/add-data-features

---

🚨 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.

---

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.

---

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: Add AI-powered data processing capabilities to a Vercel AI SDK application including embeddings generation, RAG (Retrieval Augmented Generation) with vector databases, and structured data generation.

Core Principles:

• Understand data sources and volume before designing solutions
• Ask about vector database preferences
• Follow Vercel AI SDK documentation patterns
• Optimize for cost and performance

Phase 1: Discovery Goal: Understand what data features are needed

Actions:

• Parse $ARGUMENTS to identify requested features
• If unclear or no arguments provided, use AskUserQuestion to gather:
  • Which data features do you want? (Embeddings, RAG, structured data generation)
  • What's the size of your dataset?
  • Do you have a vector database? (Pinecone, Weaviate, Chroma, pgvector, etc.)
  • What kind of data needs to be processed?
• Load package.json to understand current setup
• Example: @package.json

Phase 2: Analysis Goal: Understand current project state

Actions:

• Check for existing AI SDK installation
• Identify data sources (files, APIs, databases)
• Verify database infrastructure availability
• Assess data volume and processing requirements
• Example: !{bash ls *.txt *.pdf *.md 2>/dev/null | wc -l}

Phase 3: Implementation Goal: Add requested data features using specialized agent

Actions:

Invoke the vercel-ai-data-agent to implement the requested data features.

The agent should:

• Fetch relevant Vercel AI SDK documentation for the requested features
• Design optimal architecture for the data volume
• Install required packages (vector DB clients, zod, etc.)
• Implement requested features following SDK best practices:
  • Embeddings generation using embed() and embedMany()
  • Vector database integration and schema design
  • RAG pipeline with document chunking and retrieval
  • Structured data generation using generateObject/streamObject
• Add proper TypeScript types and Zod schemas
• Implement error handling and retry logic
• Optimize for cost and performance

Provide the agent with:

• Context: Current project structure and data sources
• Target: $ARGUMENTS (requested data features)
• Expected output: Production-ready data processing pipeline

Phase 4: Verification Goal: Ensure features work correctly

Actions:

• Run TypeScript compilation check
• Example: !{bash npx tsc --noEmit}
• Test embeddings generation with sample data
• Verify vector database operations (if applicable)
• Check cost implications and optimization

Phase 5: Summary Goal: Document what was added

Actions:

• List all data features that were implemented
• Show database schema and indexes created
• Note any API keys or environment variables needed
• Provide cost estimates and optimization suggestions
• Suggest next steps (data ingestion, query optimization)