Does this skill help with Chain-of-Thought reasoning?

Yes, it includes specific patterns for eliciting step-by-step reasoning using both zero-shot and few-shot CoT techniques to improve model accuracy on complex logic tasks.

How does it handle dynamic prompt templates?

It supports structured template systems featuring variable interpolation, conditional sections, and role-based composition, allowing for modular and reusable prompt design.

What is the Prompt Engineering Patterns skill?

It is a specialized capability for Claude Code that provides advanced techniques and implementation patterns for designing, optimizing, and managing prompts for production-grade LLM applications.

Can I use this skill to optimize token usage and latency?

Absolutely. The skill provides strategies for reducing token counts without sacrificing quality, alongside performance metrics for tracking latency and success rates.

Prompt Engineering Patterns

Name: Prompt Engineering Patterns
Author: yusoofsh

byyusoofsh

Data Science & ML

Implements advanced prompt engineering techniques to optimize LLM performance, reliability, and structured output in production environments.

About

This skill provides a comprehensive toolkit for developers building LLM-powered applications, offering sophisticated patterns like few-shot learning, chain-of-thought reasoning, and dynamic template systems. It enables users to move beyond basic instructions by implementing structured reasoning traces, strategic example selection, and iterative optimization workflows that ensure high-quality, consistent, and cost-effective AI interactions across various use cases.

Key Features

Chain-of-thought and structured reasoning elicitation
Few-shot learning with dynamic semantic example selection
Reusable prompt template systems with variable interpolation
Iterative optimization workflows and performance metrics tracking
System prompt design for specialized AI personas and constraints
0 GitHub stars

Use Cases

Standardizing AI behavior using role-based system prompts and explicit constraints
Reducing hallucinations through structured reasoning and verification steps
Building robust production-ready LLM pipelines with high consistency

About

Key Features

Chain-of-thought and structured reasoning elicitation
Few-shot learning with dynamic semantic example selection
Reusable prompt template systems with variable interpolation
Iterative optimization workflows and performance metrics tracking
System prompt design for specialized AI personas and constraints
0 GitHub stars

Use Cases

Standardizing AI behavior using role-based system prompts and explicit constraints
Reducing hallucinations through structured reasoning and verification steps
Building robust production-ready LLM pipelines with high consistency