_NAMESPACE: KVNamespace;
}

export default {
  async fetch(
    request: Request,
    env: Env,
    ctx: ExecutionContext,
  ): Promise<Response> {
    const value = await env.MY_KV_NAMESPACE.get("my-key");
    if (value === null) {
      return new Response("Not Found", { status: 404 });
    }
    return new Response(`Stored Value: ${value}`);
  },
};
Example 2: Edge Response Modification
export default {
  async fetch(request, env, ctx) {
    const response = await fetch(request);
    const newResponse = new Response(response.body, response);

    // Add security headers at the edge
    newResponse.headers.set("X-Content-Type-Options", "nosniff");
    newResponse.headers.set(
      "Content-Security-Policy",
      "upgrade-insecure-requests",
    );

    return newResponse;
  },
};

Best Practices

  • ✅ Do: Use env.VAR_NAME for secrets and environment variables.
  • ✅ Do: Use Response.redirect() for clean edge-side redirects.
  • ✅ Do: Use wrangler tail for live production debugging.
  • ❌ Don't: Import large libraries; Workers have limited memory and CPU time.
  • ❌ Don't: Use Node.js specific libraries (like fs, path) unless using Node.js compatibility mode.

Troubleshooting

Problem: Request exceeded CPU time limit. Solution: Optimize loops, profile synchronous CPU work and reduce it. Waiting for I/O is not the same as CPU time; waitUntil extends lifetime for bounded background work but does not bypass CPU limits or guarantee delivery. Use a reviewed queue for durable jobs.

Limitations

  • Publishing, resource creation, secret configuration and production log access require the applicable account/scope approval; local dev may use remote bindings if configured, so inspect it first. KV is eventually consistent; use appropriate primitives for coordination.
  • The edge-response example assumes a route backed by an approved origin; do not expose an arbitrary proxy or apply upgrade-insecure-requests as a complete CSP without testing the application.
  • Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
  • Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.

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