Agent skill · software engineering · aiskillstore
ai-ml
AI and machine learning workflow covering LLM application development, RAG implementation, agent architecture, ML pipelines, and AI-powered features.
Why this skill is useful
Adds comprehensive workflows and scripts for building LLM applications, RAG systems, and ML pipelines that extend AI capabilities significantly.
What it needs
Requires crewai, gemini-api, langchain, langfuse, langgraph installed locally. About 4k tokens when loaded. Last updated 2026-08-07. 405 stars on the source repository.
What this skill does
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 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 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. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills aiskillstore/ai-ml