develop-ai-functions-example

vercel/ai · updated Apr 8, 2026

MDX-style export adds YAML metadata + attribution linking explainx.ai and this canonical listing URL.

$npx skills add https://github.com/vercel/ai --skill develop-ai-functions-example
0 commentsdiscussion
summary

Development and validation scripts for AI SDK functions across multiple providers and capabilities.

  • Organized by AI SDK function category (text generation, streaming, structured output, embeddings, image generation, speech, transcription, reranking, and agents)
  • File naming convention maps provider and feature combinations (e.g., openai-tool-call.ts , amazon-bedrock-anthropic-cache-control.ts ) for quick identification
  • Includes shared utility helpers for error handling, environment lo
skill.md

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/:

Directory Purpose
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

Pattern Example Description
{provider}.ts openai.ts Basic provider usage
{provider}-{feature}.ts openai-tool-call.ts Specific feature
{provider}-{sub-provider}.ts amazon-bedrock-anthropic.ts Provider with sub-provider
{provider}-{sub-provider}-{feature}.ts google-vertex-anthropic-cache-control.ts Sub-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:

File Purpose
run.ts Error-handling wrapper with .env loading
print.ts Clean object printing (removes undefined values)
print-full-stream.ts Colored streaming output for tool calls, reasoning, text
save-raw-chunks.ts Save streaming chunks for test fixtures
present-image.ts Display images in terminal
save-audio.ts Save audio files to disk

Using print utilities

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

// Pretty print objects without undefined values
print('Result:', result);
print
how to use develop-ai-functions-example

How to use develop-ai-functions-example on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add develop-ai-functions-example
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/vercel/ai --skill develop-ai-functions-example

The skills CLI fetches develop-ai-functions-example from GitHub repository vercel/ai and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/develop-ai-functions-example

Reload or restart Cursor to activate develop-ai-functions-example. Access the skill through slash commands (e.g., /develop-ai-functions-example) or your agent's skill management interface.

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

List & Monetize Your Skill

Submit your Claude Code skill and start earning

GET_STARTED →

Use Cases

Task Automation & Efficiency

Automate repetitive workflows and reduce manual effort

Example

Generate reports, summarize documents, draft communications

Save 3-5 hours per week on routine tasks

Knowledge Enhancement

Learn new skills, understand complex topics, get expert guidance

Example

Explain concepts, provide examples, suggest learning resources

Accelerate learning and skill development by 2x

Quality Improvement

Enhance output quality through reviews, suggestions, and refinements

Example

Review drafts, suggest improvements, catch errors

Improve work quality by 30-40% with less effort

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client with skill support
  • Clear understanding of task or problem to solve
  • Willingness to iterate and refine outputs

Time Estimate

15-45 minutes depending on use case complexity

Installation Steps

  1. 1.Install skill using provided installation command
  2. 2.Test with simple use case relevant to your work
  3. 3.Evaluate output quality and relevance
  4. 4.Iterate on prompts to improve results
  5. 5.Integrate into regular workflow if valuable

Common Pitfalls

  • Expecting perfect results without iteration
  • Not providing enough context in prompts
  • Using skill for tasks outside its intended scope
  • Accepting outputs without review and validation

Best Practices

✓ Do

  • +Start with clear, specific prompts
  • +Provide relevant context and constraints
  • +Review and refine all outputs before using
  • +Iterate to improve output quality
  • +Document successful prompt patterns

✗ Don't

  • Don't use without understanding skill limitations
  • Don't skip validation of outputs
  • Don't share sensitive information in prompts
  • Don't expect skill to replace human judgment

💡 Pro Tips

  • Be specific about desired format and style
  • Ask for multiple options to choose from
  • Request explanations to understand reasoning
  • Combine AI efficiency with human expertise

When to Use This

✓ Use When

Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.

✗ Avoid When

Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.

Learning Path

  1. 1Familiarize yourself with skill capabilities and limitations
  2. 2Start with low-risk, non-critical tasks
  3. 3Progress to more complex and valuable use cases
  4. 4Build expertise through regular use and experimentation

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.
general reviews

Ratings

4.552 reviews
  • Alexander Haddad· Dec 28, 2024

    Useful defaults in develop-ai-functions-example — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Camila Farah· Dec 24, 2024

    develop-ai-functions-example is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Dhruvi Jain· Dec 16, 2024

    develop-ai-functions-example has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Isabella Chawla· Dec 12, 2024

    develop-ai-functions-example reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Alexander Nasser· Dec 8, 2024

    Solid pick for teams standardizing on skills: develop-ai-functions-example is focused, and the summary matches what you get after install.

  • Kofi Li· Dec 8, 2024

    Registry listing for develop-ai-functions-example matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Amelia Khanna· Nov 27, 2024

    Registry listing for develop-ai-functions-example matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Kaira White· Nov 27, 2024

    Solid pick for teams standardizing on skills: develop-ai-functions-example is focused, and the summary matches what you get after install.

  • Alexander Lopez· Nov 19, 2024

    I recommend develop-ai-functions-example for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Oshnikdeep· Nov 7, 2024

    Keeps context tight: develop-ai-functions-example is the kind of skill you can hand to a new teammate without a long onboarding doc.

showing 1-10 of 52

1 / 6