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Bun Websocket Server

Community
secondsky
bun-websocket-server

This skill should be used when the user asks about "WebSocket in Bun", "real-time communication", "Bun.serve websocket", "ws server", "socket connections", "pub/sub", "broadcasting messages", "WebSocket upgrade", or building real-time applications with Bun.

Overview

Publishersecondsky
Repositoryclaude-skills
Skill namebun-websocket-server
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 Websocket Server 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-websocket-server .claude/skills/bun-websocket-server
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Bun Websocket Server 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 Websocket Server 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 Websocket Server 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 WebSocket Server

Bun has built-in WebSocket support integrated with Bun.serve().

Quick Start

typescript
const server = Bun.serve({
  fetch(req, server) {
    // Upgrade to WebSocket
    if (server.upgrade(req)) {
      return; // Upgraded successfully
    }
    return new Response("Not a WebSocket request", { status: 400 });
  },
  websocket: {
    open(ws) {
      console.log("Client connected");
    },
    message(ws, message) {
      console.log("Received:", message);
      ws.send(`Echo: ${message}`);
    },
    close(ws) {
      console.log("Client disconnected");
    },
  },
});

console.log(`WebSocket server running on ws://localhost:${server.port}`);

WebSocket Handlers

typescript
Bun.serve({
  fetch(req, server) {
    server.upgrade(req);
  },
  websocket: {
    // Client connected
    open(ws) {
      console.log("New connection");
    },

    // Message received
    message(ws, message) {
      // message is string | Buffer
      if (typeof message === "string") {
        console.log("Text:", message);
      } else {
        console.log("Binary:", message);
      }
    },

    // Connection closed
    close(ws, code, reason) {
      console.log(`Closed: ${code} - ${reason}`);
    },

    // Drain event (buffer flushed)
    drain(ws) {
      console.log("Buffer drained");
    },

    // Ping received
    ping(ws, data) {
      // Pong sent automatically
    },

    // Pong received
    pong(ws, data) {
      console.log("Pong received");
    },
  },
});

Sending Messages

typescript
websocket: {
  message(ws, message) {
    // Send text
    ws.send("Hello");

    // Send JSON
    ws.send(JSON.stringify({ type: "greeting", data: "Hello" }));

    // Send binary
    ws.send(new Uint8Array([1, 2, 3]));
    ws.send(Buffer.from("binary data"));

    // Send with compression
    ws.send("compressed message", true);

    // Check if buffer is full
    const bufferedAmount = ws.send("data");
    if (bufferedAmount > 1024 * 1024) {
      console.log("Buffer getting full");
    }
  },
}

Attaching Data to Connections

typescript
interface UserData {
  id: string;
  name: string;
  joinedAt: Date;
}

Bun.serve<UserData>({
  fetch(req, server) {
    const url = new URL(req.url);
    const userId = url.searchParams.get("userId");

    // Attach data during upgrade
    server.upgrade(req, {
      data: {
        id: userId,
        name: "User " + userId,
        joinedAt: new Date(),
      },
    });
  },
  websocket: {
    open(ws) {
      // Access attached data
      console.log(`${ws.data.name} connected`);
    },
    message(ws, message) {
      console.log(`${ws.data.name}: ${message}`);
    },
  },
});

Pub/Sub (Topics)

typescript
Bun.serve({
  fetch(req, server) {
    const url = new URL(req.url);
    const room = url.searchParams.get("room") || "general";

    server.upgrade(req, {
      data: { room },
    });
  },
  websocket: {
    open(ws) {
      // Subscribe to a topic
      ws.subscribe(ws.data.room);

      // Publish to topic (excludes sender)
      ws.publish(ws.data.room, `User joined ${ws.data.room}`);
    },
    message(ws, message) {
      // Broadcast to all in room (excludes sender)
      ws.publish(ws.data.room, message);
    },
    close(ws) {
      // Unsubscribe (automatic on close)
      ws.unsubscribe(ws.data.room);
      ws.publish(ws.data.room, "User left");
    },
  },
});

Broadcasting to All Clients

typescript
Bun.serve({
  fetch(req, server) {
    server.upgrade(req);
  },
  websocket: {
    open(ws) {
      // Subscribe to global topic
      ws.subscribe("global");
    },
    message(ws, message) {
      // Broadcast to ALL clients including sender
      server.publish("global", message);
    },
  },
});

Server-Level Publish

typescript
const server = Bun.serve({
  fetch(req, server) {
    const url = new URL(req.url);

    // HTTP endpoint to publish
    if (url.pathname === "/broadcast") {
      const message = url.searchParams.get("msg");
      server.publish("global", message);
      return new Response("Broadcasted");
    }

    server.upgrade(req);
  },
  websocket: {
    open(ws) {
      ws.subscribe("global");
    },
  },
});

// Can also publish from outside fetch
setInterval(() => {
  server.publish("global", `Server time: ${new Date().toISOString()}`);
}, 5000);

WebSocket Options

typescript
Bun.serve({
  websocket: {
    // Max message size (default 16MB)
    maxPayloadLength: 1024 * 1024, // 1MB

    // Idle timeout in seconds (default 120)
    idleTimeout: 60,

    // Backpressure limit
    backpressureLimit: 1024 * 1024,

    // Enable compression
    perMessageDeflate: true,
    // Or with options
    perMessageDeflate: {
      compress: "shared",
      decompress: "shared",
    },

    // Send/receive pings
    sendPings: true,

    // Handlers
    open(ws) {},
    message(ws, message) {},
    close(ws) {},
  },
});

Client-Side Connection

javascript
// Browser
const ws = new WebSocket("ws://localhost:3000");

ws.onopen = () => {
  ws.send("Hello Server!");
};

ws.onmessage = (event) => {
  console.log("Received:", event.data);
};

ws.onclose = () => {
  console.log("Disconnected");
};

Authentication

typescript
Bun.serve({
  fetch(req, server) {
    // Verify auth before upgrade
    const token = req.headers.get("Authorization");

    if (!verifyToken(token)) {
      return new Response("Unauthorized", { status: 401 });
    }

    const user = decodeToken(token);
    server.upgrade(req, {
      data: { userId: user.id },
    });
  },
  websocket: {
    open(ws) {
      console.log(`Authenticated user ${ws.data.userId} connected`);
    },
  },
});

Common Errors

ErrorCauseFix
Upgrade failedInvalid requestCheck upgrade headers
Connection closedClient disconnectHandle in close handler
Message too largeExceeds maxPayloadLengthIncrease limit or chunk data
BackpressureSlow clientCheck buffer, wait for drain

Common Patterns

Chat Room

typescript
Bun.serve({
  fetch(req, server) {
    const url = new URL(req.url);
    const username = url.searchParams.get("user") || "Anonymous";

    server.upgrade(req, {
      data: { username },
    });
  },
  websocket: {
    open(ws) {
      ws.subscribe("chat");
      ws.publish("chat", `${ws.data.username} joined`);
    },
    message(ws, message) {
      ws.publish("chat", `${ws.data.username}: ${message}`);
    },
    close(ws) {
      ws.publish("chat", `${ws.data.username} left`);
    },
  },
});

When to Load References

Load references/compression.md when:

  • perMessageDeflate configuration
  • Compression tuning
  • Binary message handling

Load references/scaling.md when:

  • Multiple server instances
  • Redis pub/sub integration
  • Horizontal scaling

Frequently asked questions

What does the Bun Websocket Server AI skill do?

This skill should be used when the user asks about "WebSocket in Bun", "real-time communication", "Bun.serve websocket", "ws server", "socket connections", "pub/sub", "broadcasting messages", "WebSocket upgrade", or building real-time applications with Bun.

Why use Bun Websocket Server on TypingMind?

Because you install it once and use it with any model. Bun Websocket Server 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 Websocket Server in TypingMind?

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

Which AI models can use Bun Websocket Server?

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 Websocket Server?

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

Is the Bun Websocket Server 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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