AI Engineer Skill: Build Production-Grade LLM Applications & Intelligent Agents
Master production-grade LLM applications, RAG systems, and AI agents.
Covers 10+ models, vector search, and agent orchestration. Get the skill to build enterprise AI today.
LLM Integration
RAG
Vector Search
Agent Orchestration
AI Safety
Multimodal AI
About This Skill
Master the AI Engineer skill with 4 core capabilities: LLM Integration & Model Management, RAG & Vector Search, Agent Orchestration, and AI Safety & Monitoring. Covers 10+ models including GPT-4o, Claude 4.5, Llama 3.1, and Mixtral.
Quick Start
1Clarify use cases, constraints, and success metrics.
2Design the AI architecture, data flow, and model selection.
3Implement with monitoring, safety, and cost controls.
4Validate with tests and staged rollout plans.
Example Command
ai-engineer --model gpt-4o --task 'Build a RAG pipeline with vector search'
Core Capabilities
LLM Integration & Model Management
Integrate and manage 10+ models including OpenAI GPT-4o, Anthropic Claude 4.5, Llama 3.1, Mixtral, Qwen 2.5, DeepSeek-V2, and deploy locally with Ollama, vLLM, or TGI.
RAG & Vector Search
Build advanced retrieval-augmented generation pipelines with vector databases, embedding models, and optimized retrieval for production accuracy.
Agent Orchestration
Design and orchestrate intelligent agents with tool use, function calling, and multi-step reasoning for complex task automation.
AI Safety & Monitoring
Implement guardrails for prompt injection, PII detection, policy compliance, and cost controls with staged rollout and monitoring.
Usage Examples
Before
Support team manually searched docs, average resolution time 15 minutes.
After
AI agent resolves 80% of queries in under 30 seconds with 95% accuracy.
Input
Create an AI agent that can book meetings and send emails.
Output
Orchestrated an agent with Claude 4.5 using tool use for calendar and email APIs, with human-in-the-loop approval for sensitive actions.
Input
Deploy a local LLM for offline code generation.
Output
Set up Ollama with Llama 3.2 7B, configured vLLM for low-latency inference, and implemented cost monitoring.
SKILL.md
---
name: ai-engineer
description: Build production-ready LLM applications, advanced RAG systems, and intelligent agents. Implements vector search, multimodal AI, agent orchestration, and enterprise AI integrations.
risk: unknown
source: community
date_added: '2026-02-27'
---
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
- There is no access to data sources or deployment targets
## Instructions
1. Clarify use cases, constraints, and success metrics.
2. Design the AI architecture, data flow, and model selection.
3. Implement with monitoring, safety, and cost controls.
4. Validate with tests and staged rollout plans.
## Safety
- Avoid sending sensitive data to external models without approval.
- Add guardrails for prompt injection, PII, and policy compliance.
## Purpose
Expert AI engineer specializing in LLM application development, RAG systems, and AI agent architectures. Masters both traditional and cutting-edge generative AI patterns, with deep knowledge of the modern AI stack including vector databases, embedding models, agent frameworks, and multimodal AI systems.
## Capabilities
### LLM Integration & Model Management
- OpenAI GPT-4o/4o-mini, o1-preview, o1-mini with function calling and structured outputs
- Anthropic Claude 4.5 Sonnet/Haiku, Claude 4.1 Opus with tool use and computer use
- Open-source models: Llama 3.1/3.2, Mixtral 8x7B/8x22B, Qwen 2.5, DeepSeek-V2
- Local deployment with Ollama, vLLM, TGI (Text Generation Inference)
- Model serving w
Frequently Asked Questions
FAQ
Which LLM models and tools are compatible with this skill?
The AI Engineer skill supports OpenAI GPT-4o/4o-mini, o1-preview, Anthropic Claude 4.5 Sonnet/Haiku, Claude 4.1 Opus, open-source models like Llama 3.1/3.2, Mixtral 8x7B/8x22B, Qwen 2.5, DeepSeek-V2, and local deployment via Ollama, vLLM, and TGI.
Who is the target audience for this skill?
This skill is designed for developers and AI engineers building production-grade LLM applications, RAG systems, and intelligent agents. It is ideal for those working on enterprise AI integrations, vector search, or agent orchestration.
How is this skill different from a general data science or ML skill?
Unlike traditional ML or data science skills, this skill focuses exclusively on LLM applications, generative AI, and agent architectures. It covers modern AI stack components like vector databases, embedding models, and agent frameworks, not pure statistical modeling or data analysis.
What programming languages and frameworks are supported?
The skill is language-agnostic but emphasizes Python for model integration, with support for frameworks like LangChain, LlamaIndex, and direct API usage. It also covers deployment tools like Ollama, vLLM, and TGI.
What results can I expect after applying this skill?
You can expect to build production-ready AI features such as RAG systems that reduce support resolution time by 80%, intelligent agents that automate complex workflows, and cost-controlled LLM deployments with safety guardrails. Typical improvements include 95%+ accuracy on retrieval tasks and 10x faster response times.
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Trigger Phrases
Use these phrases to activate this skill in your AI coding assistant:
Build a RAG systemIntegrate GPT-4oDeploy an AI agentOptimize vector searchImplement AI safety guardrailsOrchestrate multi-agent workflow
