streaming — streaming setup for assistant-ui streaming, yash-full-stack-personal, community, streaming setup for assistant-ui, ide skills, Claude Code, Cursor, Windsurf

v0.0.1
GitHub

About this Skill

Perfect for AI Agents needing real-time data processing from AI backends using the assistant-stream package. Streaming is a technology that handles real-time data streams from AI backends, utilizing the assistant-stream package and native assistant-ui formats.

Features

Handles streaming from AI backends using the assistant-stream package
Supports AI SDK data stream format as referenced in data-stream.md
Utilizes native assistant-ui format as outlined in assistant-transport.md
Employs encoders and decoders as detailed in encoders.md
Consults the latest API from assistant-ui.com/llms.txt for optimal performance

# Core Topics

yashness yashness
[0]
[0]
Updated: 3/8/2026

Agent Capability Analysis

The streaming skill by yashness is an open-source community AI agent skill for Claude Code and other IDE workflows, helping agents execute tasks with better context, repeatability, and domain-specific guidance. Optimized for streaming setup for assistant-ui.

Ideal Agent Persona

Perfect for AI Agents needing real-time data processing from AI backends using the assistant-stream package.

Core Value

Empowers agents to handle streaming data from AI backends using the assistant-stream package, enabling seamless integration with AI SDK data streams and native assistant-ui formats like assistant-transport, with support for various encoders and decoders.

Capabilities Granted for streaming

Streaming AI-generated content in real-time
Processing live data streams from AI backends
Integrating AI SDK data streams with native assistant-ui formats

! Prerequisites & Limits

  • Requires the assistant-stream package
  • Dependent on AI backend data stream availability
  • Must consult the latest API documentation at assistant-ui.com/llms.txt
Labs Demo

Browser Sandbox Environment

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Boot Container Sandbox

streaming

Install streaming, an AI agent skill for AI agent workflows and automation. Works with Claude Code, Cursor, and Windsurf with one-command setup.

SKILL.md
Readonly

assistant-ui Streaming

Always consult assistant-ui.com/llms.txt for latest API.

The assistant-stream package handles streaming from AI backends.

References

When to Use

Using Vercel AI SDK?
├─ Yes → toUIMessageStreamResponse() (no assistant-stream needed)
└─ No → assistant-stream for custom backends

Installation

bash
1npm install assistant-stream

Custom Streaming Response

ts
1import { createAssistantStreamResponse } from "assistant-stream"; 2 3export async function POST(req: Request) { 4 return createAssistantStreamResponse(async (stream) => { 5 stream.appendText("Hello "); 6 stream.appendText("world!"); 7 8 // Tool call example 9 const tool = stream.addToolCallPart({ toolCallId: "1", toolName: "get_weather" }); 10 tool.argsText.append('{"city":"NYC"}'); 11 tool.argsText.close(); 12 tool.setResponse({ result: { temperature: 22 } }); 13 14 stream.close(); 15 }); 16}

With useLocalRuntime

useLocalRuntime expects ChatModelRunResult chunks. Yield content parts for streaming:

tsx
1import { useLocalRuntime } from "@assistant-ui/react"; 2 3const runtime = useLocalRuntime({ 4 model: { 5 async *run({ messages, abortSignal }) { 6 const response = await fetch("/api/chat", { 7 method: "POST", 8 body: JSON.stringify({ messages }), 9 signal: abortSignal, 10 }); 11 12 const reader = response.body?.getReader(); 13 const decoder = new TextDecoder(); 14 let buffer = ""; 15 16 while (reader) { 17 const { done, value } = await reader.read(); 18 if (done) break; 19 20 buffer += decoder.decode(value, { stream: true }); 21 const parts = buffer.split("\n"); 22 buffer = parts.pop() ?? ""; 23 24 for (const chunk of parts.filter(Boolean)) { 25 yield { content: [{ type: "text", text: chunk }] }; 26 } 27 } 28 }, 29 }, 30});

Debugging Streams

ts
1import { AssistantStream, DataStreamDecoder } from "assistant-stream"; 2 3const stream = AssistantStream.fromResponse(response, new DataStreamDecoder()); 4for await (const event of stream) { 5 console.log("Event:", JSON.stringify(event, null, 2)); 6}

Stream Event Types

  • part-start with part.type = "text" | "reasoning" | "tool-call" | "source" | "file"
  • text-delta with streamed text
  • result with tool results
  • step-start, step-finish, message-finish
  • error strings

Common Gotchas

Stream not updating UI

  • Check Content-Type is text/event-stream
  • Check for CORS errors

Tool calls not rendering

  • addToolCallPart needs both toolCallId and toolName
  • Register tool UI with makeAssistantToolUI

Partial text not showing

  • Use text-delta events for streaming

FAQ & Installation Steps

These questions and steps mirror the structured data on this page for better search understanding.

? Frequently Asked Questions

What is streaming?

Perfect for AI Agents needing real-time data processing from AI backends using the assistant-stream package. Streaming is a technology that handles real-time data streams from AI backends, utilizing the assistant-stream package and native assistant-ui formats.

How do I install streaming?

Run the command: npx killer-skills add yashness/yash-full-stack-personal/streaming. It works with Cursor, Windsurf, VS Code, Claude Code, and 19+ other IDEs.

What are the use cases for streaming?

Key use cases include: Streaming AI-generated content in real-time, Processing live data streams from AI backends, Integrating AI SDK data streams with native assistant-ui formats.

Which IDEs are compatible with streaming?

This skill is compatible with Cursor, Windsurf, VS Code, Trae, Claude Code, OpenClaw, Aider, Codex, OpenCode, Goose, Cline, Roo Code, Kiro, Augment Code, Continue, GitHub Copilot, Sourcegraph Cody, and Amazon Q Developer. Use the Killer-Skills CLI for universal one-command installation.

Are there any limitations for streaming?

Requires the assistant-stream package. Dependent on AI backend data stream availability. Must consult the latest API documentation at assistant-ui.com/llms.txt.

How To Install

  1. 1. Open your terminal

    Open the terminal or command line in your project directory.

  2. 2. Run the install command

    Run: npx killer-skills add yashness/yash-full-stack-personal/streaming. The CLI will automatically detect your IDE or AI agent and configure the skill.

  3. 3. Start using the skill

    The skill is now active. Your AI agent can use streaming immediately in the current project.

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