agentleFS
laguagulaguagu/claude-code-nextjs-skills/skills/ai-sdk-6/SKILL.md

ai-sdk-6

Skill72 starsChanged 18 days agoMITView on GitHub

Vercel AI SDK v6 development, for projects already on ai@6. Use when building or maintaining AI agents, chatbots, tool integrations, streaming apps, or structured output in a v6 codebase. New projects and ai@7 code use ai-sdk-7; an unknown version goes through ai-sdk. Covers ToolLoopAgent, useChat, generateText, streamText, tool approval, provider tools, MCP integration, and Output patterns.

1 other project uses a version of this.

Adopted by

Most recently taken up by

The file

We split this skill into the rules it gives an agent

3 rules. About 0 are common, 0 are distinctive, and 3 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈0

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 3

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Very specific:

    Read the resolved ai/docs/, provider docs and source/types for the exact minor release.

    line 12
  2. Very specific:

    Use text functions with Output for new schema output.

    line 29
  3. Very specific:

    Use a configured model verified for the actual provider.

    line 39

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

AI SDK 6 maintenance

Apply this skill only to ai@6 projects. Resolve compatible provider and UI packages from the existing manifest/lockfile. Use ai-sdk when the major is unknown and ai-sdk-7 for an authorized upgrade.

Read the resolved ai/docs/, provider docs and source/types for the exact minor release. In a monorepo, resolve from the app using the dependency. Default web docs and repository main can describe v7. ai bundles docs/ from 6.0.32; the tree mirrors the website paths (mapping in ai-sdk). If installed docs are unavailable, select the ai@<resolved-version> tag in vercel/ai and read content/docs/. Feature links here use a checked v6 documentation snapshot as a fallback; match the project's release when available. This tag selects documentation, not an application dependency version.

Boundaries that differ from old and new code

  • Text functions use system; ToolLoopAgent uses instructions.
  • Loop limits use stopWhen: stepCountIs(...). Text functions default to one step (tool execution without a follow-up answer); ToolLoopAgent defaults to 20.
  • convertToModelMessages is async; await it.
  • Use text functions with Output for new schema output; legacy object functions are deprecated.
  • Tools use inputSchema; SDK-executed approval is tool-level needsApproval. V6 does not know v7's toolApproval: unless a typecheck rejects it, it is ignored and the tool runs without approval.
  • React useChat owns messages and stream status, not form input. Use the appropriate transport and UI-message response protocol.
  • Core completion callbacks are onFinish; result usage is final-step usage, while totalUsage covers all steps.

Use a configured model verified for the actual provider. Typecheck and test a multi-turn stream with relevant tool/approval/error states after integration changes.

Core shape

Checked with ai@6.0.298, @ai-sdk/react@3.0.301, zod@4.6.5, React 19 and TypeScript 5.9: tsc --noEmit, plus a mock-model run through one tool step and the follow-up answer. A string model resolves through the AI Gateway. In v7, system, stepCountIs and result.toUIMessageStreamResponse() become instructions, isStepCount and createUIMessageStreamResponse({ stream: toUIMessageStream({ stream: result.stream }) }).

// app/api/chat/route.ts
import { convertToModelMessages, stepCountIs, streamText, tool, validateUIMessages,
  type InferUITools, type UIDataTypes, type UIMessage } from 'ai';
import { z } from 'zod';

const tools = {
  weather: tool({
    inputSchema: z.object({ city: z.string() }),
    execute: async ({ city }) => ({ city, celsius: 21 }),
  }),
};
export type ChatMessage = UIMessage<never, UIDataTypes, InferUITools<typeof tools>>;

export async function POST(req: Request) {
  const { messages } = await req.json();
  const uiMessages = await validateUIMessages<ChatMessage>({ messages, tools });
  const result = streamText({
    model: 'provider/model-id', // configured gateway ID, or a provider instance
    system: 'Answer briefly.',
    messages: await convertToModelMessages(uiMessages),
    tools,
    stopWhen: stepCountIs(5),
  });
  return result.toUIMessageStreamResponse();
}
// app/page.tsx
'use client';
import { useChat } from '@ai-sdk/react';
import { DefaultChatTransport } from 'ai';
import type { ChatMessage } from './api/chat/route';

const transport = new DefaultChatTransport<ChatMessage>({ api: '/api/chat' });

export default function Chat() {
  const { messages, sendMessage, status } = useChat<ChatMessage>({ transport });
  return (
    <form action={(form) => void sendMessage({ text: String(form.get('text')) })}>
      {messages.map((m) => <div key={m.id}>{m.parts.map((part, i) =>
        part.type === 'text' ? <p key={i}>{part.text}</p>
        : part.type === 'tool-weather' && part.state === 'output-available'
          ? <p key={i}>{part.output.city}: {part.output.celsius} °C</p> : null)}</div>)}
      <input name="text" disabled={status === 'submitted' || status === 'streaming'} />
    </form>
  );
}

Read for the feature

  • Agents: loop limits, call options and context.
  • Core functions: output and stream contracts.
  • Tools: execution, approval and typed states.
  • UI hooks: transport, restoration and stream lifetime.
  • Middleware: provider interception.
  • MCP: transport and cleanup.
  • Workflows: when a loop needs explicit orchestration.
  • Examples: version-matched official implementations.

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.