Openai Prompt Engineer

"Write some code for user authentication"

"Write a TypeScript class called AuthService that:
- Accepts email/password credentials
- Validates against a User repository
- Returns a JWT token on success
- Throws AuthenticationError on failure
- Includes comprehensive JSDoc comments
- Follows dependency injection pattern"

"Convert these variable names to camelCase"

"Convert these variable names to camelCase:

user_name → userName
total_count → totalCount
is_active → isActive

Now convert:
order_status →
created_at →
max_retry_count →"

"Analyze this code for problems"

"Analyze this code and output in this format:

## Security Issues
- [Issue]: [Description] (Line X)

## Performance Issues
- [Issue]: [Description] (Line X)

## Code Quality
- [Issue]: [Description] (Line X)

## Recommendations
1. [Priority 1 fix]
2. [Priority 2 fix]"

"Build a complete e-commerce backend with authentication,
payments, inventory, and shipping"

"Let's build this in stages:

Stage 1: Design the authentication system architecture
[Get response, review]

Stage 2: Implement the auth service
[Get response, review]

Stage 3: Add payment processing
[Continue...]"

"Using the prompt-engineer skill, create a prompt for:
[Describe your task and requirements]"

"Using the prompt-engineer skill, improve this prompt:

[Your current prompt]

Goal: [What you want to achieve]
Model: [GPT-5 / Claude / Other]"

"Using the prompt-engineer skill, analyze this prompt:
[Your prompt]"

ROLE: Senior Software Engineer conducting PR review

REVIEW THIS CODE:
[code block]

REVIEW CRITERIA:
1. Security vulnerabilities (OWASP Top 10)
2. Performance issues
3. Code quality and readability
4. Best practices compliance
5. Test coverage gaps

OUTPUT FORMAT:
For each issue found:
- Severity: [Critical/High/Medium/Low]
- Category: [Security/Performance/Quality/Testing]
- Location: [File:Line]
- Issue: [Clear description]
- Impact: [Why this matters]
- Fix: [Specific code recommendation]

At the end, provide:
- Overall assessment (Approve/Request Changes/Comment)
- Summary of critical items that must be fixed

ROLE: Technical writer with API documentation expertise

TASK: Generate API documentation for this endpoint

ENDPOINT DETAILS:
[code/specs]

DOCUMENTATION REQUIREMENTS:
- Target audience: Junior to mid-level developers
- Include curl and JavaScript examples
- Explain all parameters clearly
- Show example responses with descriptions
- Include common error cases
- Add troubleshooting section

FORMAT:
# [Endpoint Name]

## Overview
[One paragraph description]

## Endpoint
`[HTTP METHOD] /path`

## Parameters
| Name | Type | Required | Description |
|------|------|----------|-------------|

## Request Example
```bash
[curl example]

[example with inline comments]

### Example 3: Data Analysis

**Task:** Analyze data and provide insights

**Optimized Prompt:**

## Best Practices Summary

### DO ✅

- **Be specific** - Exact requirements, not vague requests
- **Use structure** - Organize with clear sections
- **Provide examples** - Show what you want (few-shot)
- **Request reasoning** - "Think step-by-step" for complex tasks
- **Define format** - Specify exact output structure
- **Test iteratively** - Refine based on results
- **Match to model** - Use model-specific techniques
- **Include context** - Give necessary background
- **Handle edge cases** - Specify exception handling
- **Set constraints** - Define limitations clearly

### DON'T ❌

- **Be vague** - "Write something about X"
- **Skip examples** - When patterns need to be matched
- **Assume format** - Model will choose unpredictably
- **Overload single prompt** - Break complex tasks into stages
- **Ignore model differences** - GPT-5 and Claude need different approaches
- **Give up too soon** - Iterate on prompts
- **Mix instructions** - Keep separate concerns separate
- **Forget constraints** - Specify ALL requirements
- **Use ambiguous terms** - "Good", "professional", "better" without definition
- **Skip testing** - Always validate outputs

## Quick Reference

### Prompt Template (Universal)

### When to Use Each Technique

| Technique | Best For | Example Use Case |
|-----------|----------|------------------|
| Chain-of-Thought | Complex reasoning | Math, logic puzzles, multi-step analysis |
| Few-Shot | Pattern matching | Classification, style transfer, formatting |
| Zero-Shot | Simple, clear tasks | Direct questions, basic transformations |
| Structured (XML) | Parsed output | Data extraction, API responses |
| Progressive Disclosure | Large tasks | Full implementations, research |
| Role-Based | Expert knowledge | Code review, architecture decisions |

### Model Selection Guide

**Use GPT-5 when:**
- Need strong reasoning
- Agentic behavior helpful
- Code generation focus
- Latest knowledge needed

**Use Claude when:**
- Very long context (100K+ tokens)
- Detailed instruction following
- Safety-critical applications
- Prefer XML structuring

## Resources

All reference materials included:
- GPT-5 specific techniques and patterns
- Claude optimization strategies
- Advanced prompting patterns
- Optimization and improvement frameworks

## Summary

Effective prompt engineering:
- **Saves time** - Get right results faster
- **Reduces costs** - Fewer API calls needed
- **Improves quality** - More accurate, consistent outputs
- **Enables complexity** - Tackle harder problems
- **Scales knowledge** - Capture best practices

Use this skill to create prompts that:
- Are clear and specific
- Use proven techniques
- Match your model
- Get consistent results
- Achieve your goals

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**Remember:** A well-crafted prompt is worth 10 poorly-attempted ones. Invest time upfront for better results.