Can this skill help with vector database selection?

Yes, it includes deep knowledge of various vector stores like Pinecone, Weaviate, and Qdrant, including indexing strategies like HNSW and IVF.

Is it compatible with multi-agent frameworks?

Yes, it is designed to assist with complex orchestration using frameworks like LangGraph, CrewAI, and AutoGen for multi-agent collaboration.

What models does the AI Engineer skill support?

It provides implementation patterns for a wide range of models including Anthropic Claude, OpenAI GPT-4o, and open-source models like Llama 3.2 and Mixtral via Ollama or vLLM.

Does it provide guidance on AI safety?

Absolutely. The skill prioritizes safety by offering patterns for prompt injection detection, PII redaction, and constitutional AI guardrails.

AI Engineer Professional

Name: AI Engineer Professional
Author: ngxtm

byngxtm

•

データサイエンスとML

Architects production-grade LLM applications, advanced RAG systems, and intelligent multi-agent workflows with enterprise-level safety and observability.

The AI Engineer skill equips your coding environment with deep expertise in generative AI development, focusing on the transition from prototype to production. It provides specialized guidance on implementing complex Retrieval-Augmented Generation (RAG) pipelines, orchestrating multi-agent systems using frameworks like LangGraph and CrewAI, and optimizing model performance through advanced prompt engineering and cost-control strategies. By prioritizing safety guardrails, structured outputs, and comprehensive monitoring, this skill ensures that AI-powered features are scalable, secure, and reliable for enterprise deployment.

主な機能

012 GitHub stars

02Multi-agent orchestration using LangChain, CrewAI, and AutoGen frameworks

03Production LLM integration with structured outputs and tool calling

04AI safety guardrails, cost optimization, and observability patterns

05Advanced RAG implementation with hybrid search and reranking strategies

06Vector database management including Pinecone, Qdrant, and pgvector

ユースケース

01Building a secure enterprise knowledge base with multi-stage RAG pipelines

02Developing autonomous agents for complex task automation and research

03Optimizing LLM inference costs and latency through caching and model routing

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add ngxtm/devkit ai-engineer

For use in Claude.ai and ChatGPT