prompt-refiner-gpt

Name: prompt-refiner-gpt
Availability: InStock
Author: Factory

Generate refined GPT prompts with role framing, structured steps, and output formats.

1|Updated Jul 29, 2026

Search Tags:#OpenAI #GPT #best-practices #prompt-engineering #prompt #instruction-writing

Ready to run

npx skills add https://github.com/Factory-AI/factory --skill prompt-refiner-gpt

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: prompt-refiner-gpt
Source: https://github.com/Factory-AI/factory/tree/main/examples/power-user-skills/prompt-refiner-gpt
Command: npx skills add https://github.com/Factory-AI/factory --skill prompt-refiner-gpt

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill helps users craft refined prompts for GPT models by applying clear role framing, structured steps, and explicit output formats to improve consistency and quality.

Core Features & Use Cases

Role framing templates: Define the assistant's persona and context to fit the task.
Procedural prompts: Break tasks into numbered steps to improve reproducibility.
Output controls: Specify exact formats (JSON, Markdown, etc.) and provide concrete examples to steer results.
Use Case: When designing prompts for complex tasks (e.g., product briefs, research summaries, or code reviews), generate a refined prompt that reduces ambiguity and improves guidance.

Quick Start

Use this skill to generate a refined prompt for a GPT task that includes a clear role, stepwise instructions, and explicit output formatting.

Frequently Asked Questions about prompt-refiner-gpt

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

FAQPage Schema

How do I write better GPT prompts for complex tasks?▼

To write better GPT prompts, you should apply clear role framing, structured steps, and explicit output formats. Defining the assistant's persona and breaking tasks into numbered steps improves consistency and reduces ambiguity.

What are OpenAI best practices for prompt engineering?▼

OpenAI best practices for prompt engineering involve using clear frontmatter, structured prompt patterns, and example-driven guidance. Specifying exact formats like JSON or Markdown and providing concrete examples steers GPT results effectively.

How do I structure GPT prompts to get consistent JSON or Markdown output?▼

To get consistent JSON or Markdown output from GPT, use output controls in your prompt to specify exact formats and provide concrete examples. Procedural prompts with numbered steps also improve reproducibility and output consistency.

Can I use prompt engineering techniques for research summaries and code reviews?▼

Yes, you can use prompt engineering for research summaries and code reviews. By generating refined prompts with role framing templates and stepwise instructions, you reduce ambiguity and improve guidance for these complex tasks.

Why do my GPT prompts produce inconsistent or ambiguous results?▼

GPT prompts produce inconsistent results when they lack clear role framing, structured steps, and explicit output formats. Applying procedural prompts and example-driven guidance aligns results with best practices and reduces ambiguity.