/ai-ml

name: ai-ml
description: "AI and machine learning workflow covering LLM application development, RAG implementation, agent architecture, ML pipelines, and AI-powered features."
category: workflow-bundle
risk: safe
source: personal
date_added: "2026-02-27"

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

Selection and completion

Select only phases needed for the requested product; this workflow does not authorize delegation, hosted calls, deployment, background jobs or external messages. Read the actual selected skill and available tool schema. Define task-level quality, retrieval/source checks, data permissions, latency/currency limits and failure recovery before implementation. Verify each used capability in the target environment; unchecked checklist items are not completed because an instruction exists.

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-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

1. Design data pipeline 2. Choose embedding model 3. Set up vector database 4. Implement chunking strategy 5. Configure retrieval 6. Add reranking 7. 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

1. Design agent architecture 2. Define agent roles 3. Implement tool integration 4. Set up memory systems 5. Configure orchestration 6. 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

1. Design ML pipeline 2. Set up data processing 3. Implement model training 4. Configure evaluation 5. Set up model registry 6. 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

1. Set up tracing 2. Configure logging 3. Implement evaluation 4. Monitor performance 5. Track costs 6. 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

1. Implement input validation 2. Add output filtering 3. Configure rate limiting 4. Set up access controls 5. Monitor for abuse 6. 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