to-prompt-gpt

Rewrite rough requests into concise GPT-5.5-ready prompts.

Updated May 28, 2026

Search Tags:#prompt engineering #responses api #openai api #prompt rewriting #gpt-5.5 #context normalization #output template

Ready to run

npx skills add https://github.com/haryelramalho/skills --skill to-prompt-gpt

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: to-prompt-gpt
Source: https://github.com/haryelramalho/skills/tree/main/skills/mine/to-prompt-gpt
Command: npx skills add https://github.com/haryelramalho/skills --skill to-prompt-gpt

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill turns rough context, incomplete requests, or existing prompts into clear GPT-5.5-ready prompts, reducing ambiguity and improving downstream model behavior.

Core Features & Use Cases

Prompt Rewriting: Refines user requests into outcome-first prompts with the right role, goal, context, constraints, and output shape.
Context Normalization: Converts scattered notes, issue reports, logs, or code snippets into structured prompt material.
GPT-Specific Optimization: Applies GPT-5.5 guidance for API prompts, Responses workflows, coding agents, research synthesis, and support assistants.

Quick Start

Use the to-prompt-gpt skill to rewrite my rough request into a concise GPT-5.5-ready prompt.

Frequently Asked Questions about to-prompt-gpt

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

FAQPage Schema

How do I rewrite rough notes into OpenAI API prompts for GPT models?▼

Rewriting rough notes into OpenAI API prompts requires normalizing scattered context into structured material with a defined role, goal, constraints, and output shape. This approach converts incomplete requests into outcome-first prompts tailored for GPT-5.5-specific guidance.

What is the best way to optimize existing prompts for coding agents and research synthesis?▼

Optimizing existing prompts for coding agents and research synthesis involves applying GPT-specific guidance to reduce ambiguity and enforce output templating. This refines requests into concise, outcome-first prompts that improve downstream model behavior across Responses workflows.

How do I normalize incomplete requests into structured prompt material for support assistants?▼

Normalizing incomplete requests into structured prompt material for support assistants converts scattered issue reports and logs into clear role, goal, and context definitions. This context normalization handles missing information to produce concise GPT-5.5-ready prompts.

Can I use context normalization for OpenAI API Responses workflows with missing information?▼

Context normalization for OpenAI API Responses workflows handles missing information by restructuring incomplete requests into outcome-first prompts. It applies missing-context handling and output templating to ensure concise prompt material for GPT-5.5-specific guidance.