Optimize rough prompts into structured prompts with Identity, Instructions, Examples, and Context sections.
npx skills add https://github.com/huangwb8/skills --skill better-prompt
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
Transform vague, poorly structured prompts into clear, execution-ready prompts that follow community best practices.
Provide an optimized, ready-to-use prompt from a rough user prompt.
High-intent search queries and answers about installing and using this skill.
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.
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.
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.
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.
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.
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.
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