Ai Engineer Expert

This is a solid production AI engineering reference that covers the full stack from LLM integration to RAG systems. You get working examples of unified clients with fallback handling, vector database implementations using Chroma and LangChain, and practical prompt engineering patterns like chain-of-thought and few-shot learning. The code is production-focused with proper error handling, streaming responses, and function calling. Most valuable when you're moving beyond proof-of-concept work and need to build reliable AI systems that handle failures gracefully and scale properly. The agent system section appears incomplete but the RAG and LLM client implementations are comprehensive enough to fork and modify for real projects.

skills.sh installs: 239
GitHub stars: 44
last updated: Aug 21, 2026

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Install

$ npx -y skills add personamanagmentlayer/pcl --skill ai-engineer-expert --agent claude-code

Installs into .claude/skills of the current project.

Files

AI Engineer Expert

Expert guidance for implementing AI systems, LLM integration, prompt engineering, and deploying production AI applications.

Core Concepts

AI Engineering

LLM integration and orchestration
Prompt engineering and optimization
RAG (Retrieval-Augmented Generation)
Vector databases and embeddings
Fine-tuning and adaptation
AI agent systems

Production AI

Model deployment strategies
API design for AI services
Rate limiting and cost control
Error handling and fallbacks
Monitoring and logging
Security and safety

LLM Patterns

Chain-of-thought prompting
Few-shot learning
System/user message design
Function calling and tools
Streaming responses
Context window management

LLM Integration

from openai import AsyncOpenAI
from anthropic import Anthropic
from typing import List, Dict, Optional
import asyncio

class LLMClient:
    """Unified LLM client with fallback"""

    def __init__(self, primary: str = "openai", fallback: str = "anthropic"):
        self.openai_client = AsyncOpenAI()
        self.anthropic_client = Anthropic()
        self.primary = primary
        self.fallback = fallback

SKILL.md on GitHub