How does this skill improve AI output consistency?

It implements techniques like few-shot learning with dynamic examples, Chain-of-Thought reasoning traces, and self-verification steps to ensure models follow instructions reliably.

What is the Prompt Engineering Patterns skill?

It is a specialized capability for Claude Code that provides proven patterns, best practices, and implementation guides for creating high-performing, reliable LLM prompts.

Can I use this for production applications?

Yes, the skill is specifically designed for production use cases, focusing on controllability, error recovery, and performance metrics like token usage and latency.

Does it support prompt templates?

Absolutely. It includes patterns for variable interpolation, conditional prompt sections, and modular prompt composition to make your prompts reusable and maintainable.

Prompt Engineering Patterns

Name: Prompt Engineering Patterns
Author: zsutxz

byzsutxz

データサイエンスとML

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

概要

This skill equips Claude with specialized knowledge for designing, optimizing, and scaling LLM prompts for production-grade applications. It provides structured guidance on implementing sophisticated patterns such as Chain-of-Thought reasoning, dynamic few-shot learning with semantic selection, and modular template systems. By leveraging these patterns, users can transform inconsistent AI outputs into reliable, structured, and high-quality responses while maintaining token efficiency and minimizing latency across complex AI workflows.

主な機能

Dynamic few-shot learning with example selection patterns
0 GitHub stars
Self-verification and error recovery implementation
Systematic prompt optimization and iterative refinement
Chain-of-Thought and reasoning elicitation strategies
Modular prompt template systems and variable interpolation

ユースケース

Developing robust prompt templates for enterprise AI applications
Building specialized AI assistants with strict behavioral constraints
Debugging and refining inconsistent or low-quality LLM outputs

概要

主な機能

Dynamic few-shot learning with example selection patterns
0 GitHub stars
Self-verification and error recovery implementation
Systematic prompt optimization and iterative refinement
Chain-of-Thought and reasoning elicitation strategies
Modular prompt template systems and variable interpolation

ユースケース

Developing robust prompt templates for enterprise AI applications
Building specialized AI assistants with strict behavioral constraints
Debugging and refining inconsistent or low-quality LLM outputs