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vercel-ai skill

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/toolchains/platforms/deployment/vercel-ai

This skill guides building AI-powered apps on Vercel by configuring AI SDK, Gateway, and Agent workflows, with secure key management and monitoring.

npx playbooks add skill bobmatnyc/claude-mpm-skills --skill vercel-ai

Review the files below or copy the command above to add this skill to your agents.

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SKILL.md

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---
name: vercel-ai
description: Vercel AI platform guidance covering AI SDK, AI Gateway, Vercel Agent, and MCP. Use when building AI-powered or agentic workloads on Vercel.
progressive_disclosure:
  entry_point:
    summary: "Vercel AI platform guidance covering AI SDK, AI Gateway, Vercel Agent, and MCP. Use when building AI-powered or agentic workloads on Vercel."
    when_to_use: "When working with vercel-ai or related functionality."
    quick_start: "1. Review the core concepts below. 2. Apply patterns to your use case. 3. Follow best practices for implementation."
---
# Vercel AI Skill

---
progressive_disclosure:
  entry_point:
    summary: "Vercel AI platform: AI SDK, AI Gateway, Vercel Agent, agent integrations, and MCP."
    when_to_use:
      - "When building AI-powered apps on Vercel"
      - "When routing model traffic through AI Gateway"
      - "When using Vercel Agent or MCP workflows"
    quick_start:
      - "Choose AI SDK, AI Gateway, or Agent"
      - "Configure models and routing"
      - "Secure keys and environment variables"
      - "Deploy and monitor usage"
  token_estimate:
    entry: 90-110
    full: 3600-4700
---

## Overview

Vercel AI capabilities include SDKs and services for building AI-enabled applications, model routing, and agent workflows.

## AI SDK

- Use the AI SDK to build AI-driven app features.

## AI Gateway

- Route model traffic through AI Gateway.
- Apply usage controls and monitoring.

## Vercel Agent

- Build and operate agentic workflows.
- Connect agent integrations as needed.

## MCP

- Use MCP integrations for AI tooling and workflows.

## Complementary Skills

When using this skill, consider these related skills (if deployed):

- **vercel-functions-runtime**: Functions and Edge execution for AI workloads.
- **vercel-storage-data**: Data stores for embeddings and artifacts.
- **vercel-observability**: Usage monitoring and debugging.

*Note: Complementary skills are optional. This skill is fully functional without them.*

## Resources

**Vercel Docs**:
- AI on Vercel: https://vercel.com/docs/ai
- AI Gateway: https://vercel.com/docs/ai-gateway
- AI SDK: https://vercel.com/docs/ai-sdk
- Vercel Agent: https://vercel.com/docs/agent
- Agent integrations: https://vercel.com/docs/agent-integrations
- MCP: https://vercel.com/docs/mcp

Overview

This skill provides practical guidance for building AI-powered and agentic workloads on Vercel, covering the AI SDK, AI Gateway, Vercel Agent, and MCP. It explains core capabilities, configuration flow, and when to route model traffic or deploy agents, so you can move from prototype to production with confidence.

How this skill works

The skill inspects Vercel platform features and maps them to common AI architecture needs: embedding and inference via the AI SDK, controlled model routing through AI Gateway, agent orchestration with Vercel Agent, and tooling/workflow integration via MCP. It highlights configuration steps, security considerations, and monitoring points to ensure safe model usage and operational visibility.

When to use it

Building web apps or APIs that require model inference at the edge or serverless functions
Routing requests to multiple models or providers and enforcing usage controls
Designing agentic workflows that coordinate tools, triggers, and external integrations
Integrating developer tooling, indexing, or observability into AI pipelines via MCP
Prototyping a full AI stack on Vercel before scaling to production

Best practices

Choose the AI SDK for local inference patterns and consistent client APIs across runtimes
Use AI Gateway to centralize model routing, rate limits, and access controls
Store secrets and API keys in environment variables and never hard-code credentials
Design agents with clear intent, sandboxed tool access, and explicit failure handling
Monitor usage and latency with observability tooling to detect drift and cost spikes

Example use cases

A customer support chat widget that routes to smaller models for FAQs and larger models for complex escalations via AI Gateway
An email triage agent that uses Vercel Agent to invoke external APIs and update CRM records
A documentation search app using the AI SDK to compute embeddings stored in Vercel storage and serving semantic search
A CI workflow that validates model responses and logs metrics to MCP integrations for compliance

FAQ

Can I use any model provider with Vercel AI Gateway?

Yes — AI Gateway is designed to route traffic to multiple providers and models. Configure providers and routes in the gateway settings and manage access centrally.

When should I prefer Vercel Agent over direct SDK calls?

Use Vercel Agent for agentic tasks that require orchestration, tool use, or long-running workflows. Use direct SDK calls for simple synchronous inference inside your app.