Regtransfers/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

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

@ Related Workflow Bundles

• development - Application development
• database - Data management
• cloud-devops - Infrastructure
• testing-qa - AI testing

@ Limitations

• Use this skill only when the task clearly matches the scope described above.
• never 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.