---

name: cloudflare-workers
description: >-
Assists with building and deploying applications on Cloudflare Workers edge computing platform.
Use when working with Workers runtime, Wrangler CLI, KV, D1, R2, Durable Objects, Queues,
or Hyperdrive. Trigger words: cloudflare, workers, edge functions, wrangler, KV, D1, R2,
durable objects, edge computing.
license: Apache-2.0
compatibility: "Requires Wrangler CLI and a Cloudflare account"
metadata:
author: terminal-skills
version: "1.0.0"
category: development
tags: ["cloudflare", "edge-computing", "serverless", "workers", "wrangler"]

Cloudflare Workers

Overview

Cloudflare Workers enables building and deploying applications at the edge with sub-millisecond cold starts. The platform leverages the Workers runtime alongside storage services like KV, D1, R2, Durable Objects, and Queues to build globally distributed, low-latency applications.

Instructions

• When asked to create a Worker, scaffold with wrangler init using ES Module syntax (export default { fetch }) and set compatibility_date in wrangler.toml.
• When configuring storage, recommend KV for read-heavy key-value caching, D1 for relational data with SQL, R2 for S3-compatible object storage with zero egress fees, and Durable Objects for strongly consistent state coordination.
• When setting up local development, use wrangler dev with hot reload and local KV/D1/R2 simulation.
• When deploying, use wrangler deploy and configure routes, bindings, and build settings in wrangler.toml.
• When managing secrets, use wrangler secret put KEY_NAME and type bindings with an Env interface.
• When optimizing performance, leverage the Cache API (caches.default), Smart Placement, streaming responses with TransformStream, and HTMLRewriter for HTML transformation.
• When handling background work, use ctx.waitUntil() for fire-and-forget async tasks like analytics or logging.
• When building AI features, use Workers AI for edge inference, AI Gateway for multi-provider management, and Vectorize for RAG pipelines.

Examples

Example 1: Create an edge API with KV caching

User request: "Set up a Cloudflare Worker that serves cached API responses from KV"

Actions:

1. Scaffold a new Worker project with wrangler init
2. Configure KV namespace binding in wrangler.toml
3. Implement fetch handler with KV read/write and cache-control headers
4. Test locally with wrangler dev

Output: A Worker that checks KV for cached data, falls back to origin, and stores results in KV with TTL.

Example 2: Deploy a scheduled data sync Worker

User request: "Build a Worker that runs on a schedule to sync data from an external API into D1"

Actions:

1. Configure Cron Trigger in wrangler.toml
2. Create D1 database and migration with schema
3. Implement scheduled() handler that fetches external data and inserts into D1
4. Use ctx.waitUntil() for non-blocking cleanup tasks

Output: A Worker with cron-triggered data synchronization and D1 storage.

Guidelines

• Always set compatibility_date in wrangler.toml to pin runtime behavior.
• Use ES Module syntax (export default) over Service Worker syntax.
• Type all environment bindings with an Env interface for type safety.
• Handle errors gracefully with proper HTTP status codes instead of unhandled exceptions.
• Use ctx.waitUntil() for fire-and-forget async work that should not block the response.
• Prefer D1 over KV for relational data; use KV for simple key-value caching.
• Set appropriate Cache-Control headers and leverage Cloudflare's edge cache.