prompt-engineer

license	Apache-2.0
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
metadata	{"category":"AI & Machine Learning","tags":["prompt-engineering","llm","optimization","few-shot","chain-of-thought"],"provenance":{"kind":"first-party","owners":["port-daddy"]},"pairs-with":[{"skill":"agentic-app-architecture","reason":"Decides the app's transparency, memory, and execution shape first; this skill fills that shape in with the actual prompt wording once it exists."},{"skill":"llm-router","reason":"Router picks the model tier by task complexity; this skill's prompt design (length, few-shot count, reasoning depth) is a primary input to that complexity estimate."},{"skill":"output-contract-enforcer","reason":"Runtime-validates that the prompt's declared output contract (schema/format) is actually what the model returns, closing the loop this skill's audit only checks statically."},{"skill":"ai-engineer","reason":"The prompt is one component of the larger LLM app that skill architects (RAG, agents, eval harness, guardrails)."}],"io-contract":{"kind":"deliverable","consumes":["[Truncated]","[Truncated]","[Truncated]"],"produces":["[Truncated]","[Truncated]","[Truncated]"]}}

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

Technique	When to Use	Expected Improvement
Chain-of-Thought	Complex reasoning	20-40% accuracy
Few-Shot Examples	Format consistency	30-50% reliability
Constraint Engineering	Edge case handling	50%+ consistency
Role Prompting	Domain expertise	15-25% quality
Self-Consistency	Critical decisions	10-20% accuracy

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]