cloudflare-workers

Getting Started

1. Install the skill using the command above
2. Open your AI coding agent (Claude Code, Codex, Gemini CLI, or Cursor)
3. Reference the skill in your prompt
4. The AI will use the skill's capabilities automatically

Example Prompts

• "Review the open pull requests and summarize what needs attention"
• "Generate a changelog from the last 20 commits on the main branch"

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.