Best AI Prompts for Next.js Application Setup with Claude Code

Quick Answer

We provide a curated set of AI prompts to automate Next.js project setup using Claude Code, eliminating hours of manual boilerplate configuration. These prompts handle everything from initial scaffolding with TypeScript and Tailwind to complex integrations like Supabase and Prisma. This approach transforms the tedious setup phase into a streamlined, automated workflow.

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Revolutionizing Next.js Development with AI-Powered Automation

Have you ever spent an entire afternoon just to set up a new Next.js project? You know the drill: npx create-next-app, then configure TypeScript, integrate ESLint and Prettier for code quality, set up Husky for commit hooks, and finally, wrestle with Tailwind CSS for styling. Just when you think you’re ready to build, you hit the next wall: integrating a database like Supabase with Prisma and wiring up a robust authentication solution like Auth.js or Clerk. This “boilerplate fatigue” is a notorious productivity killer, stealing precious hours from what truly matters—building unique application features. It’s a complex, repetitive chore that even seasoned developers dread.

Enter Claude Code, a new class of AI assistant that fundamentally changes the game. Unlike standard chatbots that only generate text, Claude Code acts as an autonomous agent directly within your terminal. It can read and write files, execute commands, and orchestrate multi-step workflows on your local machine. This isn’t just about getting code suggestions; it’s about delegating the entire setup process. You can literally watch it run the commands, install packages, and configure files for you, turning a manual, error-prone process into a guided, automated one.

The secret lies in the power of strategic prompting. The right prompt transforms this terminal assistant from a novelty into a production-ready scaffolding tool. By providing clear, context-rich instructions, you can automate the entire setup, turning hours of tedious configuration into a single, guided command execution. This guide provides a library of copy-paste-ready prompts designed to do exactly that. We’ll walk you through everything from initial project creation to complex, production-ready integrations, giving you a powerful toolkit to accelerate your Next.js development workflow.

Section 1: The Foundation - Automating the Initial Next.js Boilerplate

You’ve just greenlit a new project. The excitement is palpable, but then you hit the first wall: the boilerplate. Manually running create-next-app, answering a dozen prompts, and then spending the next hour configuring ESLint, Prettier, path aliases, and Git hooks feels like paying a tax before you can even write your first line of feature code. It’s repetitive, error-prone, and frankly, a momentum killer. What if you could delegate this entire setup phase to an AI agent that not only runs the commands but also configures the files to your exact specifications, all from a single, powerful instruction?

This is the core promise of leveraging an AI terminal agent like Claude Code for your Next.js foundation. By crafting precise, multi-step prompts, you transform a manual, hour-long chore into a guided, automated workflow that takes minutes. This section provides a library of battle-tested prompts designed to establish a rock-solid, production-ready foundation for your next project. We’ll cover everything from generating the initial app with the right flags to enforcing code quality standards and setting up a professional Git workflow from the very first commit.

Prompting for the Perfect create-next-app

The first step is always the same, but the flags you choose have long-term consequences. A generic npx create-next-app@latest leaves you with decisions to make and potential inconsistencies. Instead, you can instruct Claude Code to run the command with a specific configuration, and then immediately clean up the boilerplate to give you a true “blank slate.”

This prompt is designed to eliminate the default Next.js landing page, which often clutters your initial Git history with irrelevant template code. It ensures your project starts with a clean page.tsx and a minimal layout.tsx, ready for your design system.

The Prompt:

“Run the command npx create-next-app@latest in the current directory. Use the following exact flags: --typescript, --tailwind, --eslint, --app, --src-dir, --import-alias "@/*", and --use-npm. Do not prompt me for these choices; execute them directly. After the installation is complete, navigate to src/app/page.tsx and replace its entire content with a minimal ‘Hello, World’ component. Then, go to src/app/layout.tsx and remove all default metadata and boilerplate styles, leaving only the essential html and body tag structure.”

Why This Works:

• Autonomy: The prompt explicitly tells the AI what to do and how to do it, removing ambiguity.
• Flags Explained: --typescript and --tailwind are non-negotiable for modern Next.js development. --app selects the App Router, the current standard. --src-dir keeps your project root clean by placing source code in a src folder. --import-alias "@/*" sets up a convenience for imports that we’ll formalize later.
• Immediate Cleanup: The crucial second step prevents “template debt.” You start with code you wrote (or at least, code you own), not boilerplate you’ll delete anyway. This is a small but significant step for a clean project history.

Configuring Code Quality Standards from Day One

Consistency is the hallmark of a professional codebase. Waiting to configure linting and formatting until later invites style drift and messy pull requests. The best time to set these standards is immediately after the project is created. This prompt sequence creates and configures your code quality files, ensuring every developer on the team (including your future self) adheres to the same rules.

The Prompt Sequence:

“First, create three files in the project root: .eslintrc.json, .prettierrc, and .editorconfig.

In .eslintrc.json, configure it to extend next/core-web-vitals and @typescript-eslint/recommended. Add a rule to enforce import order and another to flag any console.log statements as an error.

In .prettierrc, set the following rules: semi: true, singleQuote: true, tabWidth: 2, and trailingComma: "es5".

In .editorconfig, ensure it sets root = true, charset = utf-8, indent_style = space, and indent_size = 2 for all files.”

Why This Works:

• Proactive Enforcement: By defining rules at the project’s inception, you prevent bad habits from taking root.
• Specificity: The prompt doesn’t just ask for “standard” configs; it specifies rules that matter in a real-world Next.js project, like banning console.log in production code and enforcing a clean import order.
• Cross-Editor Consistency: The .editorconfig file is an often-overlooked but critical piece of the puzzle. It ensures that regardless of whether a team member uses VS Code, WebStorm, or Neovim, basic formatting like indentation remains consistent.

Golden Nugget: Your ESLint and Prettier configurations are a living document. As your project grows, you might add plugins like eslint-plugin-jsx-a11y for accessibility. A powerful move is to create a prompt that updates these files, for example: “Add the eslint-plugin-jsx-a11y plugin to your ESLint config and enable the ‘no-autofocus’ rule.” This treats your code quality standards as a project feature that can be iterated on with AI assistance.

Setting Up Path Aliases and Environment Variables

Navigating relative import paths like ../../../../lib/utils is a common source of friction and brittle code. Path aliases solve this by creating clean, absolute import paths. Similarly, managing environment variables without a template is a recipe for disaster when it comes to deployment and onboarding new developers.

The Prompt:

“Edit the tsconfig.json file. In the compilerOptions object, add a baseUrl set to "." and a paths object that maps "@/*" to ["./src/*"]. Next, create a .env.example file in the root directory. Inside this file, add placeholders for a DATABASE_URL (e.g., DATABASE_URL="postgresql://user:password@localhost:5432/db_name") and a NEXT_PUBLIC_APP_URL (e.g., NEXT_PUBLIC_APP_URL="http://localhost:3000").”

Why This Works:

• Developer Experience (DX): import { Button } from '@/components/ui/button' is significantly cleaner and easier to refactor than its relative counterpart. This prompt formalizes the alias we set up in the initial create-next-app command.
• Onboarding & Security: The .env.example file is a non-negotiable best practice. It documents all required environment variables without exposing actual secrets. A new developer can simply copy .env.example to .env.local and fill in their own values, drastically reducing setup friction. This is a crucial step for building trust and ensuring your project is deploy-ready from the start.

Initializing Git with Husky & Commitizen

A clean Git history is not a luxury; it’s a necessity for effective collaboration and debugging. Enforcing conventional commits from the very first commit ensures your history is readable, searchable, and automatically generates changelogs. This prompt automates the setup of Husky for Git hooks and Commitizen for standardized commit messages.

The Prompt:

“Initialize a Git repository in the current directory. Then, install Husky and Commitizen as development dependencies using npm. Configure Husky to run on git commit and create a commit-msg hook. This hook should execute npx --no-install commitlint --edit $1 to lint the commit message. Finally, configure Commitizen to use the cz-conventional-changelog adapter by creating a .czrc file.”

Why This Works:

• Automated Professionalism: This prompt takes a multi-step, often confusing process and condenses it into a single instruction. It sets up a system that prevents messy commit messages like “fixed stuff” before they can even happen.
• Quality Gate: The commit-msg hook acts as a quality gate, ensuring that every message adheres to the Conventional Commits specification. This discipline pays massive dividends as the project scales, enabling automated versioning and clearer communication across the team.
• Frictionless Commits: Once configured, developers can simply run git add . and git commit, and Commitizen will guide them through an interactive prompt to build a perfectly formatted commit message. This lowers the barrier to following best practices.

Section 2: Integrating UI Libraries and Design Systems Seamlessly

Once your Next.js project is live, the real work begins: building the user interface. This is where most developers get bogged down in configuration hell. Installing UI libraries, configuring design tokens, and ensuring consistent styling across components can easily consume an entire day. But with a well-crafted prompt, you can instruct Claude Code to handle this entire workflow autonomously, from installation to implementation.

Think of this as having a senior developer on standby who never gets tired of running npm install or editing config files. The key is to be explicit about the desired end state, including file paths, configuration values, and even your preferred color palette. Let’s break down how to automate the most common UI integration tasks.

Installing and Configuring shadcn/ui

shadcn/ui has become the de facto standard for Next.js applications that need a beautiful, accessible, and highly customizable component library. However, its initial setup is a multi-step process that can be intimidating. You need to initialize the CLI, configure the components.json file, and then add your first component. Here’s how to automate it all.

First, you need to initialize the library with your project’s specific preferences. This prompt tells Claude exactly what to do, including your color scheme and alias preferences.

Master Prompt for Initial Setup:

“Run the command npx shadcn-ui@latest init to set up shadcn/ui. When prompted during execution, choose the following options:

• Style: New York
• Base Color: Slate (or your preference, e.g., Zinc, Neutral)
• CSS Variables: Yes
• Global CSS: src/app/globals.css (adjust if using the app root)
• Import alias: @/components

After the command completes, verify that a components.json file has been created in your project root with these settings.”

This prompt is powerful because it doesn’t just ask for the library; it guides the AI through the interactive CLI, ensuring the configuration is locked in from the start. The components.json file becomes the single source of truth for all future component additions.

Once initialized, adding new components is trivial. You can use a follow-up prompt like: “Add the button, card, and input components using npx shadcn-ui@latest add.” Claude will execute the command and place the generated component files in your designated components folder, fully typed and ready to use.

Automating Tailwind CSS Plugin Installation

A default Tailwind setup is powerful, but its true potential is unlocked with plugins. Plugins like @tailwindcss/typography for rich text content or @tailwindcss/forms for consistent form styling are essential for production apps. Manually installing and configuring them involves updating both package.json and tailwind.config.ts. This is a perfect task for automation.

Prompt for Plugin Installation and Configuration:

“Install the following Tailwind CSS plugins as dev dependencies: @tailwindcss/typography, @tailwindcss/forms, and @tailwindcss/aspect-ratio.

After installation, open tailwind.config.ts and update the plugins array to include these new plugins. The final configuration should look like this:

import type { Config } from "tailwindcss"

const config: Config = {
  // ... rest of your config
  plugins: [
    require("@tailwindcss/typography"),
    require("@tailwindcss/forms"),
    require("@tailwindcss/aspect-ratio"),
  ],
}
export default config
```"

This prompt is effective because it provides the exact target code, eliminating any ambiguity. The AI knows to install the packages and then surgically edit the configuration file without disturbing other settings. This ensures your project is immediately ready to use utilities like prose, form-input, and aspect-video.

Golden Nugget: When prompting for config file changes, always provide the final, desired state of the relevant code block. This technique, known as “state-driven prompting,” drastically reduces errors. Instead of asking the AI to “add plugins,” you’re showing it the correct outcome, which it can then achieve by writing the file from scratch or making a precise edit.

Creating a Reusable Layout Component

A consistent UI starts with a solid layout. Your root layout (app/layout.tsx) should define the global structure, including navigation, footers, and any context providers. Manually creating this file and the utility functions it often requires (like a cn function for merging Tailwind classes) is repetitive. Let’s automate it.

Prompt for a Production-Ready Root Layout:

“Create a new file at src/components/layout/root-layout.tsx. This should be a Server Component that accepts children as a prop.

Inside this file:

1. Create a cn utility function in a separate file at src/lib/utils.ts. This function should use clsx and tailwind-merge to reliably merge Tailwind classes. Install these packages if they are not present.
2. In root-layout.tsx, build a semantic HTML structure with a <header>, <main>, and <footer>.
3. The <header> should contain a simple navigation bar with a logo link and two placeholder links for ‘Features’ and ‘Contact’.
4. The <footer> should have a centered paragraph with the current year and your project name.
5. Use the cn function to conditionally apply Tailwind classes to the header and footer, for example, cn("sticky top-0 border-b", className) for the header.”

By breaking the request into these distinct steps, you guide the AI to build a robust, reusable foundation. The cn utility is a critical piece of modern Tailwind development, preventing class conflicts when you need to override styles. Automating its creation ensures you start with best practices baked in.

Integrating a Headless UI Library (Radix UI / Headless UI)

Sometimes, you need powerful, accessible, unstyled components to build your own design system on top of. This is where libraries like Radix UI or Headless UI shine. Their setup involves installing individual packages for each component (e.g., @radix-ui/react-dialog) and wrapping your application in provider components. This workflow is ripe for AI automation.

Prompt for Headless UI Integration:

“We are adding Radix UI to this project. First, install the core primitives by running npm install @radix-ui/react-dialog @radix-ui/react-dropdown-menu @radix-ui/react-tooltip.

Next, create a new provider component at src/components/providers/radix-provider.tsx. This component should not render any UI itself; it will be used to house any necessary Radix configuration.

Finally, update src/app/layout.tsx to import and render the <RadixProvider> component, wrapping the {children} prop. This ensures all Radix components used within the app will have the correct context.”

This prompt demonstrates an expert understanding of how these libraries work. It separates the installation step from the architectural setup, ensuring the project is organized correctly. By creating a dedicated provider component, you future-proof your app for easy addition of more context providers (like a theme or state management provider) later on.

Section 3: Backend Power-Up: Database and ORM Automation

Setting up a database and its accompanying ORM is often where the “magic” of a new project grinds to a halt. You’re switching between package managers, schema files, and environment variables, a process that’s ripe for small, time-consuming errors. This is precisely where an autonomous AI agent like Claude Code shifts from a convenience to a necessity. By crafting a single, detailed prompt, you can instruct the agent to handle the entire workflow, from installation to final configuration, ensuring a production-ready setup in minutes instead of an hour.

Automating Prisma with PostgreSQL

The first step in building a robust backend is defining your data model. Prisma excels here with its declarative schema file, but getting it configured correctly with a PostgreSQL provider can involve several manual steps. You can automate this entire process. Instead of manually installing packages and creating files, you can give the agent a single, comprehensive instruction.

Here is a powerful prompt to automate the Prisma and PostgreSQL setup:

“Install Prisma as a dev dependency using npm install prisma --save-dev and the PostgreSQL client with npm install @prisma/client. Then, initialize Prisma with npx prisma init --datasource-provider postgresql. After initialization, open the prisma/schema.prisma file. Update the generator block to include previewFeatures = ["fullTextSearch"] for future-proofing. Then, replace the default User model with a more robust one: a User model with an auto-incrementing id (Int, @id), email (String, @unique), name (String?), createdAt (DateTime, @default(now())), and a posts relation to a Post model. The Post model should have an id, title, content (String), published (Boolean, @default(false)), authorId (Int), and an author relation back to User.”

This single command chain handles the installation, initialization, and schema definition. The inclusion of previewFeatures and a relational model demonstrates an understanding of real-world application needs, not just a basic setup. It’s a perfect example of how you can encode best practices directly into your prompt.

One-Prompt Migrations: Generating and Pushing Schema

Once your schema.prisma file is defined, the next step is to make it real. This involves two distinct commands: npx prisma generate, which creates the typed database client, and npx prisma db push, which synchronizes your schema with the database without generating a migration file. This is perfect for rapid development. You can chain these commands in a single prompt to create a seamless workflow.

Use this prompt to execute the migration workflow:

“Execute the following commands in sequence to sync our database. First, run npx prisma generate to update the Prisma Client types based on our current schema. Second, run npx prisma db push to push the schema changes directly to our connected PostgreSQL database. Confirm both commands complete successfully and report the output.”

This prompt treats the AI agent like a senior developer you’ve delegated a task to. You state the goal (sync the database) and the required steps, and the agent executes them. This eliminates the context switching and cognitive load of remembering and running these commands manually, keeping you in the flow state of building your application.

The Supabase Alternative: A Direct Path to Production

While Prisma is a fantastic ORM, some developers prefer the all-in-one backend solution offered by Supabase. The workflow here is different but equally automatable. Instead of an ORM schema, you’re setting up a direct client and managing your database via Supabase’s UI or SQL editor. The AI can still handle the local environment setup flawlessly.

For teams opting for this path, this prompt streamlines the integration:

“Install the Supabase JavaScript client library by running npm install @supabase/supabase-js. Next, create a new file at lib/supabase/client.ts. Inside this file, create and export a function named createClient that initializes and returns a Supabase client instance. This instance must be configured with your project’s URL and anon key, which should be read from process.env.NEXT_PUBLIC_SUPABASE_URL and process.env.NEXT_PUBLIC_SUPABASE_ANON_KEY. Finally, add these two environment variables to your .env.local file with placeholder values.”

This prompt demonstrates the AI’s versatility. It understands the specific file structure (lib/supabase/client.ts is a common convention) and the security best practice of using environment variables for sensitive keys. It automates the boilerplate, allowing you to focus on defining your database tables in the Supabase dashboard.

Golden Nugget: When prompting for environment variables, always ask the AI to use the NEXT_PUBLIC_ prefix only for variables that must be exposed to the browser. For server-side secrets like a database URL or a service key, instruct the AI to omit the prefix. This simple instruction enforces a critical security boundary from day one.

Production-Ready Data Fetching: The Prisma Singleton

A common pitfall for developers new to Next.js and Prisma is the “hot-reloading” issue. In development, if you create a new Prisma Client instance on every file reload, you can quickly exhaust your database connections, leading to errors. The solution is a “singleton” pattern: ensure only one instance of the client exists in memory.

This is a perfect task for the AI, as it requires writing a small but crucial piece of utility code. You can trust the agent to implement this robust pattern for you.

Instruct the agent with this prompt to create the singleton utility:

“Create a new file at lib/db.ts. In this file, implement a Prisma Client singleton. The logic should check if a global prisma variable already exists. If it does, it should be used. If not, a new PrismaClient should be instantiated and assigned to the global variable. Finally, export this prisma instance. This pattern is essential to prevent excessive database connections during development.”

By offloading this task, you’re not just saving time; you’re ensuring your project is built on a foundation that will scale and perform reliably. This utility is a hallmark of a mature Next.js application, and having the AI generate it correctly from the start is a significant win.

Section 4: Implementing Secure Authentication in Minutes

Authentication is the gatekeeper of your application. It’s a feature that every developer has built a dozen times, yet it remains a tedious, error-prone process of wiring up routes, managing session states, and protecting endpoints. What if you could delegate this entire workflow to an AI agent that not only writes the code but also configures the environment correctly? This is where the power of prompting transforms from a convenience into a strategic advantage.

By using a structured prompt sequence with an autonomous agent like Claude Code, you can implement a robust, production-ready authentication system—including OAuth, session management, and route protection—in under five minutes. This isn’t just about speed; it’s about offloading the mental overhead of remembering boilerplate so you can focus on your unique application logic.

Integrating Auth.js (NextAuth.js) v5

The first step is establishing the core authentication infrastructure. Auth.js v5 (the successor to NextAuth.js) offers a powerful, flexible foundation. Our prompt will instruct the AI to handle the entire setup: installing the necessary packages, creating the core configuration file, setting up the dynamic API route, and ensuring the session context is available throughout your application.

The Prompt:

“Install next-auth and its Prisma adapter (@next-auth/prisma-adapter) as dependencies. Then, perform the following setup:

1. Create an auth.ts file in the project root. Inside, configure NextAuth using the PrismaAdapter and a CredentialsProvider. For now, just set up the basic structure with a placeholder authorize function and a randomly generated NEXTAUTH_SECRET in the NEXTAUTH_SECRET variable.
2. Create the dynamic API route handler at app/api/auth/[...nextauth]/route.ts. This file should import and export the GET and POST handlers from the auth.ts configuration.
3. Create a SessionProvider.tsx component in app/providers.tsx. This component should be a client component ("use client") that uses the SessionProvider from next-auth/react to wrap its children.
4. Finally, update the root app/layout.tsx to import and wrap the children with your new SessionProvider.”

This single, comprehensive instruction automates the foundational architecture. The AI will correctly handle the App Router’s convention for API routes and the necessary client-side context provider, ensuring your app is ready for the next step.

Configuring Credentials and OAuth Providers

With the skeleton in place, it’s time to add real-world login methods. A modern app needs to support both traditional email/password logins and the frictionless experience of social sign-in. This prompt extends the auth.ts file, adding the logic for a credentials provider and a social provider like Google, while also managing the sensitive API keys.

The Prompt:

“Now, update the auth.ts file to implement two authentication providers:

1. Credentials Provider: Configure it to ask for ‘email’ and ‘password’. In the authorize function, add a placeholder logic that checks if the email is ‘[email protected]’ and the password is ‘password123’. In a real app, this is where you’d query your database.
2. Google Provider: Add the Google provider from next-auth/providers. It will need a clientId and clientSecret.
3. Environment Variables: In your .env file, add placeholders for these secrets: GOOGLE_CLIENT_ID, GOOGLE_CLIENT_SECRET, and a strong NEXTAUTH_SECRET (generate a random 32-character string). Add a comment above each explaining where to find the credentials (e.g., ‘Get from Google Cloud Console’).”

This prompt demonstrates a key principle of effective AI delegation: it combines code modification with environment configuration. The AI becomes a full-stack assistant, understanding that a provider isn’t just a few lines of code but a system of secure, correctly-named environment variables.

Golden Nugget: When setting up OAuth providers like Google, a common pitfall is misconfiguring the “Authorized redirect URIs” in the provider’s dashboard. The AI will set up the correct route (/api/auth/callback/google), but you must manually ensure this exact URI is whitelisted in your Google Cloud Console project. This is a security-critical step that prevents authentication failures and protects against malicious redirect attacks.

Protecting Routes with Middleware

Once users can log in, you need to control what they can access. Next.js Middleware is the most efficient way to intercept requests and enforce authentication rules before a page even renders. This prompt creates a powerful, declarative rule set that guards your protected routes.

The Prompt:

“Create a middleware.ts file in the project root. Configure it to protect all routes that start with /dashboard. If a user is not authenticated, they should be automatically redirected to the /login page. Exclude any public-facing routes like /, /login, and /api/auth from this check.”

This is a perfect example of declarative security. Instead of manually adding if (!session) checks inside every server component or API route, you define the rule once at the edge. The AI generates the correct matcher configuration, ensuring your application’s security is centralized and robust.

Building a Dynamic User Menu and Auth Buttons

The final piece is the user interface. Your authentication flow is useless if users can’t interact with it. This prompt generates a set of client-side components that are fully integrated with the useSession hook, automatically reflecting the user’s authentication state.

The Prompt:

“Generate the following client-side components in a new components/auth directory:

1. SignInButton.tsx: A button that, when clicked, calls signIn from next-auth/react. It should only be visible when there is no active session.
2. SignOutButton.tsx: A button that calls signOut and redirects to the homepage. It should only be visible when a user is authenticated.
3. UserNav.tsx: A dropdown menu component that displays the user’s name and email from the session. It should contain the SignOutButton inside it. This component should only render when the session status is authenticated.”

This prompt chain builds the complete UI loop. The AI will generate components that are not just static UI elements but are “live” components connected to your application’s authentication state. The result is a seamless, professional user experience that required zero manual state management wiring from you.

Section 5: Advanced Workflows: Testing, CI/CD, and Deployment

You’ve built the application, but how do you ensure it doesn’t break? And more importantly, how do you ship it to production without spending your weekend manually configuring servers? This is where we transition from building features to building a professional, scalable software product. Automating your quality assurance and deployment pipelines is a non-negotiable step for any serious project.

Setting Up a Bulletproof Testing Environment with Vitest

Testing is often the first thing to be skipped under pressure, but it’s your best defense against regression bugs. A solid testing setup provides the confidence to refactor and add features fearlessly. The key is to make the setup frictionless.

To configure a TDD (Test-Driven Development) environment from scratch, you need a prompt that orchestrates multiple steps: installing dependencies, creating configuration files, and generating a meaningful test example.

The Prompt:

“Guide me through setting up Vitest and React Testing Library for a Next.js 14 project using the App Router.

1. Install the necessary packages as dev dependencies: vitest, @testing-library/react, @testing-library/jest-dom, @testing-library/user-event, and jsdom.
2. Create a vitest.config.ts file in the project root. Configure it to use jsdom as the test environment and set up an alias for @/ to resolve to the ./src/ directory.
3. Create a setupTests.ts file and import @testing-library/jest-dom/vitest.
4. Generate a sample test file for a hypothetical Button component located at src/components/ui/Button.tsx. The component should accept children and an onClick prop. The test should verify that the button renders with the correct text and that the onClick handler is called when clicked.”

Why This Works: This prompt demonstrates a deep understanding of the testing ecosystem. It doesn’t just ask for a config file; it demands the complete environment. By specifying jsdom, you’re telling the AI you need a simulated browser environment, which is critical for testing React components. The alias configuration is a pro-move that prevents brittle import paths.

Golden Nugget: A common pitfall is forgetting to configure path aliases in your test runner. If your app uses @/components/... but your tests can’t resolve that, they’ll fail before they even run. Always explicitly ask the AI to configure aliases in your vitest.config.ts to save hours of debugging.

Generating a Production-Ready Dockerfile

Containerizing your application is the gold standard for consistent deployments. A naive Dockerfile can work, but a multi-stage, cache-optimized one can reduce your production image size by over 60% and cut deployment times in half.

The Prompt:

“Write a multi-stage Dockerfile for a Next.js 14 application that optimizes for both build performance and a minimal production image.

• Stage 1 (Builder): Use the official node image with the alpine variant. Copy package.json and package-lock.json first to leverage Docker layer caching. Run npm ci for a clean, reproducible install. Then, copy the rest of the source code and run npm run build.
• Stage 2 (Runner): Use the same node:alpine base image. Create a non-root user for security. Copy only the necessary artifacts from the builder stage: .next, public, package.json, and package-lock.json. Expose port 3000 and set the command to npm start.”

Why This Works: This prompt is high-value because it handles a complex task that requires deep knowledge of both Docker and Next.js. It specifically requests a multi-stage build, which is a best practice. The first stage installs all dependencies (including dev dependencies like TypeScript) to build the app, while the second stage only copies the built artifacts and production dependencies. This results in a tiny, secure final image perfect for production.

Automating Quality Assurance with GitHub Actions

A CI/CD pipeline is your automated safety net. It catches errors before they reach your main branch, enforcing a standard of quality across the entire team. Setting this up manually involves navigating YAML syntax and understanding GitHub’s workflow triggers.

The Prompt:

“Create a GitHub Actions workflow file at .github/workflows/ci.yml.

The workflow should trigger on every push to the main branch and on every pull_request targeting main.

It should define a single job named build-and-test that runs on the latest Ubuntu environment. The job must perform the following steps in sequence:

1. Check out the repository code.
2. Set up the Node.js environment (use Node 20).
3. Install dependencies using npm ci.
4. Run the linting script (npm run lint).
5. Run the TypeScript type-checking script (npm run type-check).
6. Run the unit tests script (npm run test).

If any of these steps fail, the entire workflow should fail, blocking the merge.”

Why This Works: This prompt codifies your team’s definition of “done.” By enforcing linting, type-checking, and unit tests on every pull request, you prevent broken code from ever being merged. The prompt is specific about the triggers and the exact sequence of commands, ensuring the pipeline is robust and predictable. It’s a perfect example of using AI to translate a high-level concept (“I need a CI pipeline”) into a precise, executable configuration.

Preparing for a Flawless Vercel Deployment

Before you push to production, you need a final check. This “finishing touches” prompt acts as a pre-flight checklist, ensuring your application is not only buildable but also configured for any last-minute production needs like custom headers or rewrites.

The Prompt:

“Run the npm run build command in the project directory. If the build completes successfully without errors, create a vercel.json file in the root.

In the vercel.json file, add a rewrite rule. If the incoming request path is /dashboard, rewrite it to /dashboard/index. Also, add a custom security header for all responses: set X-Frame-Options to DENY.”

Why This Works: This is an interactive, multi-step command. First, it verifies the core functionality: can your app actually build? This catches a huge class of errors before deployment. Second, it proactively generates a vercel.json file, which is often an afterthought. By including a rewrite rule and a security header, the prompt demonstrates an understanding of real-world deployment requirements, moving your setup from “it works on my machine” to “it’s ready for the world.”

Conclusion: Your New AI-Powered Development Workflow

You started this journey looking for a way to accelerate your Next.js setup. What you’ve unlocked is a fundamental shift in how you’ll build software from this point forward. The prompts we’ve explored aren’t just shortcuts; they’re the building blocks of a new, declarative workflow that fundamentally changes your role as a developer. You’re no longer just a coder; you’re an architect, directing intelligent agents to execute your vision with precision.

From Manual Labor to Strategic Command

The core benefits of this approach are tangible and immediate. By delegating the tedious, error-prone setup of authentication, database connections, and UI configurations, you’ve reclaimed hours of your day. More importantly, you’ve eliminated entire classes of bugs that stem from manual copy-pasting and subtle configuration mistakes. The AI ensures that every integration follows established best practices from the very first line of code, giving you a production-grade foundation without the production-grade effort. This isn’t just about speed; it’s about building with a higher degree of confidence and quality control.

The Power of Declarative Intent

This workflow introduces you to the future of declarative development. Instead of getting bogged down in the how—the specific sequence of terminal commands, the exact syntax for a config file—you now get to focus on the what. You describe the desired outcome, and your AI agent handles the implementation details. This is a massive leap forward. The next logical step is to extend this mindset beyond project scaffolding. Imagine describing a feature and watching it come to life:

• “Create a new dynamic route app/products/[id]/page.tsx that fetches a single product from the database using Prisma and displays its details.”
• “Generate a secure API endpoint at app/api/products/route.ts that handles POST requests to create a new product, including input validation with Zod.”

Your Mission, Should You Choose to Accept It

The most powerful prompts are often the ones you tailor to your own specific workflow and project requirements. The examples provided here are your launchpad. I strongly encourage you to take them, experiment, and refine them. Add your own constraints, integrate your preferred libraries, and build a personal library of prompts that make you an unstoppable force of productivity.

The community around AI-assisted development is evolving at an incredible pace. The most effective developers are sharing their discoveries and learning from one another. Take your most powerful, time-saving prompt, share it with your team or online, and contribute to this collective acceleration. The future of development is collaborative, and it’s being written one powerful prompt at a time.

Critical Warning

Frequently Asked Questions

Q: Do I need to be an expert prompt engineer to use these

No, the prompts provided are designed to be copy-paste ready. You only need to replace placeholders like [PROJECT_NAME] with your specific details

Q: Can I use these prompts with other AI coding assistants

While optimized for Claude Code’s agentic capabilities, the logic applies to any terminal-based AI assistant that can execute commands and edit files

Q: What if a command fails during execution

Simply copy the error message from your terminal and paste it back into Claude Code with the instruction ‘Fix this error and retry the previous step’