prompt-engineer

Name: prompt-engineer
Availability: InStock
Author: Coffelix

Design, optimize, and evaluate prompts for Large Language Models.

1|Updated Jan 7, 2026

Search Tags:#prompt engineering #chain-of-thought #few-shot learning #llm optimization #structured outputs #prompt evaluation

Ready to run

npx skills add https://github.com/Coffelix2023/c6x-mynotes --skill prompt-engineer-coffelix2023

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: prompt-engineer
Source: https://github.com/Coffelix2023/c6x-mynotes/tree/main/about_llm/skills/prompt-engineer
Command: npx skills add https://github.com/Coffelix2023/c6x-mynotes --skill prompt-engineer-coffelix2023

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill addresses the challenge of crafting effective prompts that elicit the best possible responses from Large Language Models, ensuring accuracy, efficiency, and desired output formats.

Core Features & Use Cases

Prompt Design: Create clear, concise, and effective prompts for various LLM tasks.
Optimization: Refine existing prompts to improve performance, reduce token usage, and enhance reliability.
Evaluation Frameworks: Build and implement methods to test and measure prompt effectiveness.
Use Case: You need to build a chatbot that summarizes legal documents. This Skill helps you design the initial prompt, test different phrasing, and optimize it to ensure accurate and concise summaries while adhering to legal terminology.

Quick Start

Use the prompt-engineer skill to design a prompt for summarizing technical articles in under 100 words.

Frequently Asked Questions about prompt-engineer

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

FAQPage Schema

How do I design LLM prompts for structured outputs?▼

To optimize LLM prompts, you refine existing phrasing to improve performance, reduce token usage, and enhance reliability. This Skill provides systematic iteration and version documentation to maximize efficiency and accuracy across various LLM tasks.

What is chain-of-thought prompt engineering and how does it work?▼

Few-shot learning in prompt engineering uses specific examples within the prompt to guide the model's output format and logic. This Skill addresses few-shot learning integration to help you craft effective prompts that elicit accurate and reliable responses.

How do I build an evaluation framework to test LLM prompt effectiveness?▼

Using prompt engineering for specialized tasks involves designing clear instructions tailored to specific domains, such as summarizing legal documents with accurate terminology. This Skill enables you to design, test, and optimize prompts for various LLM tasks to ensure domain-specific accuracy.

What's the best way to reduce token usage in Large Language Model prompts?▼

The best way to reduce token usage in Large Language Model prompts is through systematic optimization that refines existing phrasing for efficiency. This Skill helps you optimize prompts to minimize token consumption while maintaining performance and desired output formats.