/new-app

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🚨 EXECUTION NOTICE FOR CLAUDE

When you invoke this command via SlashCommand, the system returns THESE INSTRUCTIONS below.

YOU are the executor. This is NOT an autonomous subprocess.

• ✅ The phases below are YOUR execution checklist
• ✅ YOU must run each phase immediately using tools (Bash, Read, Write, Edit, TodoWrite)
• ✅ Complete ALL phases before considering this command done
• ❌ DON't wait for "the command to complete" - YOU complete it by executing the phases
• ❌ DON't treat this as status output - it IS your instruction set

Immediately after SlashCommand returns, start executing Phase 0, then Phase 1, etc.

See @CLAUDE.md section "SlashCommand Execution - YOU Are The Executor" for detailed explanation.

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Available Skills

This commands has access to the following skills from the vercel-ai-sdk plugin:

• SKILLS-OVERVIEW.md
• agent-workflow-patterns: AI agent workflow patterns including ReAct agents, multi-agent systems, loop control, tool orchestration, and autonomous agent architectures. Use when building AI agents, implementing workflows, creating autonomous systems, or when user mentions agents, workflows, ReAct, multi-step reasoning, loop control, agent orchestration, or autonomous AI.
• generative-ui-patterns: Generative UI implementation patterns for AI SDK RSC including server-side streaming components, dynamic UI generation, and client-server coordination. Use when implementing generative UI, building AI SDK RSC, creating streaming components, or when user mentions generative UI, React Server Components, dynamic UI, AI-generated interfaces, or server-side streaming.
• provider-config-validator: Validate and debug Vercel AI SDK provider configurations including API keys, environment setup, model compatibility, and rate limiting. Use when encountering provider errors, authentication failures, API key issues, missing environment variables, model compatibility problems, rate limiting errors, or when user mentions provider setup, configuration debugging, or SDK connection issues.
• rag-implementation: RAG (Retrieval Augmented Generation) implementation patterns including document chunking, embedding generation, vector database integration, semantic search, and RAG pipelines. Use when building RAG systems, implementing semantic search, creating knowledge bases, or when user mentions RAG, embeddings, vector database, retrieval, document chunking, or knowledge retrieval.
• testing-patterns: Testing patterns for Vercel AI SDK including mock providers, streaming tests, tool calling tests, snapshot testing, and test coverage strategies. Use when implementing tests, creating test suites, mocking AI providers, or when user mentions testing, mocks, test coverage, AI testing, streaming tests, or tool testing.

To use a skill:

!{skill skill-name}

Use skills when you need:

• Domain-specific templates and examples
• Validation scripts and automation
• Best practices and patterns
• Configuration generators

Skills provide pre-built resources to accelerate your work.

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Security Requirements

CRITICAL: All generated files must follow security rules:

@docs/security/SECURITY-RULES.md

Key requirements:

• Never hardcode API keys or secrets
• Use placeholders: your_service_key_here
• Protect .env files with .gitignore
• Create .env.example with placeholders only
• Document key acquisition for users

Arguments: $ARGUMENTS

Goal: Create minimal Vercel AI SDK project scaffold with basic configuration. Features like streaming, tools, and chat are added separately using other commands.

Core Principles:

• Keep it simple - scaffold only, no features
• Fetch minimal docs (3-5 URLs)
• Get user choices early
• Verify setup works

Phase 1: Discovery Goal: Understand project requirements

Actions:

• Parse $ARGUMENTS for project name (if provided)
• Ask ONE question at a time, wait for response:
  1. Project name: Use $ARGUMENTS or ask "What should we name your project?"
  2. Language: "TypeScript, JavaScript, or Python?"
  3. Framework: "Next.js, React (Vite), Node.js, Python (FastAPI), Svelte, or Vue?"
  4. AI Provider: "OpenAI, Anthropic, Google, or xAI?"

Phase 2: Fetch Minimal Documentation Goal: Get essential setup docs only

Actions: Fetch these docs in parallel (4 URLs max):

1. WebFetch: https://ai-sdk.dev/docs/introduction
2. WebFetch: https://ai-sdk.dev/docs/foundations/overview
3. WebFetch: https://ai-sdk.dev/docs/getting-started/[framework-specific]
4. WebFetch: https://ai-sdk.dev/providers/ai-sdk-providers/[provider]

Replace [framework-specific] and [provider] with user selections.

Phase 3: Project Setup Goal: Create basic project structure

Actions:

1. Check latest version:

   • Use WebSearch to find latest ai package version
   • For TypeScript/JavaScript: https://www.npmjs.com/package/ai
   • For Python: https://pypi.org/project/ai/

2. Create project directory:

   • Create directory with project name
   • Navigate into it

3. Initialize project:

   • TypeScript/JavaScript: Run npm init -y, setup package.json with type: module
   • Python: Create requirements.txt or pyproject.toml

4. Install SDK and provider:

   • TypeScript/JavaScript: npm install ai@latest @ai-sdk/[provider]
   • Python: pip install ai [provider-package]
   • Verify installed versions

5. Create basic files:

   • Create .env.example with [PROVIDER]_API_KEY placeholder
   • Add .env to .gitignore
   • Create minimal entry point (index.ts/js or main.py) with simple example
   • Add helpful comments explaining next steps

6. Environment notes:

   • Explain how to get API key from provider
   • Show how to set .env file

Phase 4: Verification Goal: Ensure setup is correct

Actions:

Invoke the appropriate verifier agent based on language:

For TypeScript: Invoke the vercel-ai-verifier-ts agent to validate the setup For JavaScript: Invoke the vercel-ai-verifier-js agent to validate the setup For Python: Invoke the vercel-ai-verifier-py agent to validate the setup

Agent should check:

• Package installation
• Configuration files
• Basic imports work
• Environment setup

Phase 5: Next Steps Goal: Guide user on what to do next

Actions: Provide clear next steps:

1. Set API key: How to add key to .env file

2. Run the app: Command to execute (npm start, python main.py, etc.)

3. Add features: Point to other commands:

   • /vercel-ai-sdk:add-streaming - Add text streaming
   • /vercel-ai-sdk:add-tools - Add function/tool calling
   • /vercel-ai-sdk:add-chat - Add chat UI
   • /vercel-ai-sdk:add-provider - Add another AI provider

4. Resources:

   • SDK docs: https://ai-sdk.dev/docs
   • Examples: https://ai-sdk.dev/examples
   • Provider docs link

Important Notes:

• This creates SCAFFOLD ONLY - no features implemented yet
• Use other commands to add streaming, tools, chat, etc.
• Always check latest package versions before installing
• Verify code compiles/runs before finishing
• Keep it minimal and focused