Does it support dynamic example selection?

Yes, it includes patterns for dynamic few-shot learning, allowing you to select examples based on semantic similarity or diversity sampling to maximize relevance.

Can I use these patterns for production applications?

Yes, the skill is specifically designed for production-grade prompt engineering, focusing on consistency, error handling, and performance metrics.

How does this skill improve LLM reliability?

It uses structured patterns like Chain-of-Thought reasoning and self-verification steps to ensure the model processes logic correctly before delivering a final response.

How does it help with token optimization?

It provides specific strategies for removing redundant instructions and consolidating prompts to reduce costs and latency without sacrificing output quality.

Prompt Engineering Patterns

Name: Prompt Engineering Patterns
Author: HermeticOrmus

byHermeticOrmus

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데이터 과학 및 ML

Implements advanced prompt engineering techniques to maximize LLM performance, reliability, and controllability in production environments.

This skill provides a comprehensive framework for designing and optimizing LLM prompts using production-grade patterns such as chain-of-thought reasoning, few-shot learning, and dynamic template systems. It empowers developers to build more reliable AI applications by providing structured methodologies for example selection, instruction hierarchy, and error recovery. By utilizing these battle-tested patterns, developers can ensure consistent, high-quality outputs while optimizing for token efficiency and latency across complex, multi-turn AI workflows.

주요 기능

01Advanced Few-Shot Learning with dynamic example selection strategies

02Reusable Prompt Templates with variable interpolation and role-based composition

03Structured Chain-of-Thought and reasoning elicitation patterns

04Iterative Prompt Optimization workflows for performance and token efficiency

05Robust Error Recovery and fallback instructions for reliable production outputs

064 GitHub stars

사용 사례

01Designing complex system prompts for specialized AI agents and autonomous assistants

02Optimizing RAG system performance through context-aware prompt engineering and grounding

03Building self-verifying prompts that validate their own output against specific quality criteria

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add hermeticormus/claude-code-game-development prompt-engineering-patterns

For use in Claude.ai and ChatGPT

Download Skill

주요 기능

01Advanced Few-Shot Learning with dynamic example selection strategies

02Reusable Prompt Templates with variable interpolation and role-based composition

03Structured Chain-of-Thought and reasoning elicitation patterns

04Iterative Prompt Optimization workflows for performance and token efficiency

05Robust Error Recovery and fallback instructions for reliable production outputs

064 GitHub stars

사용 사례

01Designing complex system prompts for specialized AI agents and autonomous assistants

02Optimizing RAG system performance through context-aware prompt engineering and grounding

03Building self-verifying prompts that validate their own output against specific quality criteria

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add hermeticormus/claude-code-game-development prompt-engineering-patterns

For use in Claude.ai and ChatGPT

Download Skill