--- name: ai-ml-fbc3584f7e6a 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 ## 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. - 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. --- Source attribution: sickn33 Original contribution (preserved): https://github.com/sickn33/antigravity-awesome-skills/blob/6f819e34e3847c4a9a4715409ec4d76b0ad42bdb/skills/ai-ml/SKILL.md License: MIT + CC-BY-4.0 License information: https://creativecommons.org/licenses/by/4.0/ Original upstream instructions; downloaded from Mega AI Hub. ## Upstream license notice MIT License Copyright (c) 2026 Antigravity User 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. Creative Commons Attribution 4.0 International (CC BY 4.0) This file clarifies the license for original non-code repository content. - Original code and tooling remain licensed under the MIT License in `LICENSE`. - Original documentation and other non-code written content are licensed under the Creative Commons Attribution 4.0 International license unless a more specific upstream notice says otherwise. - Third-party attributions, upstream licenses, and exceptions are tracked in `docs/sources/sources.md`. Canonical license URL: https://creativecommons.org/licenses/by/4.0/ Review the full license text at the canonical URL before reuse. # Mega AI Hub resource metadata Creator / publisher: sickn33 Original contribution (preserved): https://github.com/sickn33/antigravity-awesome-skills/blob/6f819e34e3847c4a9a4715409ec4d76b0ad42bdb/skills/ai-ml/SKILL.md Attribution basis: Explicit original-content license; package is not listed among upstream exceptions Fields of work: Development & Engineering, Product Management Roles: ML / AI Engineer, Data Engineer, Product Manager, Growth Product Manager, Technical Product Manager Specialties: Frontend, Design, Word Documents, API, Database, Testing, DevOps Tools: Gemini Api, Crewai, Langgraph Country applicability: Country not specified Classification basis: Source category, role and specialization labels Instruction SHA-256: 3e7b470f1268a22bfbad94729c3e30e223c60bc07b3075bf64eba7f91fa5d928