cloudflare

name	cloudflare
description	Guide for building applications on Cloudflare's edge platform. Use when implementing serverless functions (Workers), edge databases (D1), storage (R2, KV), real-time apps (Durable Objects), AI features (Workers AI, AI Gateway), static sites (Pages), or any edge computing solutions.
license	MIT
version	1.0.0

Cloudflare Developer Platform Skill

Cloudflare Developer Platform is a comprehensive edge computing ecosystem for building full-stack applications on Cloudflare's global network. It includes serverless functions, databases, storage, AI/ML capabilities, and static site hosting.

When to Use This Skill

Use this skill when:

• Building serverless applications on the edge
• Implementing edge databases (D1 SQLite)
• Working with object storage (R2) or key-value stores (KV)
• Creating real-time applications with WebSockets (Durable Objects)
• Integrating AI/ML capabilities (Workers AI, AI Gateway, Agents)
• Deploying static sites with serverless functions (Pages)
• Building full-stack applications with frameworks (Next.js, Remix, Astro, etc.)
• Implementing message queues and background jobs (Queues)
• Optimizing for global performance and low latency

Core Concepts

Edge Computing Platform

Cloudflare's Edge Network: Code runs on servers globally distributed across 300+ cities, executing requests from the nearest location for ultra-low latency.

Key Components:

• Workers: Serverless functions on the edge
• D1: SQLite database with global read replication
• KV: Distributed key-value store with eventual consistency
• R2: Object storage with zero egress fees
• Durable Objects: Stateful compute with WebSocket support
• Queues: Message queue system for async processing
• Pages: Static site hosting with serverless functions
• Workers AI: Run AI models on the edge
• AI Gateway: Unified interface for AI providers

Execution Model

V8 Isolates: Lightweight execution environments (faster than containers) with:

• Millisecond cold starts
• Zero infrastructure management
• Automatic scaling
• Pay-per-request pricing

Handler Types:

• fetch: HTTP requests
• scheduled: Cron jobs
• queue: Message processing
• tail: Log aggregation
• email: Email handling
• alarm: Durable Object timers

API Key Configuration

Cloudflare skills require API credentials for authentication. The system searches for API keys in this order:

1. process.env - Runtime environment variables
2. <project-root>/.env - Project-level environment file
3. .claude/.env - Claude configuration directory
4. .claude/skills/.env - Skills shared configuration
5. .claude/skills/cloudflare*/.env - Skill-specific configuration

Required Environment Variables:

CLOUDFLARE_API_TOKEN=your_api_token_here
CLOUDFLARE_ACCOUNT_ID=your_account_id_here

Where to Get Credentials:

• API Token: Cloudflare Dashboard → My Profile → API Tokens → Create Token
• Account ID: Cloudflare Dashboard → Overview → Account ID (right sidebar)

Example .env File:

# See .claude/skills/.env.example for complete configuration
CLOUDFLARE_API_TOKEN=abc123...
CLOUDFLARE_ACCOUNT_ID=def456...

Getting Started with Workers

Installation

# Install Wrangler CLI
npm install -g wrangler

# Login to Cloudflare
wrangler login

# Create new project
wrangler init my-worker
cd my-worker

# Start local development
wrangler dev

# Deploy to production
wrangler deploy

Basic Worker

// src/index.ts
export default {
  async fetch(request: Request, env: Env): Promise<Response> {
    return new Response('Hello from Cloudflare Workers!');
  }
};

Configuration (wrangler.toml)

name = "my-worker"
main = "src/index.ts"
compatibility_date = "2024-01-01"

# Environment variables
[vars]
ENVIRONMENT = "production"

# Bindings (added per product below)

Language Support

• JavaScript/TypeScript: Primary language (full Node.js compatibility)
• Python: Beta support via Workers Python
• Rust: Compile to WebAssembly

Storage Products

D1 (SQLite Database)

Use Cases: Relational data, complex queries, ACID transactions

Setup:

# Create database
wrangler d1 create my-database

# Add to wrangler.toml
[[d1_databases]]
binding = "DB"
database_name = "my-database"
database_id = "YOUR_DATABASE_ID"

# Generate and apply schema
wrangler d1 execute my-database --file=./schema.sql

Usage:

export default {
  async fetch(request: Request, env: Env) {
    // Query
    const result = await env.DB.prepare(
      "SELECT * FROM users WHERE id = ?"
    ).bind(userId).first();

    // Insert
    await env.DB.prepare(
      "INSERT INTO users (name, email) VALUES (?, ?)"
    ).bind("Alice", "[e-mail]").run();

    // Batch (atomic)
    await env.DB.batch([
      env.DB.prepare("UPDATE accounts SET balance = balance - 100 WHERE id = ?").bind(user1),
      env.DB.prepare("UPDATE accounts SET balance = balance + 100 WHERE id = ?").bind(user2)
    ]);

    return new Response(JSON.stringify(result));
  }
};

Key Features:

• Global read replication (low-latency reads)
• Single-writer consistency
• Standard SQLite syntax
• 25GB database size limit

KV (Key-Value Store)

Use Cases: Cache, sessions, feature flags, rate limiting

Setup:

# Create namespace
wrangler kv:namespace create MY_KV

# Add to wrangler.toml
[[kv_namespaces]]
binding = "KV"
id = "YOUR_NAMESPACE_ID"

Usage:

export default {
  async fetch(request: Request, env: Env) {
    // Put with TTL
    await env.KV.put("session:token", JSON.stringify(data), {
      expirationTtl: 3600 // 1 hour
    });

    // Get
    const data = await env.KV.get("session:token", "json");

    // Delete
    await env.KV.delete("session:token");

    // List with prefix
    const list = await env.KV.list({ prefix: "user:123:" });

    return new Response(JSON.stringify(data));
  }
};

Key Features:

• Sub-millisecond reads (edge-cached)
• Eventual consistency (~60 seconds globally)
• 25MB value size limit
• Automatic expiration (TTL)

R2 (Object Storage)

Use Cases: File storage, media hosting, backups, static assets

Setup:

# Create bucket
wrangler r2 bucket create my-bucket

# Add to wrangler.toml
[[r2_buckets]]
binding = "R2_BUCKET"
bucket_name = "my-bucket"

Usage:

export default {
  async fetch(request: Request, env: Env) {
    // Put object
    await env.R2_BUCKET.put("path/to/file.jpg", fileBuffer, {
      httpMetadata: {
        contentType: "image/jpeg"
      }
    });

    // Get object
    const object = await env.R2_BUCKET.get("path/to/file.jpg");
    if (!object) {
      return new Response("Not found", { status: 404 });
    }

    // Stream response
    return new Response(object.body, {
      headers: {
        "Content-Type": object.httpMetadata?.contentType || "application/octet-stream"
      }
    });

    // Delete
    await env.R2_BUCKET.delete("path/to/file.jpg");

    // List
    const list = await env.R2_BUCKET.list({ prefix: "uploads/" });
  }
};

Key Features:

• S3-compatible API
• Zero egress fees (huge cost advantage)
• Unlimited storage
• 5TB object size limit
• Multipart upload support

Durable Objects

Use Cases: Real-time apps, WebSockets, coordination, stateful logic

Setup:

# wrangler.toml
[[durable_objects.bindings]]
name = "COUNTER"
class_name = "Counter"
script_name = "my-worker"

Usage:

// Define Durable Object class
export class Counter {
  state: DurableObjectState;

  constructor(state: DurableObjectState, env: Env) {
    this.state = state;
  }

  async fetch(request: Request) {
    // Get current count
    let count = (await this.state.storage.get<number>('count')) || 0;

    // Increment
    count++;
    await this.state.storage.put('count', count);

    return new Response(JSON.stringify({ count }));
  }
}

// Use in Worker
export default {
  async fetch(request: Request, env: Env) {
    // Get Durable Object instance
    const id = env.COUNTER.idFromName("global-counter");
    const counter = env.COUNTER.get(id);

    // Forward request
    return counter.fetch(request);
  }
};

WebSocket Example:

export class ChatRoom {
  state: DurableObjectState;
  sessions: Set<WebSocket>;

  constructor(state: DurableObjectState) {
    this.state = state;
    this.sessions = new Set();
  }

  async fetch(request: Request) {
    const pair = new WebSocketPair();
    const [client, server] = Object.values(pair);

    this.state.acceptWebSocket(server);
    this.sessions.add(server);

    return new Response(null, { status: 101, webSocket: client });
  }

  async webSocketMessage(ws: WebSocket, message: string) {
    // Broadcast to all connected clients
    for (const session of this.sessions) {
      session.send(message);
    }
  }

  async webSocketClose(ws: WebSocket) {
    this.sessions.delete(ws);
  }
}

Key Features:

• Single-instance coordination (strong consistency)
• Persistent storage (1GB limit on paid plans)
• WebSocket support
• Automatic hibernation for inactive objects

Queues

Use Cases: Background jobs, email sending, async processing

Setup:

# wrangler.toml
[[queues.producers]]
binding = "MY_QUEUE"
queue = "my-queue"

[[queues.consumers]]
queue = "my-queue"
max_batch_size = 10
max_batch_timeout = 30

Usage:

// Producer: Send messages
export default {
  async fetch(request: Request, env: Env) {
    await env.MY_QUEUE.send({
      type: 'email',
      to: '[e-mail]',
      subject: 'Welcome!'
    });

    return new Response('Message queued');
  }
};

// Consumer: Process messages
export default {
  async queue(batch: MessageBatch<any>, env: Env) {
    for (const message of batch.messages) {
      try {
        await processMessage(message.body);
        message.ack(); // Acknowledge success
      } catch (error) {
        message.retry(); // Retry on failure
      }
    }
  }
};

Key Features:

• At-least-once delivery
• Automatic retries (exponential backoff)
• Dead-letter queue support
• Batch processing

AI Products

Workers AI

Use Cases: Run AI models directly on the edge

Setup:

# wrangler.toml
[ai]
binding = "AI"

Usage:

export default {
  async fetch(request: Request, env: Env) {
    // Text generation
    const response = await env.AI.run('@cf/meta/llama-3-8b-instruct', {
      messages: [
        { role: 'user', content: 'What is edge computing?' }
      ]
    });

    // Image classification
    const imageResponse = await env.AI.run('@cf/microsoft/resnet-50', {
      image: imageBuffer
    });

    // Text embeddings
    const embeddings = await env.AI.run('@cf/baai/bge-base-en-v1.5', {
      text: 'Hello world'
    });

    return new Response(JSON.stringify(response));
  }
};

Available Models:

• LLMs: Llama 3, Mistral, Gemma, Qwen
• Image: Stable Diffusion, DALL-E, ResNet
• Embeddings: BGE, GTE
• Translation, summarization, sentiment analysis

AI Gateway

Use Cases: Unified interface for AI providers with caching, rate limiting, analytics

Setup:

// OpenAI via AI Gateway
const response = await fetch(
  'https://gateway.ai.cloudflare.com/v1/{account_id}/{gateway_id}/openai/chat/completions',
  {
    method: 'POST',
    headers: {
      'Authorization': `Bearer ${env.OPENAI_API_KEY}`,
      'Content-Type': 'application/json'
    },
    body: JSON.stringify({
      model: 'gpt-4',
      messages: [{ role: 'user', content: 'Hello!' }]
    })
  }
);

Features:

• Request caching (reduce costs)
• Rate limiting
• Analytics and logging
• Supports OpenAI, Anthropic, HuggingFace, etc.

Agents

Use Cases: Build AI agents with tools and workflows

import { Agent } from '@cloudflare/agents';

export default {
  async fetch(request: Request, env: Env) {
    const agent = new Agent({
      model: '@cf/meta/llama-3-8b-instruct',
      tools: [
        {
          name: 'get_weather',
          description: 'Get current weather',
          parameters: {
            type: 'object',
            properties: {
              location: { type: 'string' }
            }
          },
          handler: async ({ location }) => {
            // Fetch weather data
            return { temperature: 72, conditions: 'sunny' };
          }
        }
      ]
    });

    const result = await agent.run('What is the weather in San Francisco?');
    return new Response(JSON.stringify(result));
  }
};

AI Search (RAG)

Use Cases: Build retrieval-augmented generation applications

import { VectorizeIndex } from '@cloudflare/workers-types';

export default {
  async fetch(request: Request, env: Env) {
    // Generate embeddings
    const embeddings = await env.AI.run('@cf/baai/bge-base-en-v1.5', {
      text: query
    });

    // Search vector database
    const results = await env.VECTORIZE_INDEX.query(embeddings.data[0], {
      topK: 5
    });

    // Generate response with context
    const response = await env.AI.run('@cf/meta/llama-3-8b-instruct', {
      messages: [
        {
          role: 'system',
          content: `Context: ${results.matches.map(m => m.metadata.text).join('\n')}`
        },
        { role: 'user', content: query }
      ]
    });

    return new Response(JSON.stringify(response));
  }
};

Cloudflare Pages

Static Sites + Serverless Functions

Deployment:

# Deploy via Git (recommended)
# Connect GitHub repo in Cloudflare dashboard

# Or deploy via CLI
wrangler pages deploy ./dist

Pages Functions

Directory-based routing in functions/:

functions/
├── api/
│   ├── users/
│   │   └── [id].ts       # /api/users/:id
│   └── posts.ts          # /api/posts
└── _middleware.ts        # Global middleware

Example Function:

// functions/api/users/[id].ts
export async function onRequestGet(context) {
  const { params, env } = context;
  const user = await env.DB.prepare(
    "SELECT * FROM users WHERE id = ?"
  ).bind(params.id).first();

  return new Response(JSON.stringify(user), {
    headers: { 'Content-Type': 'application/json' }
  });
}

Middleware:

// functions/_middleware.ts
export async function onRequest(context) {
  const start = Date.now();
  const response = await context.next();
  const duration = Date.now() - start;

  console.log(`${context.request.method} ${context.request.url} - ${duration}ms`);
  return response;
}

Framework Support

Next.js:

npx create-next-app@latest my-app
cd my-app
npm install -D @cloudflare/next-on-pages
npx @cloudflare/next-on-pages
wrangler pages deploy .vercel/output/static

Remix:

npx create-remix@latest --template cloudflare/remix

Astro:

npm create astro@latest
# Select "Cloudflare" adapter during setup

SvelteKit:

npm create svelte@latest
npm install -D @sveltejs/adapter-cloudflare

Wrangler CLI Essentials

Core Commands

# Development
wrangler dev                    # Local development server
wrangler dev --remote          # Dev on real Cloudflare infrastructure

# Deployment
wrangler deploy                # Deploy to production
wrangler deploy --dry-run     # Preview changes without deploying

# Logs
wrangler tail                  # Real-time logs
wrangler tail --format pretty # Formatted logs

# Versions
wrangler deployments list      # List deployments
wrangler rollback [version]   # Rollback to previous version

# Secrets
wrangler secret put SECRET_NAME    # Add secret
wrangler secret list               # List secrets
wrangler secret delete SECRET_NAME # Delete secret

Project Management

# Create projects
wrangler init my-worker        # Create Worker
wrangler pages project create  # Create Pages project

# Database
wrangler d1 create my-db           # Create D1 database
wrangler d1 execute my-db --file=schema.sql
wrangler d1 execute my-db --command="SELECT * FROM users"

# KV
wrangler kv:namespace create MY_KV
wrangler kv:key put --binding=MY_KV "key" "value"
wrangler kv:key get --binding=MY_KV "key"

# R2
wrangler r2 bucket create my-bucket
wrangler r2 object put my-bucket/file.txt --file=./file.txt

Integration Patterns

Full-Stack Application Architecture

┌─────────────────────────────────────────┐
│         Cloudflare Pages (Frontend)      │
│    Next.js / Remix / Astro / SvelteKit  │
└──────────────────┬──────────────────────┘
                   │
┌──────────────────▼──────────────────────┐
│      Workers (API Layer / BFF)          │
│    - Routing                             │
│    - Authentication                      │
│    - Business logic                      │
└─┬──────┬──────┬──────┬──────┬───────────┘
  │      │      │      │      │
  ▼      ▼      ▼      ▼      ▼
┌────┐ ┌────┐ ┌────┐ ┌────┐ ┌────────────┐
│ D1 │ │ KV │ │ R2 │ │ DO │ │ Workers AI │
└────┘ └────┘ └────┘ └────┘ └────────────┘

Polyglot Storage Pattern

export default {
  async fetch(request: Request, env: Env) {
    const url = new URL(request.url);

    // KV: Fast cache
    const cached = await env.KV.get(url.pathname);
    if (cached) return new Response(cached);

    // D1: Structured data
    const user = await env.DB.prepare(
      "SELECT * FROM users WHERE id = ?"
    ).bind(userId).first();

    // R2: Media files
    const avatar = await env.R2_BUCKET.get(`avatars/${user.id}.jpg`);

    // Durable Objects: Real-time coordination
    const chat = env.CHAT_ROOM.get(env.CHAT_ROOM.idFromName(roomId));

    // Queue: Async processing
    await env.EMAIL_QUEUE.send({ to: user.email, template: 'welcome' });

    return new Response(JSON.stringify({ user, avatar }));
  }
};

Authentication Pattern

import { verifyJWT, createJWT } from './jwt';

export default {
  async fetch(request: Request, env: Env) {
    const url = new URL(request.url);

    // Login
    if (url.pathname === '/api/login') {
      const { email, password } = await request.json();

      const user = await env.DB.prepare(
        "SELECT * FROM users WHERE email = ?"
      ).bind(email).first();

      if (!user || !await verifyPassword(password, user.password_hash)) {
        return new Response('Invalid credentials', { status: 401 });
      }

      const token = await createJWT({ userId: user.id }, env.JWT_SECRET);

      return new Response(JSON.stringify({ token }), {
        headers: { 'Content-Type': 'application/json' }
      });
    }

    // Protected route
    const authHeader = request.headers.get('Authorization');
    if (!authHeader) {
      return new Response('Unauthorized', { status: 401 });
    }

    const token = authHeader.replace('Bearer ', '');
    const payload = await verifyJWT(token, env.JWT_SECRET);

    // Store session in KV
    await env.KV.put(`session:${payload.userId}`, JSON.stringify(payload), {
      expirationTtl: 86400 // 24 hours
    });

    return new Response('Authenticated');
  }
};

Cache Strategy

export default {
  async fetch(request: Request, env: Env) {
    const cache = caches.default;
    const cacheKey = new Request(request.url);

    // Check cache
    let response = await cache.match(cacheKey);
    if (response) return response;

    // Check KV (distributed cache)
    const kvCached = await env.KV.get(request.url);
    if (kvCached) {
      response = new Response(kvCached);
      await cache.put(cacheKey, response.clone());
      return response;
    }

    // Fetch from origin (D1, R2, etc.)
    const data = await fetchFromOrigin(request, env);
    response = new Response(data);

    // Store in both caches
    await cache.put(cacheKey, response.clone());
    await env.KV.put(request.url, data, { expirationTtl: 3600 });

    return response;
  }
};

Best Practices

Performance

1. Minimize Cold Starts: Keep Workers lightweight (<1MB bundled)
2. Use Bindings Over Fetch: Direct bindings are faster than HTTP calls
3. Edge Caching: Leverage KV and Cache API for frequently accessed data
4. Batch Operations: Use D1 batch for multiple queries
5. Stream Large Responses: Use Response.body streams for large files

Security

1. Secrets Management: Use wrangler secret for API keys
2. Environment Isolation: Separate production/staging/development
3. Input Validation: Sanitize user input
4. Rate Limiting: Use KV or Durable Objects for rate limiting
5. CORS: Configure proper CORS headers

Cost Optimization

1. R2 for Large Files: Zero egress fees vs S3
2. KV for Caching: Reduce D1/R2 requests
3. Request Deduplication: Cache identical requests
4. Efficient Queries: Index D1 tables properly
5. Monitor Usage: Use Cloudflare Analytics

Development Workflow

1. Local Development: Use wrangler dev for testing
2. Type Safety: Use TypeScript with @cloudflare/workers-types
3. Testing: Use Vitest with unstable_dev()
4. CI/CD: GitHub Actions with cloudflare/wrangler-action
5. Gradual Deployments: Use percentage-based rollouts

Common Patterns

API Gateway

import { Hono } from 'hono';

const app = new Hono();

app.get('/api/users/:id', async (c) => {
  const user = await c.env.DB.prepare(
    "SELECT * FROM users WHERE id = ?"
  ).bind(c.req.param('id')).first();

  return c.json(user);
});

app.post('/api/users', async (c) => {
  const { name, email } = await c.req.json();

  await c.env.DB.prepare(
    "INSERT INTO users (name, email) VALUES (?, ?)"
  ).bind(name, email).run();

  return c.json({ success: true }, 201);
});

export default app;

Image Transformation

export default {
  async fetch(request: Request, env: Env) {
    const url = new URL(request.url);
    const imageKey = url.pathname.replace('/images/', '');

    // Get from R2
    const object = await env.R2_BUCKET.get(imageKey);
    if (!object) {
      return new Response('Not found', { status: 404 });
    }

    // Transform with Cloudflare Images
    return new Response(object.body, {
      headers: {
        'Content-Type': object.httpMetadata?.contentType || 'image/jpeg',
        'Cache-Control': 'public, max-age=86400',
        'cf-image-resize': JSON.stringify({
          width: 800,
          height: 600,
          fit: 'cover'
        })
      }
    });
  }
};

Rate Limiting (KV)

async function rateLimit(ip: string, env: Env): Promise<boolean> {
  const key = `ratelimit:${ip}`;
  const limit = 100; // requests per minute
  const window = 60; // seconds

  const current = await env.KV.get(key);
  const count = current ? parseInt(current) : 0;

  if (count >= limit) {
    return false; // Rate limit exceeded
  }

  await env.KV.put(key, (count + 1).toString(), {
    expirationTtl: window
  });

  return true;
}

export default {
  async fetch(request: Request, env: Env) {
    const ip = request.headers.get('CF-Connecting-IP') || 'unknown';

    if (!await rateLimit(ip, env)) {
      return new Response('Rate limit exceeded', { status: 429 });
    }

    return new Response('OK');
  }
};

Scheduled Jobs

# wrangler.toml
[triggers]
crons = ["0 0 * * *"] # Daily at midnight

export default {
  async scheduled(event: ScheduledEvent, env: Env) {
    // Cleanup old sessions
    const sessions = await env.KV.list({ prefix: 'session:' });
    for (const key of sessions.keys) {
      const session = await env.KV.get(key.name, 'json');
      if (session.expiresAt < Date.now()) {
        await env.KV.delete(key.name);
      }
    }
  }
};

Troubleshooting

Common Issues

"Module not found" errors

• Ensure dependencies are in package.json
• Run npm install before deploying
• Check compatibility_date in wrangler.toml

Database connection errors (D1)

• Verify database_id in wrangler.toml
• Check database exists: wrangler d1 list
• Run migrations: wrangler d1 execute DB --file=schema.sql

KV not found errors

• Create namespace: wrangler kv:namespace create MY_KV
• Add binding to wrangler.toml
• Deploy after configuration changes

Cold start timeout

• Reduce bundle size (<1MB ideal)
• Remove unnecessary dependencies
• Use dynamic imports for large libraries

CORS errors

• Add CORS headers to responses:

  return new Response(data, {
    headers: {
      'Access-Control-Allow-Origin': '*',
      'Access-Control-Allow-Methods': 'GET, POST, OPTIONS',
      'Access-Control-Allow-Headers': 'Content-Type'
    }
  });
  

Deployment fails

• Check wrangler version: wrangler --version
• Verify authentication: wrangler whoami
• Review build errors in console output

Debugging

# Real-time logs
wrangler tail

# Local debugging with breakpoints
wrangler dev --local

# Remote debugging
wrangler dev --remote

# Check deployment status
wrangler deployments list

Decision Matrix

Need	Choose
Sub-millisecond reads	KV
SQL queries	D1
Large files (>25MB)	R2
Real-time WebSockets	Durable Objects
Async background jobs	Queues
ACID transactions	D1
Strong consistency	Durable Objects
Zero egress costs	R2
AI inference	Workers AI
Static site hosting	Pages
Serverless functions	Workers
Multi-provider AI	AI Gateway

Framework-Specific Guides

Next.js

• Use @cloudflare/next-on-pages adapter
• Configure next.config.js for edge runtime
• Deploy via wrangler pages deploy

Remix

• Use official Cloudflare template
• Configure server.ts for Workers
• Access bindings via context.cloudflare.env

Astro

• Use @astrojs/cloudflare adapter
• Enable SSR in astro.config.mjs
• Access env via Astro.locals.runtime.env

SvelteKit

• Use @sveltejs/adapter-cloudflare
• Configure in svelte.config.js
• Access platform via event.platform.env

Resources

• Documentation: https://developers.cloudflare.com
• Wrangler CLI: https://developers.cloudflare.com/workers/wrangler/
• Discord Community: https://discord.cloudflare.com
• Examples: https://developers.cloudflare.com/workers/examples/
• GitHub: https://github.com/cloudflare
• Status Page: https://www.cloudflarestatus.com

Implementation Checklist

Workers Setup

• Install Wrangler CLI (npm install -g wrangler)
• Login to Cloudflare (wrangler login)
• Create project (wrangler init)
• Configure wrangler.toml
• Add environment variables/secrets
• Test locally (wrangler dev)
• Deploy (wrangler deploy)

Storage Setup (as needed)

• Create D1 database and apply schema
• Create KV namespace
• Create R2 bucket
• Configure Durable Objects
• Set up Queues
• Add bindings to wrangler.toml

Pages Setup

• Connect Git repository or use CLI
• Configure build settings
• Set environment variables
• Add Pages Functions (if needed)
• Deploy and test

Production Checklist

• Set up custom domain
• Configure DNS records
• Enable SSL/TLS
• Set up monitoring/analytics
• Configure rate limiting
• Implement error handling
• Set up CI/CD pipeline
• Test gradual deployments
• Document rollback procedure
• Configure logging/observability