prompt-engineer

Design optimized prompts for LLMs using chain-of-thought and constitutional AI techniques.

11|2|Updated Feb 6, 2026
One-click install
npx skills add https://github.com/mupengi-bot/mupengism --skill prompt-engineer-mupengi-bot
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: prompt-engineer
Source: https://github.com/mupengi-bot/mupengism/tree/main/skills/prompt-engineer
Command: npx skills add https://github.com/mupengi-bot/mupengism --skill prompt-engineer-mupengi-bot

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill addresses the challenge of creating highly effective and optimized prompts for Large Language Models (LLMs), ensuring AI systems perform reliably, safely, and efficiently.

Core Features & Use Cases

  • Advanced Prompting Techniques: Masters Chain-of-Thought, Constitutional AI, meta-prompting, and more.
  • Model-Specific Optimization: Tailors prompts for OpenAI, Anthropic, and open-source models.
  • Production Systems: Designs robust prompt templates, RAG integrations, and multi-agent workflows.
  • Use Case: When developing a new AI feature, use this Skill to design a prompt that guides the AI to generate creative marketing copy while adhering to brand guidelines and safety protocols.

Quick Start

Use the prompt-engineer skill to design a chain-of-thought prompt for financial analysis that shows clear reasoning steps.

Frequently Asked Questions about prompt-engineer

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

FAQPage Schema
How do I write a chain-of-thought prompt for LLM optimization?

Constitutional AI is a prompting technique that enforces safety and brand guidelines by instructing the model to self-critique and revise its outputs against a set of explicit rules. It ensures AI systems perform reliably and safely without generating harmful or off-brand content.

Can I use prompt engineering to tailor prompts for both OpenAI and Anthropic models?

Yes, you can design production prompt templates that integrate directly with RAG systems. This approach structures multi-agent workflows and robust templates to guide the AI in generating outputs like marketing copy while adhering strictly to safety protocols and brand guidelines.

What is the best way to design a production prompt for an AI agent?

The best way to optimize multi-agent workflows is to design robust prompt templates that clearly define agent roles and interactions. This approach improves agent performance and ensures reliable, efficient execution across complex AI system architectures.

Why does my LLM output violate safety protocols despite explicit instructions?

Your LLM output might violate safety protocols because the prompt lacks constitutional AI techniques or structured self-critique guidelines. Implementing production prompt strategies with built-in safety constraints helps the AI system perform reliably and adhere to brand rules.