Expert-level AI implementation, deployment, LLM integration, and production AI systems
NewMaintained
Automatic
◆AI Engineer packai-engineeringllmdeployment
Last verified 9/3/2026
What it changes
Passed 12 of 22 test cases with the skill, and 14 of 22 without it.
Without skill64%
With skill55%
0 fixed12 passed either way8 still failing2 broke
Pass-rate lift
-9 pp
Tokens
+105%
Agent turns
0%
Response time
-2%
Model gemini-3.6-flashMeasured 9/3/2026v112 of 22 cases passed with skill
Tokens, turns and time describe resource use, not a dollar price.
What's inside
The instructions your model reads when the skill loads.
SKILL.md435 linessingle file
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"""