Can I use these patterns for automated code reviews?

Yes, the skill includes template systems and instruction hierarchies specifically designed to structure repetitive developer workflows like code reviews and security audits.

What is the primary benefit of using this Prompt Engineering skill?

It provides standardized patterns and best practices to make your LLM interactions more predictable, accurate, and efficient, especially for complex analytical tasks.

How does this skill help with token optimization?

It teaches you how to balance example counts, use system prompts for persistent instructions, and apply progressive disclosure to minimize unnecessary token usage.

When should I use Few-Shot Learning versus Chain-of-Thought?

Use Few-Shot Learning when you need specific formatting or pattern matching; use Chain-of-Thought for tasks requiring multi-step logic and reasoning.

Prompt Engineering Expert

Name: Prompt Engineering Expert
Author: claudiodearaujo

byclaudiodearaujo

0•

Learning & Documentation

Master advanced prompting patterns and optimization techniques to maximize LLM performance and reliability.

This skill provides a comprehensive framework for advanced prompt engineering, helping developers transition from basic instructions to robust, production-grade AI interactions. It covers essential architectural patterns like few-shot learning, chain-of-thought reasoning, and modular template systems to ensure AI outputs are accurate, consistent, and controllable. By utilizing systematic optimization techniques and specific instruction hierarchies, users can significantly improve model reasoning, reduce token waste, and effectively debug complex agent behaviors.

Key Features

01Advanced System Prompt Design to define persistent roles and constraints

02Chain-of-Thought (CoT) prompting for complex logical reasoning

03Implementation of Few-Shot Learning with structured input-output examples

04Modular Template Systems for reusable and variable-driven prompt structures

05Progressive Disclosure patterns for systematic prompt refinement

060 GitHub stars

Use Cases

01Optimizing AI agent accuracy for complex software architecture and logic tasks

02Standardizing model outputs for seamless integration into production data pipelines

03Debugging inconsistent LLM behaviors through structured error recovery patterns

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add claudiodearaujo/sistema-de-narra-o-de-livro-front prompt-engineering

For use in Claude.ai and ChatGPT

Download Skill