How does this skill handle reasoning models like Claude 3.7 or o1?

The skill utilizes zero-shot prompts for reasoning models, avoiding manual chain-of-thought instructions which can sometimes interfere with the model's native reasoning processes.

What is context engineering vs prompt engineering?

Context engineering focuses on the design of the information system and data structure (like XML tags and documentation order), whereas prompt engineering often focuses on 'perfect wording'.

Can I use this for system prompt generation?

Yes, it includes specific templates for system prompts that define roles, capabilities, security constraints, and output standards for coding agents.

Is this skill specific to Claude models only?

No, while used within Claude Code, the techniques are model-agnostic and based on best practices that apply to Claude, GPT-4, Gemini, and other frontier reasoning models.

What is the Prompting skill for Claude Code?

It is a specialized skill that provides a framework for designing high-performance AI instructions, focusing on context engineering and structured XML templates to improve model output quality.

AI Prompting & Instruction Design

Name: AI Prompting & Instruction Design
Author: hongbietcode

byhongbietcode

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コンテンツ管理

Optimizes AI interactions using model-agnostic context engineering and advanced instruction patterns for high-performance coding agents.

概要

This skill provides a standardized framework for crafting high-fidelity prompts and system instructions, moving beyond simple wording to structured context engineering. It leverages 2025 industry standards for both reasoning and standard models, offering automated decision flows for code generation, complex reasoning, and multi-step workflows. By implementing XML-based structures and advanced techniques like Tree of Thoughts and ReAct, it ensures that Claude and other LLMs produce reliable, production-grade outputs while maximizing context window efficiency and caching.

主な機能

Standardized XML/Markdown templates for context, tasks, and constraints
Context stack prioritization to optimize for model recency and prompt caching
Advanced technique library including Extended Thinking and Orchestrator-Worker patterns
Model-agnostic best practices optimized for Claude, GPT, and reasoning models
Automated decision flows for selecting zero-shot vs. chain-of-thought logic
2 GitHub stars

ユースケース

Optimizing complex multi-step developer workflows into atomic, verifiable tasks
Refining long-context prompts to improve accuracy and reduce hallucination in large codebases
Designing robust system prompts for specialized AI coding agents and autonomous tools

概要

主な機能

Standardized XML/Markdown templates for context, tasks, and constraints
Context stack prioritization to optimize for model recency and prompt caching
Advanced technique library including Extended Thinking and Orchestrator-Worker patterns
Model-agnostic best practices optimized for Claude, GPT, and reasoning models
Automated decision flows for selecting zero-shot vs. chain-of-thought logic
2 GitHub stars

ユースケース

Optimizing complex multi-step developer workflows into atomic, verifiable tasks
Refining long-context prompts to improve accuracy and reduce hallucination in large codebases
Designing robust system prompts for specialized AI coding agents and autonomous tools