# Cloudflare Workers Expert

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

- Skill: `techwavedev/cloudflare-workers-expert` (Agent Skill)
- Install (CLI): `npx skillmds@latest add techwavedev/cloudflare-workers-expert`
- Raw SKILL.md: https://api.skillmd.com/api/skills/techwavedev/cloudflare-workers-expert/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: techwavedev (https://skillmd.com/u/techwavedev)
- Updated: 2026-09-10
- Page: https://skillmd.com/skills/techwavedev/cloudflare-workers-expert

---


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.

---

<!-- AGI-INTEGRATION-START -->

## AGI Framework Integration

> **Adapted for [@techwavedev/agi-agent-kit](https://www.npmjs.com/package/@techwavedev/agi-agent-kit)**
> Original source: [antigravity-awesome-skills](https://github.com/sickn33/antigravity-awesome-skills)

### Memory-First Protocol

Retrieve prior decisions and patterns to avoid re-discovering solutions. Cache results for instant retrieval in future sessions.

```bash
# Check for prior development context before starting
python3 execution/memory_manager.py auto --query "prior work and patterns related to Cloudflare Workers Expert"
```

### Storing Results

After completing work, store development decisions for future sessions:

```bash
python3 execution/memory_manager.py store \
  --content "Completed task with key insights documented for future reference" \
  --type decision --project <project> \
  --tags cloudflare-workers-expert default
```

### Multi-Agent Collaboration

Share outcomes with other agents so the team stays aligned and avoids duplicate work.

```bash
python3 execution/cross_agent_context.py store \
  --agent "<your-agent>" \
  --action "Task completed — results documented and shared with team" \
  --project <project>
```

<!-- AGI-INTEGRATION-END -->