/add-streaming

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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: Add text streaming to existing Vercel AI SDK project with minimal docs and focused implementation

Core Principles:

• Detect existing project structure
• Fetch only streaming-specific docs (2-3 URLs)
• Implement streamText() or useChat() based on framework
• Verify functionality

Phase 1: Discovery Goal: Understand existing project setup

Actions:

• Detect project type: Check for package.json, requirements.txt, framework configs
• Load existing configuration: @package.json or @requirements.txt
• Identify framework: Next.js, React, Node.js, Python, etc.
• Find entry points: Look for existing AI SDK usage

Phase 2: Fetch Streaming Documentation Goal: Get streaming-specific docs only

Actions: Fetch these docs in parallel (3 URLs max):

1. WebFetch: https://ai-sdk.dev/docs/foundations/streaming
2. WebFetch: https://ai-sdk.dev/docs/ai-sdk-core/generating-text
3. WebFetch: https://ai-sdk.dev/docs/ai-sdk-ui/chatbot (if React/Next.js/frontend)

Phase 3: Implementation Goal: Add streaming capability using appropriate agent

Actions:

Invoke the general-purpose agent to implement streaming:

The agent should:

• Analyze existing code structure
• Add streamText() function for backend/Node.js projects
• Add useChat() hook for React/Next.js/frontend projects
• Create example endpoint/component showing streaming
• Add proper error handling
• Include helpful comments explaining streaming

Provide the agent with:

• Context: Existing project files and structure
• Target: Add streaming based on framework detected
• Expected output: Working streaming implementation with example

Phase 4: Verification Goal: Ensure streaming works

Actions:

• For TypeScript: Run npx tsc --noEmit to check types
• For JavaScript: Verify syntax
• For Python: Check imports
• Test that streaming endpoint/component exists
• Verify proper SDK usage patterns

Phase 5: Summary Goal: Show what was added

Actions: Provide summary:

• Files modified/created
• Streaming implementation approach (streamText vs useChat)
• How to test streaming
• Example usage code
• Next steps: Consider adding /vercel-ai-sdk:add-tools for function calling

Important Notes:

• Adapts to existing framework (Next.js, React, Node.js, Python)
• Fetches minimal docs (3 URLs)
• Uses general-purpose agent for implementation
• Verifies code compiles/runs
• Focused on streaming only