Agent Builder Vercel SDK Skill for Claude

Build conversational AI agents using Vercel AI SDK + OpenRouter. Use when creating Next.js frontends with streaming UI, tool calling, and multi-provider support.

2k tokens

context cost

the whole folder, loaded on every use

files

instructions only

copies elsewhere

how many repositories repackaged it

532

stars on the repo

on the repository, not the skill itself

Install

one command, takes just this skill from the repository

npx skills add https://github.com/majiayu000/claude-skill-registry --skill agent-builder-vercel-sdk

What comes with it

389 bytes besides the instruction

metadata.json

The instruction itself

23 sections, as written by the author

Vercel AI SDK Agent Builder

Purpose

Create streaming AI chat interfaces with minimal code using Vercel AI SDK and OpenRouter provider.

When to Use

Building Next.js frontend with chat UI
Need streaming responses with SSE
Want type-safe tool calling in TypeScript
Switching between multiple AI providers
Building agentic loops with stopWhen/prepareStep

Quick Start

Installation

npm install ai @openrouter/ai-sdk-provider zod

Environment Variables

OPENROUTER_API_KEY=sk-or-v1-...
NEXT_PUBLIC_SITE_URL=http://localhost:3000

Backend Setup (Route Handler)

Basic Chat Endpoint

// app/api/chat/route.ts
import { OpenRouter } from '@openrouter/ai-sdk-provider'
import { streamText } from 'ai'

const openrouter = new OpenRouter({
  apiKey: process.env.OPENROUTER_API_KEY
})

export async function POST(req: Request) {
  const { messages } = await req.json()

  const result = streamText({
    model: openrouter('openai/gpt-4o'),
    system: 'You are a helpful assistant',
    messages,
  })

  return result.toDataStreamResponse()
}

With Tool Calling

import { z } from 'zod'
import { tool } from 'ai'

const tools = {
  generateImage: tool({
    description: 'Generate images using AI',
    parameters: z.object({
      prompt: z.string().describe('Image description'),
      numImages: z.number().min(1).max(10).default(1)
    }),
    execute: async ({ prompt, numImages }) => {
      // Your implementation
      const images = await generateImages(prompt, numImages)
      return { images }
    }
  })
}

export async function POST(req: Request) {
  const { messages } = await req.json()

  const result = streamText({
    model: openrouter('openai/gpt-4o'),
    system: 'You are a helpful assistant',
    messages,
    tools,
    maxSteps: 5 // Enable agentic loop
  })

  return result.toDataStreamResponse()
}

Frontend Integration

Using useChat Hook

'use client'

import { useChat } from 'ai/react'

export default function Chat() {
  const { messages, input, handleInputChange, handleSubmit, isLoading } = useChat()

  return (
    <div className="flex flex-col h-screen">
      {/* Messages */}
      <div className="flex-1 overflow-y-auto p-4">
        {messages.map(m => (
          <div key={m.id} className={m.role === 'user' ? 'text-right' : 'text-left'}>
            <div className="inline-block p-3 rounded-lg">
              {m.content}
            </div>
          </div>
        ))}
      </div>

      {/* Input */}
      <form onSubmit={handleSubmit} className="p-4 border-t">
        <input
          value={input}
          onChange={handleInputChange}
          placeholder="Type a message..."
          disabled={isLoading}
          className="w-full px-4 py-2 border rounded"
        />
      </form>
    </div>
  )
}

With Tool Results Display

'use client'

import { useChat } from 'ai/react'

export default function ChatWithTools() {
  const { messages, input, handleInputChange, handleSubmit } = useChat()

  return (
    <div>
      {messages.map(m => (
        <div key={m.id}>
          {m.content}

          {/* Display tool calls */}
          {m.toolInvocations?.map(tool => (
            <div key={tool.toolCallId} className="bg-gray-100 p-2 rounded">
              <strong>{tool.toolName}</strong>
              {tool.state === 'result' && (
                <pre>{JSON.stringify(tool.result, null, 2)}</pre>
              )}
            </div>
          ))}
        </div>
      ))}

      <form onSubmit={handleSubmit}>
        <input value={input} onChange={handleInputChange} />
      </form>
    </div>
  )
}

Advanced Patterns

Multi-Step Agentic Loop

const result = streamText({
  model: openrouter('openai/gpt-4o'),
  messages,
  tools,
  maxSteps: 5,

  // Control loop behavior
  onStepFinish: ({ stepType, text, toolCalls }) => {
    console.log(`Step finished: ${stepType}`)
  },

  // Stop condition
  experimental_continueSteps: true
})

Custom Streaming with streamUI

import { streamUI } from 'ai/rsc'

export async function generateUI(prompt: string) {
  const result = streamUI({
    model: openrouter('openai/gpt-4o'),
    prompt,
    text: ({ content }) => <p>{content}</p>,
    tools: {
      showImage: {
        description: 'Display an image',
        parameters: z.object({ url: z.string() }),
        generate: async ({ url }) => <img src={url} />
      }
    }
  })

  return result.value
}

tldraw Agent Pattern

Based on: /Users/danielcarreon/Documents/AI/software/tldraw-agent/

// Incremental JSON parsing pattern
async function* streamActions(model, prompt) {
  const { textStream } = streamText({
    model,
    system: systemPrompt,
    messages,
    maxOutputTokens: 8192,
    temperature: 0
  })

  let buffer = '{"actions": [{"_type":'

  for await (const text of textStream) {
    buffer += text

    // Parse incremental JSON
    const partialObject = closeAndParseJson(buffer)
    if (!partialObject) continue

    const actions = partialObject.actions
    if (!Array.isArray(actions)) continue

    // Yield actions as they complete
    for (const action of actions) {
      if (action.complete) {
        yield action
      }
    }
  }
}

OpenRouter Provider Setup

import { OpenRouter } from '@openrouter/ai-sdk-provider'

const openrouter = new OpenRouter({
  apiKey: process.env.OPENROUTER_API_KEY,
  // Optional: customize
  baseURL: 'https://openrouter.ai/api/v1',
  headers: {
    'HTTP-Referer': process.env.NEXT_PUBLIC_SITE_URL,
    'X-Title': 'My App'
  }
})

// Use different models
const gpt4 = openrouter('openai/gpt-4o')
const claude = openrouter('anthropic/claude-3-5-sonnet')
const gemini = openrouter('google/gemini-2.0-flash-exp')

Error Handling

export async function POST(req: Request) {
  try {
    const { messages } = await req.json()

    const result = streamText({
      model: openrouter('openai/gpt-4o'),
      messages,
      onError: (error) => {
        console.error('Stream error:', error)
      }
    })

    return result.toDataStreamResponse()
  } catch (error) {
    return new Response(
      JSON.stringify({ error: error.message }),
      { status: 500 }
    )
  }
}

Testing

import { streamText } from 'ai'
import { OpenRouter } from '@openrouter/ai-sdk-provider'

describe('Chat API', () => {
  it('should stream response', async () => {
    const openrouter = new OpenRouter({
      apiKey: process.env.OPENROUTER_API_KEY
    })

    const result = streamText({
      model: openrouter('openai/gpt-4o'),
      prompt: 'Say hello'
    })

    const chunks = []
    for await (const chunk of result.textStream) {
      chunks.push(chunk)
    }

    expect(chunks.length).toBeGreaterThan(0)
  })
})

Best Practices

Type Safety: Use Zod for tool parameters
Error Boundaries: Wrap chat UI in ErrorBoundary
Loading States: Show loading UI during streaming
Optimistic Updates: Update UI before server response
Tool Results: Display tool executions to user
Rate Limiting: Implement rate limits on API routes
Context Management: Limit message history to avoid token overflow

Common Patterns

Image Generation Agent

const tools = {
  generateAvatar: tool({
    description: 'Generate avatar with DANI identity',
    parameters: z.object({
      prompt: z.string(),
      numImages: z.number().default(3)
    }),
    execute: async ({ prompt, numImages }) => {
      const response = await fetch('/api/generate', {
        method: 'POST',
        body: JSON.stringify({ prompt, numImages })
      })
      return await response.json()
    }
  }),

  combineImages: tool({
    description: 'Combine multiple images',
    parameters: z.object({
      imageUrls: z.array(z.string()),
      prompt: z.string()
    }),
    execute: async ({ imageUrls, prompt }) => {
      // Nano Banana integration
      return await combineWithNanoBanana(imageUrls, prompt)
    }
  })
}

Resources

Other skills for the same job

different authors, same section of the catalogue

MCP Builder

by anthropics

vendor ×13

Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).

Automatically creates user-facing changelogs from git commits by analyzing commit history, categorizing changes, and transforming technical commits into clear, customer-friendly release notes. Turns hours of manual changelog writing into minutes of automated generation.

774 tokens

Finishing A Development Branch

by ZhanlinCui

×7

Use when implementation is complete, all tests pass, and you need to decide how to integrate the work - guides completion of development work by presenting structured options for merge, PR, or cleanup

Vercel React Native Skills

by vercel-labs

vendor ×6

React Native and Expo best practices for building performant mobile apps. Use when building React Native components, optimizing list performance, implementing animations, or working with native modules. Triggers on tasks involving React Native, Expo, mobile performance, or native platform APIs.

39k tokens

Vercel React Best Practices

by ratacat

×5

React and Next.js performance optimization guidelines from Vercel Engineering. This skill should be used when writing, reviewing, or refactoring React/Next.js code to ensure optimal performance patterns. Triggers on tasks involving React components, Next.js pages, data fetching, bundle optimization, or performance improvements.

Next.js best practices - file conventions, RSC boundaries, data patterns, async APIs, metadata, error handling, route handlers, image/font optimization, bundling

Use when starting feature work that needs isolation from current workspace or before executing implementation plans - creates isolated git worktrees with smart directory selection and safety verification

1k tokens

How to use it

Copy the folder

Take majiayu000/agent-builder-vercel-sdk from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

The agent identifies a skill by the name field in its header. Two skills with the same name cannot sit side by side — one of them will be ignored.

Install what it needs

The instructions reference npm. Without those the skill loads but fails at the first command.