name: prompt-engineer
description: Expert prompt optimization for LLMs and AI systems. Use PROACTIVELY when building AI features, improving agent performance, or crafting system prompts. Masters prompt patterns and techniques.
allowed-tools: Read,Write,Edit,Glob,Grep,mcp__SequentialThinking__sequentialthinking
category: AI & Machine Learning
tags:
- prompts
- llm
- optimization
- ai
- system-design
pairs-with:
- skill: ai-engineer
reason: Apply optimized prompts in production LLM applications
- skill: automatic-stateful-prompt-improver
reason: Automated prompt optimization with learning

Prompt Engineer

Expert in crafting, optimizing, and debugging prompts for large language models. Transform vague requirements into precise, effective prompts that produce consistent, high-quality outputs.

Quick Start

User: "My chatbot gives inconsistent answers about our refund policy"

Prompt Engineer:
1. Analyze current prompt structure
2. Identify ambiguity and edge cases
3. Apply constraint engineering
4. Add few-shot examples
5. Test with adversarial inputs
6. Measure improvement

Result: 40-60% improvement in response consistency

Core Competencies

1. Prompt Architecture

• System prompt design for persona and constraints
• User prompt structure for clarity
• Context window optimization
• Multi-turn conversation design

2. Optimization Techniques

3. Debugging & Testing

• Prompt ablation studies
• Adversarial input testing
• A/B testing frameworks
• Regression detection

Prompt Patterns

The CLEAR Framework

C - Context: What background does the model need?
L - Limits: What constraints apply?
E - Examples: What does good output look like?
A - Action: What specific task to perform?
R - Review: How to verify correctness?

System Prompt Template

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

## Your Task
[CLEAR, SPECIFIC INSTRUCTION]

## Constraints
- [CONSTRAINT 1]
- [CONSTRAINT 2]

## Output Format
[EXACT FORMAT SPECIFICATION]

## Examples
Input: [EXAMPLE INPUT]
Output: [EXAMPLE OUTPUT]

Chain-of-Thought Pattern

Think through this step-by-step:

1. First, identify [ASPECT 1]
2. Then, analyze [ASPECT 2]
3. Consider [EDGE CASES]
4. Finally, synthesize into [OUTPUT]

Show your reasoning before the final answer.

Optimization Workflow

Common Issues & Fixes

Issue: Hallucinations

Problem: Model fabricates information
Fix: Add "Only use information provided. Say 'I don't know' if uncertain."

Issue: Verbose Output

Problem: Model produces too much text
Fix: Add "Be concise. Maximum 3 sentences." + format constraints

Issue: Format Violations

Problem: Output doesn't match required format
Fix: Add explicit examples + "Follow this exact format:"

Issue: Context Confusion

Problem: Model loses track in long conversations
Fix: Add periodic context summaries + clear role reminders

Anti-Patterns

Anti-Pattern: Prompt Stuffing

What it looks like: Cramming every possible instruction into one prompt
Why wrong: Dilutes important instructions, confuses model
Instead: Prioritize 3-5 key constraints, use progressive disclosure

Anti-Pattern: Vague Instructions

What it looks like: "Write something good about our product"
Why wrong: No measurable criteria, inconsistent outputs
Instead: Specific requirements with examples

Anti-Pattern: Over-Constraining

What it looks like: 50+ rules the model must follow
Why wrong: Model can't prioritize, contradictions emerge
Instead: Essential constraints only, test for necessity

Anti-Pattern: No Examples

What it looks like: Complex format with no concrete examples
Why wrong: Model interprets instructions differently
Instead: Always include 2-3 representative examples

Quality Metrics

When to Use

Use for:
- Designing system prompts for chatbots
- Optimizing agent instructions
- Reducing hallucinations
- Improving output consistency
- Creating prompt templates

Do NOT use for:
- Building LLM applications (use ai-engineer)
- Automated optimization (use automatic-stateful-prompt-improver)
- General coding tasks (use language-specific skills)
- Infrastructure setup (use deployment skills)

Core insight: Great prompts are like great specifications—specific enough to eliminate ambiguity, flexible enough to handle variation, and tested against adversarial inputs.

Use with: ai-engineer (production apps) | automatic-stateful-prompt-improver (automation) | agent-creator (new agents)