How does this skill help with token usage?

It provides optimization patterns to remove redundancy, consolidate instructions, and use modular templates, helping to reduce costs and decrease response latency.

What is the Prompt Engineering Patterns skill?

It is a specialized capability for Claude Code that provides advanced techniques to improve the performance, reliability, and controllability of LLMs in production environments.

Does it support RAG integration?

Absolutely. The skill includes specific patterns for combining retrieved context with optimized prompt structures to improve accuracy and groundedness in data-heavy applications.

Can this skill help with inconsistent LLM responses?

Yes, it implements specific patterns like Chain-of-Thought, few-shot examples, and self-verification steps to ensure structured reasoning and more predictable outputs.

Prompt Engineering Patterns

Name: Prompt Engineering Patterns
Author: HermeticOrmus

byHermeticOrmus

Data Science & ML

Optimizes LLM performance and reliability through advanced techniques like few-shot learning, chain-of-thought reasoning, and modular prompt templates.

About

The Prompt Engineering Patterns skill provides a comprehensive framework for building high-performance, production-ready LLM applications. It equips developers with advanced strategies for few-shot learning, structured reasoning paths, and iterative prompt optimization to ensure consistent, controllable outputs. By implementing modular templates and robust system prompts, users can significantly reduce token usage and latency while improving the overall accuracy and reliability of their AI-driven workflows within Claude Code.

Key Features

Modular prompt template systems with variable interpolation
0 GitHub stars
Comprehensive system prompt design for specialized behavior
Dynamic few-shot learning with semantic example selection
Iterative prompt optimization and A/B testing workflows
Structured reasoning patterns including Chain-of-Thought (CoT)

Use Cases

Designing complex prompts for production-grade AI assistants
Debugging inconsistent LLM outputs using structured reasoning traces
Optimizing token efficiency and reducing latency in high-volume applications

About

Key Features

Modular prompt template systems with variable interpolation
0 GitHub stars
Comprehensive system prompt design for specialized behavior
Dynamic few-shot learning with semantic example selection
Iterative prompt optimization and A/B testing workflows
Structured reasoning patterns including Chain-of-Thought (CoT)

Use Cases

Designing complex prompts for production-grade AI assistants
Debugging inconsistent LLM outputs using structured reasoning traces
Optimizing token efficiency and reducing latency in high-volume applications