prompt-engineering

Design and optimize LLM prompts with few-shot and chain-of-thought patterns.

Updated May 25, 2026
One-click install
npx skills add https://github.com/yehezkieldio/formalist --skill prompt-engineering-yehezkieldio
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: prompt-engineering
Source: https://github.com/yehezkieldio/formalist/tree/main/.agents/skills/prompt-engineering
Command: npx skills add https://github.com/yehezkieldio/formalist --skill prompt-engineering-yehezkieldio

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps you design, debug, and optimize prompts for LLMs, improving accuracy, reliability, and efficiency of AI interactions.

Core Features & Use Cases

  • Prompt drafting and refinement
  • Few-shot example selection, chain-of-thought scaffolding, system prompt design, and template composition
  • Guidance for evaluating prompts, templates, and system prompts across tasks
  • Use cases include building consistent assistant behaviors, improving answer quality, and reducing hallucinations

Quick Start

Draft and export a complete, reusable prompt framework for a given task, including a system prompt, templates, and evaluation criteria.

Frequently Asked Questions about prompt-engineering

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

FAQPage Schema
How do I design an LLM system prompt to reduce hallucinations and improve response consistency?

To reduce hallucinations, design system prompts with clear constraints, few-shot examples, and chain-of-thought scaffolding to guide LLM reasoning and enforce consistent assistant behaviors across interactions.

What is few-shot prompt engineering and when should I use it for template composition?

Few-shot prompt engineering involves providing specific examples within a template to guide LLM outputs. Use it for technical, educational, or enterprise contexts where strict output formatting and response reliability are required.

How do I evaluate prompt quality and establish criteria for LLM interactions?

Evaluate prompt quality by establishing specific evaluation criteria for templates and system prompts, measuring response accuracy, checking safety guidelines, and testing reliability across varied educational and technical tasks.

What is the best way to structure a chain-of-thought prompt for complex technical tasks?

Structure chain-of-thought prompts by breaking down complex technical tasks into sequential reasoning steps, using modular templates and few-shot patterns to scaffold the LLM's logic and improve answer accuracy.

Can I build modular prompt templates that integrate with enterprise safety guidelines?

Yes, you can compose modular prompt templates that incorporate enterprise safety guidelines, allowing you to draft, refine, and export reusable prompt frameworks satisfying specific safety and evaluation requirements.