Prompt Engineering Patterns

关于

The Prompt Engineering Patterns skill provides a comprehensive framework for designing high-performance LLM interactions. It equips Claude with standardized methods for few-shot learning, chain-of-thought reasoning, and systematic prompt optimization. By implementing these patterns, users can ensure their AI-driven workflows are consistent, cost-effective, and highly controllable. The skill covers everything from system prompt architecture and dynamic template systems to advanced agent communication principles, making it an essential tool for developers building production-grade AI applications and autonomous agents.

主要功能

• Implementation of Few-Shot and Chain-of-Thought reasoning patterns
• Agent communication principles including degrees of freedom and persuasion
• Advanced context window management and token efficiency strategies
• Systematic prompt optimization and A/B testing frameworks
• Reusable template systems for modular and consistent interactions
• 1 GitHub stars

使用场景

• Designing production-ready prompt templates for complex application logic
• Improving model accuracy on analytical tasks through step-by-step reasoning
• Optimizing agent-to-agent communication to reduce latency and costs