Can I use this for prompt version control?

Absolutely. The skill provides a standardized metadata schema and version control strategy to manage prompt iterations in a production environment.

Does it support specific programming frameworks?

While the principles are universal, the skill includes specific implementation examples for C# and Microsoft's Semantic Kernel for seamless software integration.

Does this skill help reduce AI costs?

Yes, it includes specific techniques for token optimization and prompt compression, helping you minimize token usage without sacrificing model performance.

What is the Prompt Engineering skill for Claude Code?

It is a specialized skill that provides structured methodologies, design patterns, and evaluation frameworks to help developers create more effective and reliable prompts for Large Language Models.

Prompt Engineering Specialist

Name: Prompt Engineering Specialist
Author: melodic-software

bymelodic-software

•

데이터 과학 및 ML

Designs, evaluates, and optimizes high-performance LLM prompts using systematic engineering patterns and rigorous testing frameworks.

소개

This skill provides a comprehensive framework for developing professional-grade prompts, offering a catalog of established design patterns like Chain-of-Thought, ReAct, and Few-shot learning. It empowers developers to move beyond trial-and-error by providing structured templates, evaluation metrics for accuracy and safety, and systematic versioning strategies for prompt management. Whether you are building complex AI agents or optimizing token costs for production, this skill provides the necessary guidance for creating reliable, cost-effective, and high-performing model interactions.

주요 기능

Comprehensive prompt design patterns including CoT, ReAct, and Tree-of-Thought
Version control and metadata schemas for production prompt management
Systematic evaluation framework with functional, edge-case, and adversarial testing
5 GitHub stars
Token optimization and prompt compression techniques for cost reduction
C# and Semantic Kernel implementation patterns for seamless integration

사용 사례

Building robust system prompts for production-grade AI agents and assistants
Optimizing large-scale LLM deployments to reduce latency and token consumption
Standardizing prompt evaluation and benchmarking across development teams

소개

주요 기능

Comprehensive prompt design patterns including CoT, ReAct, and Tree-of-Thought
Version control and metadata schemas for production prompt management
Systematic evaluation framework with functional, edge-case, and adversarial testing
5 GitHub stars
Token optimization and prompt compression techniques for cost reduction
C# and Semantic Kernel implementation patterns for seamless integration

사용 사례

Building robust system prompts for production-grade AI agents and assistants
Optimizing large-scale LLM deployments to reduce latency and token consumption
Standardizing prompt evaluation and benchmarking across development teams