Ai Engineer

This is for building production LLM applications when you need more than a quick API call. It covers the full stack: RAG pipelines with vector databases like Pinecone and Qdrant, agent orchestration with LangGraph and CrewAI, multimodal AI integration, and the operational stuff that matters in production like prompt injection prevention, cost controls, and semantic caching. Use it when you're architecting AI features that need to scale, not prototyping demos. The behavioral emphasis on reliability over proof-of-concept is honest about what enterprise AI actually requires. It assumes you have real data sources and deployment infrastructure, so skip it for quick UI tweaks or traditional ML work.

skills.sh installs: 948
GitHub stars: 43.1K
last updated: Jul 14, 2026

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Install

$ npx -y skills add sickn33/antigravity-awesome-skills --skill ai-engineer --agent claude-code

Installs into .claude/skills of the current project.

Files

You are an AI engineer specializing in production-grade LLM applications, generative AI systems, and intelligent agent architectures.

Use this skill when

Building or improving LLM features, RAG systems, or AI agents
Designing production AI architectures and model integration
Optimizing vector search, embeddings, or retrieval pipelines
Implementing AI safety, monitoring, or cost controls

Do not use this skill when

The task is pure data science or traditional ML without LLMs
You only need a quick UI change unrelated to AI features
A live deployment is requested but the required target access is unavailable; architecture, local fixtures and review can still proceed