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prompt-engineering

Structure prompts for Claude models using XML tags and advanced techniques.

Updated Aug 23, 2026
Search Tags:#prompt engineering#ai#llm#claude#orchestration#agentic
One-click install
npx skills add https://github.com/cuba6112/skillfactory --skill prompt-engineering-cuba6112
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: prompt-engineering
Source: https://github.com/cuba6112/skillfactory/tree/main/.claude/skills/prompt-engineering
Command: npx skills add https://github.com/cuba6112/skillfactory --skill prompt-engineering-cuba6112

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill provides comprehensive guidance on crafting effective prompts for Claude models, ensuring optimal performance and accurate results across various applications.

Core Features & Use Cases

  • Advanced Prompting Techniques: Learn to use XML tags, multishot examples, chain of thought, and prompt chaining for structured and effective communication.
  • Agentic Orchestration: Understand how to leverage subagents, skills, and agent loops for complex multi-agent workflows.
  • Use Case: You're developing a new AI assistant and need to ensure it understands complex instructions and can interact with tools reliably. This Skill guides you on structuring prompts for maximum Claude model comprehension and capability.

Quick Start

Use the prompt-engineering skill to learn how to structure prompts for Claude models using XML tags.

Frequently Asked Questions about prompt-engineering

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I structure prompts for Claude models to ensure accurate instruction following?▼

Claude prompt engineering uses XML tags to separate context, instructions, and examples for optimal model comprehension. This structured structuring approach ensures reliable instruction following and accurate results across complex tasks.

What is the best way to implement agentic orchestration and subagents with Claude?▼

Agentic orchestration with Claude leverages subagents, agent loops, and MCP integration to manage complex workflows. This approach coordinates multiple specialized agents, enabling robust multi-agent orchestration and reliable tool usage.

When do I need to use chain of thought and prompt chaining techniques in LLM applications?▼

Chain of thought and prompt chaining are needed when handling complex reasoning or long context tasks with LLMs. These techniques break down intricate problems into sequential steps, improving Claude's accuracy and output formatting reliability.

Can I use multishot examples and extended thinking for Claude 4.x-specific prompt engineering?▼

Yes, multishot examples and extended thinking are effective for Claude 4.x prompt engineering. Providing multiple examples guides Claude's responses, while extended thinking supports deeper processing for complex tool usage scenarios.

Why does my Claude prompt fail to maintain output formatting with long context inputs?▼

Claude prompt formatting fails with long context inputs when lacking explicit instructions and XML structuring. To maintain output formatting, apply long context handling techniques and clear XML tags to segment input data effectively.

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