/openai-prompt-engineer

name: openai-prompt-engineer
description: Generate and improve prompts using best practices for OpenAI GPT-5 and other LLMs. Apply advanced techniques like chain-of-thought, few-shot prompting, and progressive disclosure.

OpenAI Prompt Engineer

A comprehensive skill for crafting, analyzing, and improving prompts for OpenAI's GPT-5 and other modern Large Language Models (LLMs), with focus on GPT-5-specific optimizations and universal prompting techniques.

What This Skill Does

Helps you create and optimize prompts using cutting-edge techniques:

• **Generate new prompts** - Build effective prompts from scratch
• **Improve existing prompts** - Enhance clarity, structure, and results
• **Apply best practices** - Use proven techniques for each model
• **Optimize for specific models** - GPT-5, Claude-specific strategies
• **Implement advanced patterns** - Chain-of-thought, few-shot, structured prompting
• **Analyze prompt quality** - Identify issues and suggest improvements

Why Prompt Engineering Matters

**Without good prompts:**

• Inconsistent or incorrect outputs
• Poor instruction following
• Wasted tokens and API costs
• Multiple attempts needed
• Unpredictable behavior

**With optimized prompts:**

• Accurate, consistent results
• Better instruction adherence
• Lower costs and latency
• First-try success
• Predictable, reliable outputs

Supported Models & Approaches

GPT-5 (OpenAI)

• Structured prompting (role + task + constraints)
• Reasoning effort calibration
• Agentic behavior control
• Verbosity management
• Prompt optimizer integration

Claude (Anthropic)

• XML tag structuring
• Step-by-step thinking
• Clear, specific instructions
• Example-driven prompting
• Progressive disclosure

Universal Techniques

• Chain-of-thought prompting
• Few-shot learning
• Zero-shot prompting
• Self-consistency
• Role-based prompting

Core Prompting Principles

1. Be Clear and Specific

**Bad:** "Write about AI" **Good:** "Write a 500-word technical article explaining transformer architecture for software engineers with 2-3 years of experience. Include code examples in Python and focus on practical implementation."

2. Provide Structure

Use clear formatting to organize instructions:

Role: You are a senior Python developer
Task: Review this code for security vulnerabilities
Constraints:
- Focus on OWASP Top 10
- Provide specific line numbers
- Suggest fixes with code examples
Output format: Markdown with severity ratings

3. Use Examples (Few-Shot)

Show the model what you want:

Input: "User clicked login"
Output: "USER_LOGIN_CLICKED"

Input: "Payment processed successfully"
Output: "PAYMENT_PROCESSED_SUCCESS"

Input: "Email verification failed"
Output: [Your turn]

4. Enable Reasoning

Add phrases like:

• "Think step-by-step"
• "Let's break this down"
• "First, analyze... then..."
• "Show your reasoning"

5. Define Output Format

Specify exactly how you want the response:

<output_format>
  <summary>One sentence overview</summary>
  <details>
    <point>Key finding 1</point>
    <point>Key finding 2</point>
  </details>
  <recommendation>Specific action to take</recommendation>
</output_format>

Prompt Engineering Workflow

1. Define Your Goal

• What task are you solving?
• What's the ideal output?
• Who's the audience?
• What model will you use?

2. Choose Your Technique

• **Simple task?** → Direct instruction
• **Complex reasoning?** → Chain-of-thought
• **Pattern matching?** → Few-shot examples
• **Need consistency?** → Structured format + examples

3. Build Your Prompt

Use this template:

[ROLE/CONTEXT]
You are [specific role with relevant expertise]

[TASK]
[Clear, specific task description]

[CONSTRAINTS]
- [Limitation 1]
- [Limitation 2]

[FORMAT]
Output should be [exact format specification]

[EXAMPLES - if using few-shot]
[Example 1]
[Example 2]

[THINK STEP-BY-STEP - if complex reasoning]
Before answering, [thinking instruction]

4. Test and Iterate

• Run the prompt
• Analyze output quality
• Identify issues
• Refine and retry
• Document what works

Advanced Techniques

Chain-of-Thought (CoT) Prompting

**When to use:** Complex reasoning, math, multi-step problems

**How it works:** Ask the model to show intermediate steps

**Example:**

Problem: A store has 15 apples. They sell 60% in the morning and
half of what's left in the afternoon. How many remain?

Please solve this step-by-step:
1. Calculate morning sales
2. Calculate remaining after morning
3. Calculate afternoon sales
4. Calculate final remaining

**Result:** More accurate answers through explicit reasoning

Few-Shot Prompting

**When to use:** Pattern matching, classification, style transfer

**How it works:** Provide 2-5 examples, then the actual task

**Example:**

Convert casual text to professional business tone:

Input: "Hey! Thanks for reaching out. Let's chat soon!"
Output: "Thank you for your message. I look forward to our conversation."

Input: "That's a great idea! I'm totally on board with this."
Output: "I appreciate your suggestion and fully support this initiative."

Input: "Sounds good, catch you later!"
Output: [Model completes]

Zero-Shot Chain-of-Thought

**When to use:** Complex problems without examples

**How it works:** Simply add "Let's think step by step"

**Example:**

Question: What are the security implications of storing JWTs
in localStorage?

Let's think step by step:

**Magic phrase:** "Let's think step by step" → dramatically improves reasoning

Structured Output with XML

**When to use:** Working with Claude or need parsed output

**Example:**

Analyze this code for issues. Structure your response as:

<analysis>
  <security_issues>
    <issue severity="high|medium|low">
      <description>What's wrong</description>
      <location>File and line number</location>
      <fix>How to fix it</fix>
    </issue>
  </security_issues>
  <performance_issues>