Can these patterns be used for production applications?

Yes, the skill includes integration patterns for RAG systems and self-verification steps specifically designed for production-grade reliability and consistency.

Why is token efficiency important for Claude Code agents?

The context window is a shared resource; concise prompting ensures more 'working memory' is available for conversation history and complex task execution.

What is the 'Instruction Hierarchy' pattern?

It is a structured way to organize prompts using the sequence: [System Context] → [Task Instruction] → [Examples] → [Input Data] → [Output Format] for maximum clarity.

What are the core capabilities of this prompt engineering skill?

The skill focuses on few-shot learning, chain-of-thought prompting, systematic prompt optimization, template systems, and strategic system prompt design.

How does Chain-of-Thought (CoT) improve LLM performance?

CoT forces the model to perform step-by-step reasoning before answering, which significantly improves accuracy for analytical, mathematical, and logical tasks.

Prompt Engineering Patterns

Name: Prompt Engineering Patterns
Author: pur3v4d3r

bypur3v4d3r

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学习与文档

Optimizes LLM interactions using advanced strategies like few-shot learning, chain-of-thought, and systematic prompt refinement.

关于

This skill provides a comprehensive framework for designing, testing, and scaling high-performance prompts for AI agents and LLM applications. It equips users with specialized patterns such as progressive disclosure, instruction hierarchy, and error recovery to ensure consistent, controllable, and high-quality outputs. By treating prompts as code, this skill bridges the gap between simple chat interactions and production-grade agent workflows, focusing on token efficiency and psychological persuasion principles to maximize model adherence to complex instructions.

主要功能

Instruction hierarchy frameworks to prioritize critical constraints.
Advanced reasoning techniques including Chain-of-Thought and Few-Shot learning.
Reusable template systems for modular and variable-based prompt design.
1 GitHub stars
Systematic optimization workflows for measuring and improving output accuracy.
Agent-specific communication principles for token-efficient interactions.

使用场景

Reducing operational costs through token-efficient system prompt design.
Designing production-ready prompt templates for automated software development.
Improving the reliability of multi-step reasoning tasks for autonomous agents.

关于

主要功能

Instruction hierarchy frameworks to prioritize critical constraints.
Advanced reasoning techniques including Chain-of-Thought and Few-Shot learning.
Reusable template systems for modular and variable-based prompt design.
1 GitHub stars
Systematic optimization workflows for measuring and improving output accuracy.
Agent-specific communication principles for token-efficient interactions.

使用场景

Reducing operational costs through token-efficient system prompt design.
Designing production-ready prompt templates for automated software development.
Improving the reliability of multi-step reasoning tasks for autonomous agents.