prompt-creator

Name: prompt-creator
Availability: InStock
Author: Melvyn

Design system prompts, user prompts, and few-shot examples for LLMs.

223|68|Updated Aug 18, 2025

Search Tags:#Claude #LLM #GPT #few-shot #prompt-engineering #system-prompt #prompt-optimization

Ready to run

npx skills add https://github.com/Melvynx/aiblueprint --skill prompt-creator-melvynx

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: prompt-creator
Source: https://github.com/Melvynx/aiblueprint/tree/main/claude-code-config/skills/meta-prompt-creator
Command: npx skills add https://github.com/Melvynx/aiblueprint --skill prompt-creator-melvynx

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Prompt engineering is essential for getting reliable, high-quality responses from Claude, GPT, and other LLMs. This skill provides a practical, repeatable approach to create system prompts, user prompts, few-shot examples, and optimization strategies that improve performance across tasks.

Core Features & Use Cases

System prompt design: Build robust system prompts that define role, constraints, and interaction style.
User prompt refinement: Clarify intent, reduce ambiguity, and tailor prompts to model capabilities.
Few-shot guidance: Create effective in-context exemplars to guide model outputs.
Prompt optimization workflows: Iterate prompts to reduce errors, improve consistency, and adapt to new tasks.

Quick Start

To start, provide a target task and desired output format, then request a system prompt and a few-shot example set to bootstrap the interaction.

Frequently Asked Questions about prompt-creator

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

FAQPage Schema

How do I write a system prompt for Claude and GPT?▼

To write a system prompt for Claude and GPT, define the model's role, establish constraints, and specify the interaction style to guide outputs reliably across diverse tasks. This structured approach reduces ambiguity and improves performance.

What is few-shot prompt engineering and when do I need it?▼

Few-shot prompt engineering provides in-context examples to guide LLM outputs. You need it when clarifying intent or reducing ambiguity is insufficient, ensuring consistent performance across complex tasks.

How do I optimize LLM prompts to reduce errors?▼

To optimize LLM prompts and reduce errors, iterate through a workflow that refines user prompts, adds few-shot examples, and enforces best practices. This improves consistency and adapts the model to new tasks.

What is the best way to structure prompts for different LLMs?▼

The best way to structure prompts for different LLMs is to separate system prompts from user prompts, clarify intent, and tailor the prompt to model capabilities using a repeatable optimization workflow.

Do I need coding skills to create few-shot examples for GPT?▼

No, you do not need coding skills to create few-shot examples for GPT. You provide a target task and desired output format, then request a system prompt and example set to bootstrap the interaction.