ai-ml

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 Define AI use cases Choose appropriate models Design system architecture Plan data flows 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 Select LLM provider Set up API access Implement prompt templates Configure model parameters Add streaming support Implement error handling Copy-Paste Prompts Use @llm-application-dev-ai-assistant to build conversational AI Use @llm-application-dev-langchain-agent to create LangChain agents Use @llm-application-dev-prompt-optimize to optimize prompts Phase 3: RAG Implementation Skills to Invoke rag-engineer - RAG engineering rag-implementation - RAG implementation embedding-strategies - Embedding selection vector-database-engineer - Vector databases similarity-search-patterns - Similarity search hybrid-search-implementation - Hybrid search Actions Design data pipeline Choose embedding model Set up vector database Implement chunking strategy Configure retrieval Add reranking Implement caching Copy-Paste Prompts Use @rag-engineer to design RAG pipeline Use @vector-database-engineer to set up vector search Use @embedding-strategies to select optimal embeddings Phase 4: AI Agent Development Skills to Invoke autonomous-agents - Autonomous agent patterns autonomous-agent-patterns - Agent patterns crewai - CrewAI framework langgraph - LangGraph multi-agent-patterns - Multi-agent systems computer-use-agents - Computer use agents Actions Design agent architecture Define agent roles Implement tool integration Set up memory systems Configure orchestration Add human-in-the-loop Copy-Paste Prompts Use @crewai to build role-based multi-agent system Use @langgraph to create stateful AI workflows Use @autonomous-agents to design autonomous agent Phase 5: ML Pipeline Development Skills to Invoke ml-engineer - ML engineering mlops-engineer - MLOps machine-learning-ops-ml-pipeline - ML pipelines ml-pipeline-workflow - ML workflows data-engineer - Data engineering Actions Design ML pipeline Set up data processing Implement model training Configure evaluation Set up model registry Deploy models Copy-Paste Prompts Use @ml-engineer to build machine learning pipeline Use @mlops-engineer to set up MLOps infrastructure Phase 6: AI Observability Skills to Invoke langfuse - Langfuse observability manifest - Manifest telemetry evaluation - AI evaluation llm-evaluation - LLM evaluation Actions Set up tracing Configure logging Implement evaluation Monitor performance Track costs Set up alerts Copy-Paste Prompts Use @langfuse to set up LLM observability Use @evaluation to create evaluation framework Phase 7: AI Security Skills to Invoke prompt-engineering - Prompt security security-scanning-security-sast - Security scanning Actions Implement input validation Add output filtering Configure rate limiting Set up access controls Monitor for abuse Implement audit logging AI Development Checklist LLM Integration API keys secured Rate limiting configured Error handling implemented Streaming enabled Token usage tracked RAG System Data pipeline working Embeddings generated Vector search optimized Retrieval accuracy tested Caching implemented AI Agents Agent roles defined Tools integrated Memory working Orchestration tested Error handling robust Observability Tracing enabled Metrics collected Evaluation running Alerts configured Dashboards created Quality Gates All AI features tested Performance benchmarks met Security measures in place Observability configured Documentation complete Related Workflow Bundles development - Application development database - Data management cloud-devops - Infrastructure testing-qa - AI testing