ai-mllisted

# AI/ML Workflow Bundle ## Overview Comprehensive AI/ML workflow for building LLM applications, implementing RAG systems, creating AI agents, and developing machine learning pipelines. This bundle orchestrates skills for production AI development. ## When to Use This Workflow Use this workflow when: - Building LLM-powered applications - Implementing RAG (Retrieval-Augmented Generation) - Creating AI agents - Developing ML pipelines - Adding AI features to applications - Setting up AI observability ## Workflow Phases ### Phase 1: AI Application Design #### Skills to Invoke - `ai-product` - AI product development - `ai-engineer` - AI engineering - `ai-agents-architect` - Agent architecture - `llm-app-patterns` - LLM patterns #### Actions 1. Define AI use cases 2. Choose appropriate models 3. Design system architecture 4. Plan data flows 5. Define success metrics #### Copy-Paste Prompts ``` Use @ai-product to design AI-powered features ``` ``` Use @ai-agents-architect to design multi-agent system ``` ### Phase 2: LLM Integration #### Skills to Invoke - `llm-application-dev-ai-assistant` - AI assistant development - `llm-application-dev-langchain-agent` - LangChain agents - `llm-application-dev-prompt-optimize` - Prompt engineering - `gemini-api-dev` - Gemini API #### Actions 1. Select LLM provider 2. Set up API access 3. Implement prompt templates 4. Configure model parameters 5. Add streaming support 6. Implement error handling #### Copy-Paste Prompts ``` Use @llm