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cloudflare-workers-expert

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

28,273 stars

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/cloudflare-workers-expert/SKILL.md --create-dirs "https://raw.githubusercontent.com/sickn33/antigravity-awesome-skills/main/plugins/antigravity-awesome-skills-claude/skills/cloudflare-workers-expert/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/cloudflare-workers-expert/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How cloudflare-workers-expert Compares

Feature / Agentcloudflare-workers-expertStandard Approach
Platform SupportmultiLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

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

Which AI agents support this skill?

This skill is compatible with multi.

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

SKILL.md Source

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 `wrangler.toml` for 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**: Workers have 0ms cold starts, but keep the bundle size small to stay within the 1MB limit for the free tier.
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

```typescript
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) {
      return new Response("Not Found", { status: 404 });
    }
    return new Response(`Stored Value: ${value}`);
  },
};
```

### Example 2: Edge Response Modification

```javascript
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, reduce the number of await calls, and move synchronous heavy lifting out of the request/response path. Use `ctx.waitUntil()` for tasks that don't block the response.