How do I measure prompt success?

The skill includes an optimization workflow that focuses on measurable metrics such as accuracy, consistency, token efficiency, and response latency to validate improvements.

How does this skill improve AI performance?

It implements advanced patterns like Chain-of-Thought and Few-Shot learning which provide the model with better reasoning paths and relevant examples to follow, significantly increasing task accuracy.

Does it support multiple LLM models?

While designed for Claude Code, the underlying prompt engineering principles provided (like CoT and template architecture) are portable and applicable to most modern large language models.

What is Chain-of-Thought reasoning?

It is a prompting technique that encourages the model to break down complex problems into logical, sequential steps before providing a final answer, which is essential for analytical tasks.

Can I use this for production applications?

Yes, it includes frameworks for building modular, versioned prompt templates and system prompts designed specifically for production stability and consistent model behavior.

Prompt Engineering & Optimization

Name: Prompt Engineering & Optimization
Author: giuseppe-trisciuoglio

bygiuseppe-trisciuoglio

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126

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Data Science & ML

Optimizes LLM performance through advanced prompt patterns, few-shot learning, and structured chain-of-thought reasoning frameworks.

The Prompt Engineering skill provides a comprehensive framework for designing, testing, and refining high-performance prompts for production AI applications. It enables developers to implement sophisticated techniques like few-shot learning with strategic example selection, structured chain-of-thought reasoning for complex logic, and modular template systems for scalable prompt architecture. Whether you are troubleshooting poor model performance or building a complex multi-step reasoning system, this skill provides the necessary patterns and optimization workflows to ensure consistent, accurate, and token-efficient AI interactions across various tasks.

Key Features

01126 GitHub stars

02Comprehensive System Prompt Design for consistent model behavior and safety

03Iterative Prompt Optimization workflows with measurable performance metrics

04Advanced Few-Shot Learning with diversity sampling and edge-case selection

05Modular Template Systems for reusable and scalable prompt architectures

06Structured Chain-of-Thought reasoning patterns for complex multi-step problems

Use Cases

01Creating reusable, modular prompt components for large-scale AI integration

02Improving the accuracy of complex analytical or reasoning tasks in AI apps

03Building production-ready system prompts with strict output constraints

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add giuseppe-trisciuoglio/developer-kit prompt-engineering

For use in Claude.ai and ChatGPT

Download Skill