zircote/.claude/openai-prompt-engineer
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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.
Install
npx vskill@latest install zircote/.claude/openai-prompt-engineerSKILL.md
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:
3. Use Examples (Few-Shot)
Show the model what you want:
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:
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:
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:
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:
Zero-Shot Chain-of-Thought
When to use: Complex problems without examples
How it works: Simply add "Let's think step by step"
Example:
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:
Progressive Disclosure
When to use: Large context, multi-step workflows
How it works: Break tasks into stages, only request what's needed now
Example:
Model-Specific Best Practices
GPT-5 Optimization
Structured Prompting:
Control Agentic Behavior:
Manage Verbosity:
Claude Optimization
Use XML Tags:
Step-by-Step Thinking:
Clear Specificity:
Prompt Improvement Checklist
Use this checklist to improve any prompt:
- [ ] Clear role defined - Is the AI's expertise specified?
- [ ] Specific task - Is it unambiguous what to do?
- [ ] Constraints listed - Are limitations clear?
- [ ] Format specified - Is output structure defined?
- [ ] Examples provided - Do you show what you want (if needed)?
- [ ] Reasoning enabled - Do you ask for step-by-step thinking (if complex)?
- [ ] Context included - Does the AI have necessary background?
- [ ] Edge cases covered - Are exceptions handled?
- [ ] Length specified - Is output length clear?
- [ ] Tone/style defined - Is the desired voice specified?
Common Prompt Problems & Fixes
Problem: Vague Instructions
Before:
After:
Problem: No Examples (When Needed)
Before:
After:
Problem: Missing Output Format
Before:
After:
Problem: Too Complex (Single Shot)
Before:
After (Progressive):
Using This Skill
Generate a New Prompt
Ask:
You'll get:
- Structured prompt template
- Recommended techniques
- Example few-shots if applicable
- Model-specific optimizations
Improve an Existing Prompt
Ask:
You'll get:
- Analysis of current issues
- Improved version
- Explanation of changes
- Expected improvement in results
Analyze Prompt Quality
Ask:
You'll get:
- Quality score
- Identified weaknesses
- Specific improvement suggestions
- Best practices violations
Real-World Examples
Example 1: Code Review Prompt
Task: Get thorough, consistent code reviews
Optimized Prompt:
Example 2: Technical Documentation
Task: Generate clear API documentation
Optimized Prompt:
This is the start of the file. Read the full SKILL.md on GitHub.
Security
- Security scanPassed, 100 of 100Pattern checks on the skill's files.
- Model reviewNot runA model review is scheduled for the next rescan.
Quality evaluation
- Task evaluationNot runNo evaluation yet. Run one in Skill Studio.
Source
- SKILL.md in the repositoryCheckingskills/openai-prompt-engineer/SKILL.md
Popularity
- Repository stars
- 17
- Repository forks
- 3
- 7-day trend score
- 14
Stars and forks are for the whole GitHub repository, not this skill alone. Refreshed 29 Mar 2026, 7 months ago.
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