prompt-engineer

name	prompt-engineer
description	Create optimized, production-ready prompts for any AI model or use case. TRIGGERS - Use this skill when: - User wants to create or improve AI prompts - User mentions prompt engineering, prompt writing, or system prompts - User wants to optimize prompts for better AI outputs - User asks for help with ChatGPT, Claude, or other AI model prompts

Prompt Engineer

Overview

Creates structured, optimized prompts that get consistent, high-quality outputs from any AI model. Covers system prompts, user prompts, chain-of-thought, few-shot examples, and output formatting.

Workflow

Step 1: Understand the Use Case

Ask the user:

1. What should the AI do? (specific task)
2. What model? (Claude, GPT-4, Llama, etc.)
3. Where will it run? (chatbot, automation, API, manual use)
4. What does good output look like? (example if available)
5. What does bad output look like? (common failures to avoid)

Step 2: Choose the Prompt Architecture

Architecture	When to Use
Single prompt	Simple, one-shot tasks
System + User prompt	Chatbots, consistent behavior
Chain of thought	Complex reasoning, analysis
Few-shot	When format/style must be exact
Multi-step chain	Complex workflows, agents

Step 3: Build the Prompt

Core Components:

1. ROLE — Who is the AI?
2. CONTEXT — What background does it need?
3. TASK — What exactly should it do?
4. FORMAT — How should output be structured?
5. CONSTRAINTS — What to avoid or limit?
6. EXAMPLES — What does good output look like?

Prompt Template:

You are a [ROLE] with expertise in [DOMAIN].

## Context
[Background information the AI needs to know]

## Task
[Exactly what the AI should do — be specific]

## Input
[What the user/system will provide]
{{variable_name}}

## Output Format
[Exact structure expected]
- Format: [JSON/markdown/plain text]
- Length: [word count or structure]
- Tone: [professional/casual/technical]

## Rules
- [Constraint 1]
- [Constraint 2]
- [What to NEVER do]

## Examples

### Input:
[Example input]

### Expected Output:
[Example output]

### Input:
[Second example — different case]

### Expected Output:
[Second example output]

Step 4: Optimize

Apply these optimization techniques:

1. Be specific: "Write 3 bullet points" > "Write some points"
2. Use positive instructions: "Include metrics" > "Don't forget metrics"
3. Add guardrails: "If unsure, say 'I need more information about X'"
4. Structure with XML/markdown: Use headers and tags for clarity
5. Test edge cases: What happens with weird input?
6. Add fallback instructions: What to do when input is incomplete

Step 5: Test & Iterate

Provide 3 test inputs:

1. Happy path — typical, well-formed input
2. Edge case — unusual or minimal input
3. Adversarial — tricky input that might break it

Output Format

# Prompt: [Use Case Name]

## Model: [recommended model]
## Architecture: [single/system+user/chain/few-shot]

---

## System Prompt

[Full system prompt]

## User Prompt Template

[User prompt with {{variables}}]

## Variables
| Variable | Type | Description | Example |
|----------|------|-------------|---------|
| {{var1}} | string | ... | ... |

## Test Cases

### Test 1: Happy Path
**Input**: [test input]
**Expected behavior**: [what should happen]

### Test 2: Edge Case
**Input**: [edge case input]
**Expected behavior**: [what should happen]

### Test 3: Adversarial
**Input**: [tricky input]
**Expected behavior**: [what should happen]

## Integration Notes
- [How to use in Zapier/Make/n8n]
- [API call example if applicable]
- [Token/cost estimate per call]

Quality Checklist

• Role is specific (not generic "helpful assistant")
• Task is unambiguous (one interpretation only)
• Output format is explicitly defined
• At least 2 examples included
• Edge cases handled with fallback instructions
• Variables are clearly marked with {{brackets}}
• Tested with 3 different inputs