What is the Prompt Engineering Patterns skill?

It is a specialized capability for Claude Code designed to help developers implement advanced prompting techniques like chain-of-thought and few-shot learning to improve AI output quality.

How does this skill improve LLM reliability?

It provides structured patterns for reasoning and example selection, which significantly reduces model hallucinations and ensures consistent formatting for production use.

Can I use this for production AI applications?

Yes, the skill focuses on production-grade concerns including latency reduction, token efficiency, and building error recovery directly into your prompts.

Does it support dynamic prompt templates?

Yes, it includes patterns for variable interpolation, conditional sections, and modular components to make your prompts reusable across different datasets and contexts.

Prompt Engineering Patterns

Name: Prompt Engineering Patterns
Author: wshobson

bywshobson

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23,194

Data Science & ML

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

About

This skill provides a comprehensive toolkit for mastering prompt engineering within Claude Code, enabling developers to build production-grade AI applications with high controllability and precision. It guides the implementation of sophisticated patterns such as dynamic few-shot example selection, structured chain-of-thought reasoning traces, and modular template systems to ensure consistent, high-quality outputs. Whether you are debugging inconsistent model behavior or designing complex multi-agent workflows, this skill offers the frameworks and best practices needed to maximize LLM efficiency and minimize hallucinations.

Key Features

23,194 GitHub stars
Advanced few-shot learning with dynamic semantic similarity selection
Modular prompt template systems with variable interpolation
Chain-of-thought reasoning patterns for complex logic elicitation
Iterative prompt optimization and performance metric tracking
Structured system prompt design for specialized AI behavior

Use Cases

Implementing structured reasoning to improve model accuracy on complex tasks
Designing robust prompts for production-ready LLM applications
Creating reusable, version-controlled prompt templates for collaborative development

About

Key Features

23,194 GitHub stars
Advanced few-shot learning with dynamic semantic similarity selection
Modular prompt template systems with variable interpolation
Chain-of-thought reasoning patterns for complex logic elicitation
Iterative prompt optimization and performance metric tracking
Structured system prompt design for specialized AI behavior

Use Cases

Implementing structured reasoning to improve model accuracy on complex tasks
Designing robust prompts for production-ready LLM applications
Creating reusable, version-controlled prompt templates for collaborative development