--- name: ai-ml-0d6abb80c103 description: "AI and machine learning workflow covering LLM application development, RAG implementation, agent architecture, ML pipelines, and AI-powered features." source: personal risk: safe domain: artificial-intelligence category: workflow-bundle version: 1.0.0 --- # 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 --- Source attribution: ngxtm Original project: https://github.com/ngxtm/devkit/blob/9439f27ce3fa7fe53ff9278a1ee9a44bffa70eba/skills/ai-ml/SKILL.md License: MIT License information: https://opensource.org/license/mit Original upstream instructions; downloaded from Dave AI. ## Upstream license notice MIT License Copyright (c) 2024 Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. # Dave AI resource metadata Creator / publisher: ngxtm Original project: https://github.com/ngxtm/devkit/blob/9439f27ce3fa7fe53ff9278a1ee9a44bffa70eba/skills/ai-ml/SKILL.md Attribution basis: Source repository record Fields of work: Development & Engineering, Product Management Roles: Data Engineer, Growth Product Manager, ML / AI Engineer, Product Manager, Technical Product Manager Specialties: API, Backend, DevOps Tools: Crewai, Gemini Api, Langgraph Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: 2c2f1c1c70036c38110d1fa199b420cd13ce90e3e767bd8206340e87344b797c