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prompting

Structure prompts with role, instructions, context, output format, and examples.

2|1|Updated Nov 13, 2025
Search Tags:#writing#prompt engineering#communication#ai#llm#prompt design
One-click install
npx skills add https://github.com/MolcajeteAI/plugin --skill prompting-molcajeteai
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: prompting
Source: https://github.com/MolcajeteAI/plugin/tree/main/molcajete/skills/prompting
Command: npx skills add https://github.com/MolcajeteAI/plugin --skill prompting-molcajeteai

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill addresses the challenge of generating consistent and high-quality output from Large Language Models (LLMs) by providing a structured approach to prompt engineering.

Core Features & Use Cases

  • Prompt Structure Guidance: Offers a template for organizing prompts into clear sections (Role, Instructions, Context, Output Format, Examples).
  • Best Practices: Details principles like specificity, positive instructions, role-setting, and output format definition.
  • Common Mistakes: Highlights pitfalls to avoid, such as vague instructions or missing context.
  • Quality Checklist: Provides a checklist to ensure prompts are effective before use.
  • Use Case: A marketing team needs to generate social media posts for a new product. They can use this Skill to structure a prompt that clearly defines the target audience, key message, desired tone, and output format (e.g., Twitter thread, LinkedIn post).

Quick Start

Use the prompting skill to generate a prompt for summarizing technical documents for a non-technical audience.

Frequently Asked Questions about prompting

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

FAQPage Schema
How do I write effective LLM prompts for consistent output?▼

Effective LLM prompts require a structured approach using clear sections for role-setting, instructions, context, output format, and examples to generate consistent, high-quality responses.

What is the best way to structure prompt engineering for large language models?▼

The best prompt engineering structure organizes prompts into clear sections like Role, Instructions, Context, Output Format, and Examples, applying principles of specificity and positive instructions.

Why does my LLM prompt return vague or inconsistent responses?▼

Vague or inconsistent LLM responses often occur when prompts lack specificity, miss necessary context, or fail to define the desired output format and role clearly.

Can I use this prompt design methodology across different AI platforms?▼

Yes, this prompt design methodology is model-agnostic, facilitating structured prompt engineering principles that are applicable across various large language model platforms.

What should be on a prompt quality checklist before executing an LLM task?▼

A prompt quality checklist should verify that instructions are specific, roles are set, context is complete, and output formatting is explicitly defined to ensure effective LLM responses.

Does prompt engineering work for summarizing technical documents for non-technical audiences?▼

Prompt engineering works for summarizing technical documents by structuring prompts to clearly define the target audience, key message, and desired tone for the LLM.

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