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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 developmentai-engineer - AI engineeringai-agents-architect - Agent architecturellm-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 developmentllm-application-dev-langchain-agent - LangChain agentsllm-application-dev-prompt-optimize - Prompt engineeringgemini-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 engineeringrag-implementation - RAG implementationembedding-strategies - Embedding selectionvector-database-engineer - Vector databasessimilarity-search-patterns - Similarity searchhybrid-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 patternsautonomous-agent-patterns - Agent patternscrewai - CrewAI frameworklanggraph - LangGraphmulti-agent-patterns - Multi-agent systemscomputer-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 engineeringmlops-engineer - MLOpsmachine-learning-ops-ml-pipeline - ML pipelinesml-pipeline-workflow - ML workflowsdata-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 observabilitymanifest - Manifest telemetryevaluation - AI evaluationllm-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 securitysecurity-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
RAG System
AI Agents
Observability
Quality Gates
Related Workflow Bundles
development - Application developmentdatabase - Data managementcloud-devops - Infrastructuretesting-qa - AI testing
Limitations
Use this skill only when the task clearly matches the scope described above.
Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.
