When should I use the Chain of Thought feature?

Chain of Thought is best used for complex tasks requiring logic, mathematical calculations, or multi-factor decision-making where the model needs to 'think' step-by-step before providing a final answer.

Does this skill work with models other than Claude?

While specifically optimized for Claude's capabilities (such as its proficiency with XML tags), these engineering principles are broadly applicable to most modern large language models.

How do XML tags help in prompting?

XML tags act as clear delimiters that separate your instructions from the input data, preventing the model from getting confused and ensuring it follows your constraints accurately.

Why is document placement important in long-context prompts?

Placing documents at the top of a prompt before the instructions helps models like Claude maintain better focus on the source material when processing large amounts of information.

What is the primary benefit of the Prompt Engineering skill?

It provides a standardized framework for creating prompts that are clearer to AI models, significantly reducing errors and improving the consistency and accuracy of outputs.

Prompt Engineering Expert

Name: Prompt Engineering Expert
Author: itsmostafa

byitsmostafa

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データサイエンスとML

Optimizes LLM output quality by providing domain-specific guidance and structural patterns for crafting high-performance prompts.

概要

This skill serves as a comprehensive reference guide for developers and AI engineers looking to maximize the effectiveness of LLM interactions within Claude Code. It provides a standardized framework for implementing advanced techniques like multishot examples, Chain of Thought reasoning, and XML tag structuring. By following these best practices, users can significantly reduce model hallucinations, ensure consistent formatting, and handle complex multi-step tasks or long-context documents with higher precision. It is an essential utility for anyone building AI-powered applications who needs to move beyond basic instructions to production-grade prompt architecture.

主な機能

Role-based prompting patterns for domain-specific expertise
10 GitHub stars
Long-context optimization strategies for processing large documents
Multishot example templates to guide model behavior and formatting
Structured reasoning patterns using Chain of Thought and thinking tags
XML tag implementation for clear separation of instructions and data

ユースケース

Debugging and refining inconsistent or low-quality model responses
Designing robust instructions for complex, multi-step code generation tasks
Standardizing output formats for reliable automated post-processing

概要

主な機能

Role-based prompting patterns for domain-specific expertise
10 GitHub stars
Long-context optimization strategies for processing large documents
Multishot example templates to guide model behavior and formatting
Structured reasoning patterns using Chain of Thought and thinking tags
XML tag implementation for clear separation of instructions and data

ユースケース

Debugging and refining inconsistent or low-quality model responses
Designing robust instructions for complex, multi-step code generation tasks
Standardizing output formats for reliable automated post-processing