What does the Cloudflare AI skill do?

Build and deploy on Cloudflare's edge platform. Use when creating Workers, Pages, D1 databases, R2 storage, AI inference, or KV storage. Triggers on Cloudflare, Workers, Cloudflare Pages, D1, R2, KV, Cloudflare AI, Durable Objects, edge computing.

Why use Cloudflare on TypingMind?

Because you install it once and use it with any model. Cloudflare is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Cloudflare in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/hoodini/ai-agents-skills/tree/master/skills/cloudflare. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Cloudflare?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Cloudflare?

As many as you like. As long as a model supports skills, you can use Cloudflare with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Cloudflare AI skill free?

It is published on GitHub by hoodini. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

Cloudflare by hoodini — AI Skill for Any LLM

Cloudflare Platform

Build globally distributed applications on Cloudflare's edge network.

Quick Start

bash
# Install Wrangler CLI
npm install -g wrangler

# Login
wrangler login

# Create new Worker
wrangler init my-worker

# Deploy
wrangler deploy

Workers

Basic Worker

typescript
// src/index.ts
export default {
  async fetch(request: Request, env: Env, ctx: ExecutionContext): Promise<Response> {
    const url = new URL(request.url);
    
    if (url.pathname === '/api/hello') {
      return Response.json({ message: 'Hello from the edge!' });
    }
    
    return new Response('Not Found', { status: 404 });
  },
};

wrangler.toml Configuration

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

[vars]
ENVIRONMENT = "production"

# KV Namespace
[[kv_namespaces]]
binding = "MY_KV"
id = "abc123"

# D1 Database
[[d1_databases]]
binding = "DB"
database_name = "my-database"
database_id = "def456"

# R2 Bucket
[[r2_buckets]]
binding = "BUCKET"
bucket_name = "my-bucket"

# AI
[ai]
binding = "AI"

# Durable Objects
[[durable_objects.bindings]]
name = "COUNTER"
class_name = "Counter"

[[migrations]]
tag = "v1"
new_classes = ["Counter"]

Request Routing

typescript
export default {
  async fetch(request: Request, env: Env): Promise<Response> {
    const url = new URL(request.url);
    const { pathname } = url;
    
    // Router pattern
    const routes: Record<string, () => Promise<Response>> = {
      '/api/users': () => handleUsers(request, env),
      '/api/posts': () => handlePosts(request, env),
    };
    
    const handler = routes[pathname];
    if (handler) {
      return handler();
    }
    
    // Wildcard matching
    if (pathname.startsWith('/api/users/')) {
      const userId = pathname.split('/')[3];
      return handleUser(userId, request, env);
    }
    
    return new Response('Not Found', { status: 404 });
  },
};

KV Storage

typescript
interface Env {
  MY_KV: KVNamespace;
}

export default {
  async fetch(request: Request, env: Env): Promise<Response> {
    const url = new URL(request.url);
    
    // Set value
    await env.MY_KV.put('key', 'value', {
      expirationTtl: 3600, // 1 hour
      metadata: { created: Date.now() },
    });
    
    // Get value
    const value = await env.MY_KV.get('key');
    
    // Get with metadata
    const { value: data, metadata } = await env.MY_KV.getWithMetadata('key');
    
    // List keys
    const list = await env.MY_KV.list({ prefix: 'user:' });
    
    // Delete
    await env.MY_KV.delete('key');
    
    return Response.json({ value });
  },
};

D1 Database (SQLite)

typescript
interface Env {
  DB: D1Database;
}

// Create tables (run once via wrangler d1 execute)
// wrangler d1 execute my-database --file=./schema.sql

export default {
  async fetch(request: Request, env: Env): Promise<Response> {
    // Query
    const { results } = await env.DB.prepare(
      'SELECT * FROM users WHERE id = ?'
    ).bind(1).all();
    
    // Insert
    const { meta } = await env.DB.prepare(
      'INSERT INTO users (name, email) VALUES (?, ?)'
    ).bind('Alice', 'alice@example.com').run();
    
    // Batch operations
    const batch = await env.DB.batch([
      env.DB.prepare('INSERT INTO logs (action) VALUES (?)').bind('login'),
      env.DB.prepare('UPDATE users SET last_login = ? WHERE id = ?').bind(Date.now(), 1),
    ]);
    
    // First result only
    const user = await env.DB.prepare(
      'SELECT * FROM users WHERE email = ?'
    ).bind('alice@example.com').first();
    
    return Response.json({ results, insertId: meta.last_row_id });
  },
};

Schema Example

sql
-- schema.sql
CREATE TABLE IF NOT EXISTS users (
  id INTEGER PRIMARY KEY AUTOINCREMENT,
  name TEXT NOT NULL,
  email TEXT UNIQUE NOT NULL,
  created_at DATETIME DEFAULT CURRENT_TIMESTAMP
);

CREATE TABLE IF NOT EXISTS posts (
  id INTEGER PRIMARY KEY AUTOINCREMENT,
  user_id INTEGER NOT NULL,
  title TEXT NOT NULL,
  content TEXT,
  FOREIGN KEY (user_id) REFERENCES users(id)
);

R2 Object Storage

typescript
interface Env {
  BUCKET: R2Bucket;
}

export default {
  async fetch(request: Request, env: Env): Promise<Response> {
    const url = new URL(request.url);
    const key = url.pathname.slice(1);
    
    switch (request.method) {
      case 'PUT': {
        // Upload file
        const body = await request.arrayBuffer();
        await env.BUCKET.put(key, body, {
          httpMetadata: {
            contentType: request.headers.get('content-type') || 'application/octet-stream',
          },
          customMetadata: {
            uploadedBy: 'api',
          },
        });
        return new Response('Uploaded', { status: 201 });
      }
      
      case 'GET': {
        // Download file
        const object = await env.BUCKET.get(key);
        if (!object) {
          return new Response('Not Found', { status: 404 });
        }
        
        const headers = new Headers();
        object.writeHttpMetadata(headers);
        headers.set('etag', object.httpEtag);
        
        return new Response(object.body, { headers });
      }
      
      case 'DELETE': {
        await env.BUCKET.delete(key);
        return new Response('Deleted');
      }
      
      default:
        return new Response('Method Not Allowed', { status: 405 });
    }
  },
};

// List objects
async function listObjects(env: Env, prefix?: string) {
  const listed = await env.BUCKET.list({
    prefix,
    limit: 100,
  });
  return listed.objects.map(obj => ({
    key: obj.key,
    size: obj.size,
    uploaded: obj.uploaded,
  }));
}

Cloudflare AI

typescript
interface Env {
  AI: Ai;
}

export default {
  async fetch(request: Request, env: Env): Promise<Response> {
    const { prompt } = await request.json();
    
    // Text generation (Llama, Mistral, etc.)
    const response = await env.AI.run('@cf/meta/llama-3-8b-instruct', {
      messages: [
        { role: 'system', content: 'You are a helpful assistant.' },
        { role: 'user', content: prompt },
      ],
      max_tokens: 1024,
    });
    
    return Response.json(response);
  },
};

// Image generation
async function generateImage(env: Env, prompt: string) {
  const response = await env.AI.run('@cf/stabilityai/stable-diffusion-xl-base-1.0', {
    prompt,
    num_steps: 20,
  });
  
  return new Response(response, {
    headers: { 'content-type': 'image/png' },
  });
}

// Text embeddings
async function getEmbeddings(env: Env, text: string) {
  const response = await env.AI.run('@cf/baai/bge-base-en-v1.5', {
    text: [text],
  });
  return response.data[0]; // Float32Array
}

// Image classification
async function classifyImage(env: Env, imageData: ArrayBuffer) {
  const response = await env.AI.run('@cf/microsoft/resnet-50', {
    image: [...new Uint8Array(imageData)],
  });
  return response;
}

// Speech to text
async function transcribe(env: Env, audioData: ArrayBuffer) {
  const response = await env.AI.run('@cf/openai/whisper', {
    audio: [...new Uint8Array(audioData)],
  });
  return response.text;
}

Durable Objects

typescript
// Durable Object class
export class Counter {
  state: DurableObjectState;
  
  constructor(state: DurableObjectState) {
    this.state = state;
  }
  
  async fetch(request: Request): Promise<Response> {
    const url = new URL(request.url);
    
    let value = (await this.state.storage.get<number>('count')) || 0;
    
    switch (url.pathname) {
      case '/increment':
        value++;
        await this.state.storage.put('count', value);
        break;
      case '/decrement':
        value--;
        await this.state.storage.put('count', value);
        break;
    }
    
    return Response.json({ count: value });
  }
}

// Worker using Durable Object
interface Env {
  COUNTER: DurableObjectNamespace;
}

export default {
  async fetch(request: Request, env: Env): Promise<Response> {
    // Get unique ID for this counter (e.g., per user)
    const counterId = env.COUNTER.idFromName('global-counter');
    const counter = env.COUNTER.get(counterId);
    
    // Forward request to Durable Object
    return counter.fetch(request);
  },
};

Cloudflare Pages

pages.toml (Functions Config)

toml
[build]
command = "npm run build"
output_directory = "dist"

[[redirects]]
from = "/old-page"
to = "/new-page"
status = 301

[[headers]]
for = "/api/*"
[headers.values]
Access-Control-Allow-Origin = "*"

Pages Functions

typescript
// functions/api/hello.ts
export const onRequestGet: PagesFunction = async (context) => {
  return Response.json({ message: 'Hello!' });
};

export const onRequestPost: PagesFunction<Env> = async (context) => {
  const body = await context.request.json();
  
  // Access bindings
  await context.env.KV.put('key', JSON.stringify(body));
  
  return Response.json({ success: true });
};

// functions/api/users/[id].ts
export const onRequestGet: PagesFunction = async (context) => {
  const userId = context.params.id;
  return Response.json({ userId });
};

// Middleware: functions/_middleware.ts
export const onRequest: PagesFunction = async (context) => {
  // Auth check
  const auth = context.request.headers.get('Authorization');
  if (!auth) {
    return new Response('Unauthorized', { status: 401 });
  }
  
  // Continue to next handler
  return context.next();
};

Queues

typescript
// Producer
interface Env {
  MY_QUEUE: Queue;
}

export default {
  async fetch(request: Request, env: Env): Promise<Response> {
    // Send message to queue
    await env.MY_QUEUE.send({
      type: 'email',
      to: 'user@example.com',
      subject: 'Welcome!',
    });
    
    // Batch send
    await env.MY_QUEUE.sendBatch([
      { body: { task: 'process', id: 1 } },
      { body: { task: 'process', id: 2 } },
    ]);
    
    return Response.json({ queued: true });
  },
};

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

Cron Triggers

toml
# wrangler.toml
[triggers]
crons = ["0 0 * * *", "*/15 * * * *"]  # Daily at midnight, every 15 min

typescript
export default {
  async scheduled(event: ScheduledEvent, env: Env, ctx: ExecutionContext): Promise<void> {
    switch (event.cron) {
      case '0 0 * * *':
        await dailyCleanup(env);
        break;
      case '*/15 * * * *':
        await checkHealthStatus(env);
        break;
    }
  },
};

WebSockets

typescript
export default {
  async fetch(request: Request, env: Env): Promise<Response> {
    const upgradeHeader = request.headers.get('Upgrade');
    
    if (upgradeHeader === 'websocket') {
      const [client, server] = Object.values(new WebSocketPair());
      
      server.accept();
      server.addEventListener('message', (event) => {
        server.send(`Echo: ${event.data}`);
      });
      
      return new Response(null, {
        status: 101,
        webSocket: client,
      });
    }
    
    return new Response('Expected WebSocket', { status: 400 });
  },
};

Resources

Workers Docs: https://developers.cloudflare.com/workers/
D1 Docs: https://developers.cloudflare.com/d1/
R2 Docs: https://developers.cloudflare.com/r2/
Pages Docs: https://developers.cloudflare.com/pages/
AI Docs: https://developers.cloudflare.com/workers-ai/
Wrangler CLI: https://developers.cloudflare.com/workers/wrangler/

Publisher	hoodini
Repository	`ai-agents-skills`
Skill name	`cloudflare`
Stars	280
Forks	62
Bundled files	Instructions only
Links	Skill source Repository

Cloudflare

Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Cloudflare Platform

Quick Start

Workers

Basic Worker

wrangler.toml Configuration

Request Routing

KV Storage

D1 Database (SQLite)

Schema Example

R2 Object Storage

Cloudflare AI

Durable Objects

Cloudflare Pages

pages.toml (Functions Config)

Pages Functions

Queues

Cron Triggers

WebSockets

Resources

Frequently asked questions

Set up your own AI workspace now

Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Cloudflare Platform

Quick Start

Workers

Basic Worker

wrangler.toml Configuration

Request Routing

KV Storage

D1 Database (SQLite)

Schema Example

R2 Object Storage

Cloudflare AI

Durable Objects

Cloudflare Pages

pages.toml (Functions Config)

Pages Functions

Queues

Cron Triggers

WebSockets

Resources

Frequently asked questions

Related AI Skills

Set up your own AI workspace now