cloudflare-workers
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.
What this skill does
# 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.
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