Bun Cloudflare Workers logo

Bun Cloudflare Workers

Community
secondsky
bun-cloudflare-workers

This skill should be used when the user asks about "Cloudflare Workers with Bun", "deploying Bun to Workers", "wrangler with Bun", "edge deployment", "Bun to Cloudflare", or building and deploying applications to Cloudflare Workers using Bun.

Overview

Publishersecondsky
Repositoryclaude-skills
Skill namebun-cloudflare-workers
Stars
219
Forks
31
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by secondsky on GitHub. Read the source before you install it.

Installation

Install the Bun Cloudflare Workers AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/secondsky/claude-skills.git /tmp/claude-skills
mkdir -p .claude/skills
cp -r /tmp/claude-skills/plugins/bun/skills/bun-cloudflare-workers .claude/skills/bun-cloudflare-workers
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Bun Cloudflare Workers in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Bun Cloudflare Workers on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Bun Cloudflare Workers is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Bun Cloudflare Workers

Build and deploy Cloudflare Workers using Bun for development.

Quick Start

bash
# Create new Workers project
bunx create-cloudflare my-worker
cd my-worker

# Install dependencies
bun install

# Development
bun run dev

# Deploy
bun run deploy

Secure Installation

Scaffolding tools like bunx create-cloudflare download and execute remote code. Before running, follow supply chain security best practices:

  • Block post-install scripts — Bun disables them by default; allow specific packages via trustedDependencies in package.json
  • Cooldown period — Configure minimumReleaseAge in bunfig.toml to wait 7 days for new versions
  • Audit before installing — Run socket package score npm <pkg> or use socket npm install <pkg> to check packages

Load the dependency-upgrade skill for full security configuration including Socket CLI integration, cooldown setup, lockfile validation, and CI enforcement.

Project Setup

package.json

json
{
  "scripts": {
    "dev": "wrangler dev",
    "deploy": "wrangler deploy",
    "build": "bun build src/index.ts --outdir=dist --target=browser"
  },
  "devDependencies": {
    "@cloudflare/workers-types": "^4.20260408.0",
    "wrangler": "^4.81.0"
  }
}

wrangler.toml

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

# Use Bun for local dev
[dev]
local_protocol = "http"

# Bindings
[[kv_namespaces]]
binding = "KV"
id = "xxx"

[[d1_databases]]
binding = "DB"
database_name = "my-db"
database_id = "xxx"

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 === "/") {
      return new Response("Hello from Cloudflare Workers!");
    }

    if (url.pathname === "/api/data") {
      return Response.json({ message: "Hello" });
    }

    return new Response("Not Found", { status: 404 });
  },
};

interface Env {
  KV: KVNamespace;
  DB: D1Database;
}

Using Hono

typescript
// src/index.ts
import { Hono } from "hono";

type Bindings = {
  KV: KVNamespace;
  DB: D1Database;
};

const app = new Hono<{ Bindings: Bindings }>();

app.get("/", (c) => c.text("Hello Hono!"));

app.get("/api/users", async (c) => {
  const users = await c.env.DB.prepare("SELECT * FROM users").all();
  return c.json(users.results);
});

app.post("/api/users", async (c) => {
  const { name } = await c.req.json();
  await c.env.DB.prepare("INSERT INTO users (name) VALUES (?)").bind(name).run();
  return c.json({ success: true });
});

export default app;

KV Storage

typescript
export default {
  async fetch(request: Request, env: Env): Promise<Response> {
    const url = new URL(request.url);
    const key = url.searchParams.get("key");

    if (request.method === "GET" && key) {
      const value = await env.KV.get(key);
      return Response.json({ key, value });
    }

    if (request.method === "PUT" && key) {
      const value = await request.text();
      await env.KV.put(key, value, { expirationTtl: 3600 });
      return Response.json({ success: true });
    }

    return new Response("Bad Request", { status: 400 });
  },
};

D1 Database

typescript
export default {
  async fetch(request: Request, env: Env): Promise<Response> {
    // Query
    const { results } = await env.DB.prepare(
      "SELECT * FROM users WHERE active = ?"
    ).bind(1).all();

    // Insert
    const info = await env.DB.prepare(
      "INSERT INTO users (name, email) VALUES (?, ?)"
    ).bind("Alice", "alice@example.com").run();

    // Transaction
    const batch = await env.DB.batch([
      env.DB.prepare("INSERT INTO users (name) VALUES (?)").bind("Bob"),
      env.DB.prepare("INSERT INTO users (name) VALUES (?)").bind("Charlie"),
    ]);

    return Response.json(results);
  },
};

Durable Objects

typescript
// src/counter.ts
export class Counter {
  private state: DurableObjectState;
  private value = 0;

  constructor(state: DurableObjectState) {
    this.state = state;
    this.state.blockConcurrencyWhile(async () => {
      this.value = (await this.state.storage.get("value")) || 0;
    });
  }

  async fetch(request: Request): Promise<Response> {
    const url = new URL(request.url);

    if (url.pathname === "/increment") {
      this.value++;
      await this.state.storage.put("value", this.value);
    }

    return Response.json({ value: this.value });
  }
}

// src/index.ts
export { Counter } from "./counter";

export default {
  async fetch(request: Request, env: Env): Promise<Response> {
    const id = env.COUNTER.idFromName("global");
    const stub = env.COUNTER.get(id);
    return stub.fetch(request);
  },
};
toml
# wrangler.toml
[[durable_objects.bindings]]
name = "COUNTER"
class_name = "Counter"

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

R2 Storage

typescript
export default {
  async fetch(request: Request, env: Env): Promise<Response> {
    const url = new URL(request.url);
    const key = url.pathname.slice(1);

    if (request.method === "GET") {
      const object = await env.BUCKET.get(key);
      if (!object) {
        return new Response("Not Found", { status: 404 });
      }
      return new Response(object.body, {
        headers: { "Content-Type": object.httpMetadata?.contentType || "application/octet-stream" },
      });
    }

    if (request.method === "PUT") {
      await env.BUCKET.put(key, request.body, {
        httpMetadata: { contentType: request.headers.get("Content-Type") || undefined },
      });
      return Response.json({ success: true });
    }

    return new Response("Method Not Allowed", { status: 405 });
  },
};

Development with Bun

Local Development

bash
# Run with wrangler (uses Bun for TypeScript)
bun run dev

# Or directly
bunx wrangler dev

Testing with Bun

typescript
// src/index.test.ts
import { describe, test, expect } from "bun:test";

// Mock worker
const worker = {
  async fetch(request: Request) {
    return new Response("Hello");
  },
};

describe("Worker", () => {
  test("returns hello", async () => {
    const request = new Request("http://localhost/");
    const response = await worker.fetch(request);
    expect(await response.text()).toBe("Hello");
  });
});

Miniflare for Testing

typescript
import { Miniflare } from "miniflare";

const mf = new Miniflare({
  script: await Bun.file("./dist/index.js").text(),
  kvNamespaces: ["KV"],
});

const response = await mf.dispatchFetch("http://localhost/");
console.log(await response.text());

Build for Production

typescript
// build.ts
await Bun.build({
  entrypoints: ["./src/index.ts"],
  outdir: "./dist",
  target: "browser", // Workers use browser APIs
  minify: true,
  sourcemap: "external",
});
bash
bun run build.ts
bunx wrangler deploy

Environment Variables

toml
# wrangler.toml
[vars]
API_URL = "https://api.example.com"

# Secrets (set via CLI)
# wrangler secret put API_KEY
typescript
export default {
  async fetch(request: Request, env: Env): Promise<Response> {
    console.log(env.API_URL);    // From vars (non-secret, safe to log)
    // Access a Worker secret (NEVER log it — wrangler tail / Logpush persist logs)
    if (env.API_KEY) {
      // use env.API_KEY to call the upstream API
    }
    // ❌ NEVER: console.log(env.API_KEY) — secrets must not appear in logs
    return new Response("OK");
  },
};

Scheduled Workers (Cron)

typescript
export default {
  async scheduled(event: ScheduledEvent, env: Env, ctx: ExecutionContext): Promise<void> {
    console.log("Cron triggered at:", event.scheduledTime);
    // Perform scheduled task
    await env.DB.prepare("DELETE FROM logs WHERE created_at < ?")
      .bind(Date.now() - 7 * 24 * 60 * 60 * 1000)
      .run();
  },

  async fetch(request: Request, env: Env): Promise<Response> {
    return new Response("OK");
  },
};
toml
# wrangler.toml
[triggers]
crons = ["0 * * * *"]  # Every hour

Common Errors

ErrorCauseFix
Bun API not availableWorkers use V8Use Web APIs only
Module not foundBuild issueCheck bundler config
Script too largeExceeds 10MBOptimize bundle
CPU time exceededLong executionOptimize or use queues

API Compatibility

Workers support Web APIs, NOT Bun-specific APIs:

AvailableNot Available
fetch()Bun.file()
ResponseBun.serve()
Requestbun:sqlite
URLbun:ffi
cryptofs
TextEncoderchild_process

When to Load References

Load references/bindings.md when:

  • Advanced KV/D1/R2 patterns
  • Queue workers
  • Service bindings

Load references/performance.md when:

  • Bundle optimization
  • Cold start reduction
  • Caching strategies

Frequently asked questions

What does the Bun Cloudflare Workers AI skill do?

This skill should be used when the user asks about "Cloudflare Workers with Bun", "deploying Bun to Workers", "wrangler with Bun", "edge deployment", "Bun to Cloudflare", or building and deploying applications to Cloudflare Workers using Bun.

Why use Bun Cloudflare Workers on TypingMind?

Because you install it once and use it with any model. Bun Cloudflare Workers 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 Bun Cloudflare Workers in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/secondsky/claude-skills/tree/main/plugins/bun/skills/bun-cloudflare-workers. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Bun Cloudflare Workers?

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 Bun Cloudflare Workers?

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

Is the Bun Cloudflare Workers AI skill free?

Yes. It is published on GitHub by secondsky under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

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.

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