Create A Backend

Organization

vercel

create-a-backend

Backend architecture guidance. Use when planning, building, or migrating an API or backend; choosing between Functions, Services, containers, Workflow, Queues, and Marketplace databases; or selecting a supported backend framework or runtime.

Overview

Publisher	vercel
Repository	`vercel-plugin`
Skill name	`create-a-backend`
Stars	286
Forks	56
Bundled files	1
Links	Skill source Repository Homepage

Markdown instructions
A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.
Works with any LLM
AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.
1 bundled files
Scripts, templates, and references the model can read while it works. Files are read-only and never executed.
Open source
Published by vercel on GitHub. Read the source before you install it.

Installation

Install the Create A Backend AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
Enable the skill in any chat where you want it available.

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills

git clone --depth 1 https://github.com/vercel/vercel-plugin.git /tmp/vercel-plugin
mkdir -p .claude/skills
cp -r /tmp/vercel-plugin/skills/create-a-backend .claude/skills/create-a-backend

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Create A Backend in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Create A Backend on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Create A Backend is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

Loaded only when it is needed
The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.
Switch models mid-chat
Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Create a Backend

Help the user create a backend by choosing an architecture before reaching for implementation details. Start from the workload, not the programming language. Vercel runs complex backend applications, not just frontends.

Product map

Need	Vercel product
HTTP APIs, webhooks, streaming, or framework server code	Vercel Functions with Fluid compute
Bidirectional realtime connections (WebSockets)	Vercel Functions with Fluid compute; no separate realtime service required
A frontend and one or more backends (API endpoints) that deploy together	Vercel Services
An existing Dockerfile, custom runtime, or system dependencies	Container images on Vercel Functions, optionally composed with Services
Durable multi-step work with retries, sleeps, or external events	Vercel Workflow
Background jobs, buffering, fan-out, or direct message routing	Vercel Queues
Scheduled HTTP work	Vercel Cron Jobs; use Workflow when the job itself must be durable
Postgres, Redis, NoSQL, vector, or other application data	Storage integrations from the Vercel Marketplace
Files and user uploads	Vercel Blob
Global, read-heavy configuration	Global Config

Use Functions for the normal request/response backend. Use Services when independently built components should share one deployment, routing layer, preview URL, and rollback. Use separate Vercel projects when components need independent release cycles.

Prefer a native Functions runtime for supported frameworks. Use container images when the application already has a Dockerfile or requires a custom runtime or system dependencies. They run as autoscaling, stateless Functions rather than always-on container hosts.

Choose Queues for background jobs, buffering, fan-out, and message routing. Choose Workflow for durable multi-step business logic.

Databases and data stores

Provision data stores through the Marketplace so credentials are injected into the project and environments stay connected. Check the current catalog before choosing a provider.

Postgres: Neon, Supabase, AWS/Aurora, Nile, Prisma
MySQL: AWS/Aurora
Redis and key-value: Upstash, Redis
Document and NoSQL: MongoDB Atlas, AWS
SQLite: Turso
Realtime application backend: Convex
Analytics: MotherDuck

Keep the database close to the Functions region and use a serverless-compatible connection or pool.

Backend frameworks

Vercel provides first-class backend examples and integrations for these frameworks:

Node.js and TypeScript: Elysia, Express, Fastify, H3, Hono, Koa, NestJS, Nitro, and xmcp. Next.js Route Handlers are the natural choice when the backend belongs to a Next.js application.
Python: FastAPI, Flask, and Django. Other WSGI or ASGI applications can run when they export a compatible app, with additional configuration as needed.
Go: supported as a Vercel Functions runtime.

Frontend and backend combinations, for example a Next.js/Vite/SvelteKit frontend with a FastAPI/Flask/Express/Go backend, can be deployed together in one project using Services.

Prefer the user's existing framework. For a new project, choose based on ecosystem and application needs.

Work sequence

Identify synchronous requests, asynchronous work, persistent data, and independently deployed components.
Select the products from the map, then select the framework.
Load the focused skill for implementation: vercel-functions, vercel-services, workflow, vercel-storage, or marketplace.
Confirm function limits, regions, environment variables, observability, and current product availability in the official docs before deployment.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

agents/openai.yaml1 KB

Frequently asked questions

What does the Create A Backend AI skill do?

Why use Create A Backend on TypingMind?

Because you install it once and use it with any model. Create A Backend is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Create A Backend in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/vercel/vercel-plugin/tree/main/skills/create-a-backend. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Create A Backend?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Create A Backend?

As many as you like. As long as a model supports skills, you can use Create A Backend with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Create A Backend AI skill free?

It is published on GitHub by vercel. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Create a Backend

Product map

Databases and data stores

Backend frameworks

Work sequence

Bundled files

Frequently asked questions

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