better-prompt

Optimize rough prompts into structured prompts with Identity, Instructions, Examples, and Context sections.

47|8|Updated Jan 3, 2026

Search Tags:#structure #openai #best-practices #anthropic #prompt-engineering #prompt-optimization #better-prompt

Ready to run

npx skills add https://github.com/huangwb8/skills --skill better-prompt

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: better-prompt
Source: https://github.com/huangwb8/skills/tree/main/better-prompt
Command: npx skills add https://github.com/huangwb8/skills --skill better-prompt

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Transform vague, poorly structured prompts into clear, execution-ready prompts that follow community best practices.

Core Features & Use Cases

Structured prompt optimization based on OpenAI and Anthropic best practices
Handles clarity, completeness, structure, examples, and constraints
Generates a ready-to-use prompt for various models (GPT/Reasoning) across platforms
Use cases: refining prompts for code generation, data analysis, and content creation

Quick Start

Provide an optimized, ready-to-use prompt from a rough user prompt.

Frequently Asked Questions about better-prompt

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

FAQPage Schema

How do I optimize rough prompts for GPT models to get better outputs?▼

To optimize rough prompts, you refine them into a clear structure containing Identity, Instructions, Examples, and Context sections. This structured prompt optimization follows OpenAI and Anthropic best practices to ensure execution-ready inputs for GPT and reasoning models.

What is the best way to structure a prompt for Anthropic models?▼

The best way to structure a prompt is to divide it into Identity, Instructions, Examples, and Context sections. Applying these Anthropic best practices ensures your prompt is complete, clear, and ready for immediate use across various platforms.

Can I use this prompt engineering approach for both content creation and code generation?▼

Yes, you can use this prompt engineering approach for both content creation and code generation. It handles clarity, completeness, and constraints to transform vague inputs into executable prompts tailored for diverse tasks across GPT and reasoning models.

Does this prompt optimization method work with OpenAI and Anthropic best practices simultaneously?▼

Yes, this prompt optimization method works with OpenAI and Anthropic best practices simultaneously. It applies community-validated guidelines from both platforms to generate structured, ready-to-use prompts suitable for GPT and reasoning models.

How do I turn a vague idea into an executable prompt?▼

To turn a vague idea into an executable prompt, you apply structured optimization that enforces clarity, adds relevant examples, and defines constraints. The output is a polished prompt with Identity, Instructions, Examples, and Context ready for deployment.

Why does my unstructured prompt produce inconsistent AI outputs?▼

Unstructured prompts produce inconsistent AI outputs because they lack clear instructions, identity, and context. Applying structured prompt optimization with defined sections resolves ambiguity and enforces best practices for reliable model execution.