Description
ai-engineer is an agent skill for building production-grade LLM applications, advanced retrieval-augmented generation systems and intelligent agents. It covers vector search, embeddings, retrieval pipelines, multimodal AI, agent orchestration, AI safety, monitoring and cost controls. Use it when you are building or improving LLM features, RAG systems or agents, designing a production AI architecture and its model integration, optimising vector search, or adding safety, monitoring or cost limits to an AI feature. The skill says when not to use it: for pure data science or traditional machine learning without LLMs, for a quick interface change unrelated to AI features, or when there is no access to the data sources or deployment targets. It is part of the sickn33/agentic-awesome-skills repository on GitHub, which has about 47,600 stars. The listing shows it as free, and it has about 1,300 installs. Its security audits show passes from Gen Agent Trust Hub, Socket and Snyk. Install it with npx skills add https://github.com/sickn33/agentic-awesome-skills --skill ai-engineer.…
Highlights
- Production LLM applications
- RAG and retrieval pipelines
- Vector search and embeddings
- AI safety, monitoring and cost controls
- Clear when-not-to-use guidance
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ai-engineer FAQ
What does it help build?
Production LLM applications, RAG systems and intelligent agents.
When should I not use it?
For pure data science or traditional ML without LLMs, for small UI changes unrelated to AI, or without access to data and deployment targets.
What does it cover beyond building?
AI safety, monitoring and cost controls.
How do I install it?
Run npx skills add https://github.com/sickn33/agentic-awesome-skills --skill ai-engineer.
Is it checked for security?
Its security audits show passes from Gen Agent Trust Hub, Socket and Snyk.
What are the best ai-engineer alternatives?
Popular alternatives to ai-engineer include rag-architect, agent-architecture-audit and cost-aware-llm-pipeline. See all ai-engineer alternatives