op-prompter

Design reusable prompt templates with few-shot and chain-of-thought patterns.

Updated Aug 23, 2026

Search Tags:#templates #prompt engineering #ai #llm #reliability #output quality

Ready to run

npx skills add https://github.com/MiguelAguiarDEV/baymax --skill op-prompter

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: op-prompter
Source: https://github.com/MiguelAguiarDEV/baymax/tree/main/skills/op-prompter
Command: npx skills add https://github.com/MiguelAguiarDEV/baymax --skill op-prompter

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill addresses the challenge of inconsistent and unreliable output from Large Language Models (LLMs) by providing structured methods for prompt engineering.

Core Features & Use Cases

Structured Prompting: Design and manage prompts with clear instructions, templates, and evaluation patterns.
Reliability Improvement: Enhance LLM output quality, consistency, and control for production environments.
Use Case: Develop reusable prompts for customer service chatbots that consistently provide accurate and helpful responses, adhering to brand guidelines.

Quick Start

Use the op-prompter skill to create a new prompt template for summarizing user feedback.

Frequently Asked Questions about op-prompter

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

FAQPage Schema

How do I improve LLM output consistency using structured prompt templates?▼

LLM output consistency improves by using structured prompt templates that enforce clear instructions, few-shot examples, and chain-of-thought patterns. This approach standardizes model behavior and enforces structured outputs for production reliability.

What is the best way to design reusable prompts for customer service chatbots?▼

Designing reusable prompts for customer service chatbots requires structured prompting with clear instructions and evaluation patterns. This ensures responses consistently provide accurate information while adhering to brand guidelines across interactions.

How do I apply few-shot and chain-of-thought patterns to enforce structured LLM outputs?▼

Few-shot and chain-of-thought patterns enforce structured LLM outputs by providing explicit examples and reasoning steps within prompt templates. These patterns guide the model to generate predictable, formatted responses.

Can I use prompt engineering templates for iterative optimization with A/B tests?▼

Prompt engineering templates support iterative optimization with metrics and A/B tests by providing a structured baseline. You can compare template variations systematically to measure and improve reliability and output quality.

Why does my LLM output quality fluctuate and how can evaluation patterns help?▼

LLM output quality fluctuates due to unstructured prompts lacking clear constraints. Evaluation patterns help by introducing measurable metrics and structured templates that control model responses and enforce consistency.

Do I need specific frameworks to enforce structured outputs from Large Language Models?▼

You do not need specific frameworks to enforce structured outputs from Large Language Models. Structured prompting, templates, and evaluation patterns are applied directly within your prompt design to achieve reliable and consistent generation.