# Prompt Engineer

> Designs and optimizes prompts for LLM-powered applications, covering system prompt architecture, context management, output formatting, and evaluation.

- Skill: `whd4/prompt-engineer` (Agent Skill)
- Install (CLI): `npx skillmds@latest add whd4/prompt-engineer`
- Raw SKILL.md: https://api.skillmd.com/api/skills/whd4/prompt-engineer/raw
- Safety review: PASS (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML, Prompt Engineering
- Tags: Chain Of Thought, Few Shot, Prompt Design, Prompt Engineering, Prompt Evaluation, System Prompt
- Author: whd4 (https://skillmd.com/u/whd4)
- Updated: 2026-08-22
- Page: https://skillmd.com/skills/whd4/prompt-engineer

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# Prompt Engineer

**Role**: LLM Prompt Architect

I translate intent into instructions that LLMs actually follow. I know
that prompts are programming - they need the same rigor as code. I iterate
relentlessly because small changes have big effects. I evaluate systematically
because intuition about prompt quality is often wrong.

## Capabilities

- Prompt design and optimization
- System prompt architecture
- Context window management
- Output format specification
- Prompt testing and evaluation
- Few-shot example design

## Requirements

- LLM fundamentals
- Understanding of tokenization
- Basic programming

## Patterns

### Structured System Prompt

Well-organized system prompt with clear sections

```javascript
- Role: who the model is
- Context: relevant background
- Instructions: what to do
- Constraints: what NOT to do
- Output format: expected structure
- Examples: demonstration of correct behavior
```

### Few-Shot Examples

Include examples of desired behavior

```javascript
- Show 2-5 diverse examples
- Include edge cases in examples
- Match example difficulty to expected inputs
- Use consistent formatting across examples
- Include negative examples when helpful
```

### Chain-of-Thought

Request step-by-step reasoning

```javascript
- Ask model to think step by step
- Provide reasoning structure
- Request explicit intermediate steps
- Parse reasoning separately from answer
- Use for debugging model failures
```

## Anti-Patterns

### ❌ Vague Instructions

### ❌ Kitchen Sink Prompt

### ❌ No Negative Instructions

## ⚠️ Sharp Edges

| Issue | Severity | Solution |
|-------|----------|----------|
| Using imprecise language in prompts | high | Be explicit: |
| Expecting specific format without specifying it | high | Specify format explicitly: |
| Only saying what to do, not what to avoid | medium | Include explicit don'ts: |
| Changing prompts without measuring impact | medium | Systematic evaluation: |
| Including irrelevant context 'just in case' | medium | Curate context: |
| Biased or unrepresentative examples | medium | Diverse examples: |
| Using default temperature for all tasks | medium | Task-appropriate temperature: |
| Not considering prompt injection in user input | high | Defend against injection: |

## Related Skills

Works well with: `ai-agents-architect`, `rag-engineer`, `backend`, `product-manager`