Can I use this for production applications?

Yes, the skill is specifically designed for production-grade LLM implementations where performance, cost, and reliability are critical factors.

How does it improve LLM reliability?

By applying structured reasoning techniques like Chain-of-Thought and dynamic few-shot learning, it reduces hallucination and ensures more consistent outputs.

Does it support template management?

Yes, it includes patterns for modular prompt construction, variable interpolation, and progressive disclosure to handle complex tasks.

How does it handle token usage?

It provides optimization patterns focused on token efficiency, helping you minimize costs and latency without sacrificing the quality of the AI response.

What is the Prompt Engineering Patterns skill?

It is a specialized Claude Code skill that provides frameworks and best practices for designing, optimizing, and implementing high-performance prompts for LLMs.

Prompt Engineering Patterns

Name: Prompt Engineering Patterns
Author: HermeticOrmus

byHermeticOrmus

Data Science & ML

Implements advanced prompt engineering techniques to optimize LLM performance and output reliability for production applications.

About

This skill equips Claude with specialized patterns and best practices for high-performance prompt engineering. It covers essential strategies like chain-of-thought reasoning, dynamic few-shot learning, and structured system prompt design to ensure LLM outputs are consistent, controllable, and efficient. Developers can use these patterns to build robust AI-driven applications, reduce token costs, and implement sophisticated reasoning workflows within their codebases, making it ideal for moving from simple chat interactions to production-grade AI systems.

Key Features

Reusable prompt template systems with variable interpolation
Advanced Chain-of-Thought (CoT) and reasoning elicitation patterns
Performance optimization techniques for token efficiency and latency
System prompt design for specialized AI personas and constraints
0 GitHub stars
Dynamic Few-Shot learning strategies with semantic similarity selection

Use Cases

Refining inconsistent AI responses through iterative optimization workflows
Building production-ready LLM pipelines with structured output
Implementing complex RAG systems with sophisticated context integration

About

Key Features

Reusable prompt template systems with variable interpolation
Advanced Chain-of-Thought (CoT) and reasoning elicitation patterns
Performance optimization techniques for token efficiency and latency
System prompt design for specialized AI personas and constraints
0 GitHub stars
Dynamic Few-Shot learning strategies with semantic similarity selection

Use Cases

Refining inconsistent AI responses through iterative optimization workflows
Building production-ready LLM pipelines with structured output
Implementing complex RAG systems with sophisticated context integration