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APIs & servers (backend)48k · #2554

cloudflare-workers-expert

Empowers your agent to work with Cloudflare Workers and edge computing. It understands tools like Wrangler, KV, D1, Durable Objects, and R2.

What it does

This expert helps you build and optimize programs that run on the internet's 'edge' (Cloudflare Workers). It understands how to make these programs fast, store data, and interact with other Cloudflare services.

When to use it

  • ✓When building programs that need to be very fast for users anywhere in the world.
  • ✓When you want to store information directly on Cloudflare's network.
  • ✓To modify how requests arrive and leave your websites before reaching the main server.

Example of what to ask your AI

“Create a Worker that saves a webpage's title to a Cloudflare KV store.”

Want more? Open the sections below.

How to use

The easiest way: copy the message below and paste it into Claude, ChatGPT, Cursor or any AI. It reads the skill and follows the instructions.

Skill link

https://github.com/sickn33/agentic-awesome-skills/blob/main/plugins/agentic-awesome-skills-claude/skills/cloudflare-workers-expert/SKILL.md

Ready-to-paste message

Use this skill to help me: https://github.com/sickn33/agentic-awesome-skills/blob/main/plugins/agentic-awesome-skills-claude/skills/cloudflare-workers-expert/SKILL.md Read the SKILL.md file at that link and follow its instructions for my task.
How it works

It uses tools like Wrangler to set up and test your Workers. Workers use the standard web Fetch API and connect to services like KV, D1, and R2 through 'bindings' configured in the wrangler.toml file.

Installation

Easiest way: copy the skill link and paste it into Claude, ChatGPT or Cursor asking it to follow the instructions.

In the terminal (Claude Code, Codex, Cursor), in your project folder:

npx skills add sickn33/agentic-awesome-skills --skill cloudflare-workers-expert

Or download just the file:

curl -L https://raw.githubusercontent.com/sickn33/agentic-awesome-skills/main/plugins/agentic-awesome-skills-claude/skills/cloudflare-workers-expert/SKILL.md -o SKILL.md
Technical content (original SKILL.md)

Original text by the author, straight from GitHub.

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.
FAQ

What are Cloudflare Workers?

They are small programs that run on Cloudflare's global network, close to users, to be faster.

What is 'Edge Computing'?

It's the idea of processing data and running programs closer to where the data is generated or where users are, rather than in a central data center.

What are KV, D1, and R2?

They are Cloudflare services for storing data quickly and in a distributed way, used by Workers.

Details

Original description by the author

Expert in Cloudflare Workers and the Edge Computing ecosystem. Covers Wrangler, KV, D1, Durable Objects, and R2 storage.

repo: sickn33/agentic-awesome-skills

Popularity

GitHub stars
48k
Ranking position
#2554
License
MIT
Updated
10/15/2026

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