| 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. |
| user-invocable | false |
| disable-model-invocation | true |
| 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:
