| name | ai-ml |
| description | AI and machine learning workflow covering LLM application development, RAG implementation, agent architecture, ML pipelines, and AI-powered features. |
| type | skill |
| created | 2026-02-27T00:00:00.000Z |
| domain | ai-ml |
| category | ml-data-science |
| risk | safe |
| source | personal |
| tags | ["skill","ai-ml","ml-data-science"] |
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 engineering- - RAG implementation
