develop-ai-functions-example

34
4
Source

Develop examples for AI SDK functions. Use when creating, running, or modifying examples under examples/ai-functions/src to validate provider support, demonstrate features, or create test fixtures.

Install

mkdir -p .claude/skills/develop-ai-functions-example && curl -L -o skill.zip "https://mcp.directory/api/skills/download/534" && unzip -o skill.zip -d .claude/skills/develop-ai-functions-example && rm skill.zip

Installs to .claude/skills/develop-ai-functions-example

About this skill

AI Functions Examples

The examples/ai-functions/ directory contains scripts for validating, testing, and iterating on AI SDK functions across providers.

Example Categories

Examples are organized by AI SDK function in examples/ai-functions/src/:

DirectoryPurpose
generate-text/Non-streaming text generation with generateText()
stream-text/Streaming text generation with streamText()
generate-object/Structured output generation with generateObject()
stream-object/Streaming structured output with streamObject()
agent/ToolLoopAgent examples for agentic workflows
embed/Single embedding generation with embed()
embed-many/Batch embedding generation with embedMany()
generate-image/Image generation with generateImage()
generate-speech/Text-to-speech with generateSpeech()
transcribe/Audio transcription with transcribe()
rerank/Document reranking with rerank()
middleware/Custom middleware implementations
registry/Provider registry setup and usage
telemetry/OpenTelemetry integration
complex/Multi-component examples (agents, routers)
lib/Shared utilities (not examples)
tools/Reusable tool definitions

File Naming Convention

Examples follow the pattern: {provider}-{feature}.ts

PatternExampleDescription
{provider}.tsopenai.tsBasic provider usage
{provider}-{feature}.tsopenai-tool-call.tsSpecific feature
{provider}-{sub-provider}.tsamazon-bedrock-anthropic.tsProvider with sub-provider
{provider}-{sub-provider}-{feature}.tsgoogle-vertex-anthropic-cache-control.tsSub-provider with feature

Example Structure

All examples use the run() wrapper from lib/run.ts which:

  • Loads environment variables from .env
  • Provides error handling with detailed API error logging

Basic Template

import { providerName } from '@ai-sdk/provider-name';
import { generateText } from 'ai';
import { run } from '../lib/run';

run(async () => {
  const result = await generateText({
    model: providerName('model-id'),
    prompt: 'Your prompt here.',
  });

  console.log(result.text);
  console.log('Token usage:', result.usage);
  console.log('Finish reason:', result.finishReason);
});

Streaming Template

import { providerName } from '@ai-sdk/provider-name';
import { streamText } from 'ai';
import { printFullStream } from '../lib/print-full-stream';
import { run } from '../lib/run';

run(async () => {
  const result = streamText({
    model: providerName('model-id'),
    prompt: 'Your prompt here.',
  });

  await printFullStream({ result });
});

Tool Calling Template

import { providerName } from '@ai-sdk/provider-name';
import { generateText, tool } from 'ai';
import { z } from 'zod';
import { run } from '../lib/run';

run(async () => {
  const result = await generateText({
    model: providerName('model-id'),
    tools: {
      myTool: tool({
        description: 'Tool description',
        inputSchema: z.object({
          param: z.string().describe('Parameter description'),
        }),
        execute: async ({ param }) => {
          return { result: `Processed: ${param}` };
        },
      }),
    },
    prompt: 'Use the tool to...',
  });

  console.log(JSON.stringify(result, null, 2));
});

Structured Output Template

import { providerName } from '@ai-sdk/provider-name';
import { generateObject } from 'ai';
import { z } from 'zod';
import { run } from '../lib/run';

run(async () => {
  const result = await generateObject({
    model: providerName('model-id'),
    schema: z.object({
      name: z.string(),
      items: z.array(z.string()),
    }),
    prompt: 'Generate a...',
  });

  console.log(JSON.stringify(result.object, null, 2));
  console.log('Token usage:', result.usage);
});

Running Examples

From the examples/ai-functions directory:

pnpm tsx src/generate-text/openai.ts
pnpm tsx src/stream-text/openai-tool-call.ts
pnpm tsx src/agent/openai-generate.ts

When to Write Examples

Write examples when:

  1. Adding a new provider: Create basic examples for each supported API (generateText, streamText, generateObject, etc.)

  2. Implementing a new feature: Demonstrate the feature with at least one provider example

  3. Reproducing a bug: Create an example that shows the issue for debugging

  4. Adding provider-specific options: Show how to use providerOptions for provider-specific settings

  5. Creating test fixtures: Use examples to generate API response fixtures (see capture-api-response-test-fixture skill)

Utility Helpers

The lib/ directory contains shared utilities:

FilePurpose
run.tsError-handling wrapper with .env loading
print.tsClean object printing (removes undefined values)
print-full-stream.tsColored streaming output for tool calls, reasoning, text
save-raw-chunks.tsSave streaming chunks for test fixtures
present-image.tsDisplay images in terminal
save-audio.tsSave audio files to disk

Using print utilities

import { print } from '../lib/print';

// Pretty print objects without undefined values
print('Result:', result);
print('Usage:', result.usage, { depth: 2 });

Using printFullStream

import { printFullStream } from '../lib/print-full-stream';

const result = streamText({ ... });
await printFullStream({ result }); // Colored output for text, tool calls, reasoning

Reusable Tools

The tools/ directory contains reusable tool definitions:

import { weatherTool } from '../tools/weather-tool';

const result = await generateText({
  model: openai('gpt-4o'),
  tools: { weather: weatherTool },
  prompt: 'What is the weather in San Francisco?',
});

Best Practices

  1. Keep examples focused: Each example should demonstrate one feature or use case

  2. Use descriptive prompts: Make it clear what the example is testing

  3. Handle errors gracefully: The run() wrapper handles this automatically

  4. Use realistic model IDs: Use actual model IDs that work with the provider

  5. Add comments for complex logic: Explain non-obvious code patterns

  6. Reuse tools when appropriate: Use weatherTool or create new reusable tools in tools/

turborepo

vercel

Turborepo monorepo build system guidance. Triggers on: turbo.json, task pipelines, dependsOn, caching, remote cache, the "turbo" CLI, --filter, --affected, CI optimization, environment variables, internal packages, monorepo structure/best practices, and boundaries. Use when user: configures tasks/workflows/pipelines, creates packages, sets up monorepo, shares code between apps, runs changed/affected packages, debugs cache, or has apps/packages directories.

12633

web-design-guidelines

vercel

Review UI code for Web Interface Guidelines compliance. Use when asked to "review my UI", "check accessibility", "audit design", "review UX", or "check my site against best practices".

8019

update-docs

vercel

This skill should be used when the user asks to "update documentation for my changes", "check docs for this PR", "what docs need updating", "sync docs with code", "scaffold docs for this feature", "document this feature", "review docs completeness", "add docs for this change", "what documentation is affected", "docs impact", or mentions "docs/", "docs/01-app", "docs/02-pages", "MDX", "documentation update", "API reference", ".mdx files". Provides guided workflow for updating Next.js documentation based on code changes.

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streamdown

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Implement, configure, and customize Streamdown — a streaming-optimized React Markdown renderer with syntax highlighting, Mermaid diagrams, math rendering, and CJK support. Use when working with Streamdown setup, configuration, plugins, styling, security, or integration with AI streaming (e.g., Vercel AI SDK). Triggers on: (1) Installing or setting up Streamdown, (2) Configuring plugins (code, mermaid, math, cjk), (3) Styling or theming Streamdown output, (4) Integrating with AI chat/streaming, (5) Configuring security, link safety, or custom HTML tags, (6) Using carets, static mode, or custom components, (7) Troubleshooting Tailwind, Shiki, or Vite issues.

3311

cache-components

vercel

Expert guidance for Next.js Cache Components and Partial Prerendering (PPR). **PROACTIVE ACTIVATION**: Use this skill automatically when working in Next.js projects that have `cacheComponents: true` in their next.config.ts/next.config.js. When this config is detected, proactively apply Cache Components patterns and best practices to all React Server Component implementations. **DETECTION**: At the start of a session in a Next.js project, check for `cacheComponents: true` in next.config. If enabled, this skill's patterns should guide all component authoring, data fetching, and caching decisions. **USE CASES**: Implementing 'use cache' directive, configuring cache lifetimes with cacheLife(), tagging cached data with cacheTag(), invalidating caches with updateTag()/revalidateTag(), optimizing static vs dynamic content boundaries, debugging cache issues, and reviewing Cache Component implementations.

418

ai-sdk

vercel

Answer questions about the AI SDK and help build AI-powered features. Use when developers: (1) Ask about AI SDK functions like generateText, streamText, ToolLoopAgent, embed, or tools, (2) Want to build AI agents, chatbots, RAG systems, or text generation features, (3) Have questions about AI providers (OpenAI, Anthropic, Google, etc.), streaming, tool calling, structured output, or embeddings, (4) Use React hooks like useChat or useCompletion. Triggers on: "AI SDK", "Vercel AI SDK", "generateText", "streamText", "add AI to my app", "build an agent", "tool calling", "structured output", "useChat".

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