openai-prompt-engineer

# 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 or