/prompt-engineering

name: prompt-engineering
description: >
  Provides workflows to write, debug, and optimize prompts for LLMs, including
  few-shot example selection, chain-of-thought structuring, system prompt
  design, and template composition. Use when the user asks to write or improve
  a prompt, wants help with few-shot examples, chain-of-thought, system prompts,
  prompt templates, or asks how to get better results from an LLM.
allowed-tools: Read, Write, Edit, Glob, Grep, Bash

Prompt Engineering

Overview

Use this skill to design prompt systems that are clear, testable, and reusable. It covers prompt drafting, optimization, evaluation, and production-oriented patterns for few-shot prompting, reasoning workflows, templates, and system prompts.

Keep the main workflow in this file and load the targeted reference files only for the pattern you are applying.

When to Use

Use this skill when:

• A user asks to write, rewrite, or improve a prompt
• A prompt needs better structure, reliability, or output formatting
• Few-shot examples or reasoning scaffolds are needed
• A system prompt or reusable prompt template must be created
• An existing prompt needs measurable optimization and testing

Read the relevant files in `references/` when you need deeper guidance on a specific pattern.

Core Patterns

1. Few-Shot Learning

Example Selection Strategy

• Use `references/few-shot-patterns.md` for comprehensive selection frameworks
• Balance example count (3-5 optimal) with context window limitations
• Include edge cases and boundary conditions in example sets
• Prioritize diverse examples that cover problem space variations
• Order examples from simple to complex for progressive learning

Few-Shot Example (Sentiment Classification)

Classify the sentiment as Positive, Negative, or Neutral.

Text: "I love this product! It exceeded my expectations."
Sentiment: Positive
Reasoning: Enthusiastic language, positive adjectives, satisfaction

Text: "The app keeps crashing when I upload large files."
Sentiment: Negative
Reasoning: Complaint about functionality, frustration indicator

Text: "It arrived on time, as described."
Sentiment: Neutral
Reasoning: Factual statement, no strong emotion either way

Text: "{user_input}"
Sentiment:
Reasoning:

2. Chain-of-Thought Reasoning

Implementation Patterns

• Reference `references/cot-patterns.md` for detailed reasoning frameworks
• Use "Let's think step by step" for zero-shot CoT initiation
• Provide complete reasoning traces for few-shot CoT demonstrations
• Implement self-consistency by sampling multiple reasoning paths
• Include verification and validation steps in reasoning chains

CoT Template Structure

Let's approach this step-by-step:

Step 1: {break_down_the_problem}
Analysis: {detailed_reasoning}

Step 2: {identify_key_components}
Analysis: {component_analysis}

Step 3: {synthesize_solution}
Analysis: {solution_justification}

Final Answer: {conclusion_with_confidence}

3. Prompt Optimization

Optimization Process

• Use `references/optimization-frameworks.md` for comprehensive optimization strategies
• Measure baseline performance before optimization attempts
• Implement single-variable changes for accurate attribution
• Track metrics: accuracy, consistency, latency, token efficiency
• Use statistical significance testing for A/B validation
• Document optimization iterations and their impacts

Track these metrics: accuracy, consistency, token efficiency, robustness, safety. See `references/optimization-frameworks.md` for measurement utilities.

4. Template Systems

Template Design Principles

• Reference `references/template-systems.md` for modular template frameworks
• Use clear variable naming conventions (e.g., `{user_input}`, `{context}`)
• Implement conditional sections for different scenario handling
• Design role-based templates for specific use cases
• Create hierarchical template composition patterns

Template Structure Example

# System Context
You are a {role} with {expertise_level} expertise in {domain}.

# Task Context
{if background_information}
Background: {background_information}
{endif}

# Instructions
{task_instructions}

# Examples
{example_count}

# Output Format
{output_specification}

# Input
{user_query}

5. System Prompt Design

System Prompt Components

• Use `references/system-prompt-design.md` for detailed design guidelines
• Define clear role specification and expertise boundaries
• Establish output format requirements and structural constraints
• Include safety guidelines and content policy adherence
• Set context for background information and domain knowledge

System Prompt Framework

You are an expert {role} specializing in {domain} with {experience_level} of experience.

## Core Capabilities
- List specific capabilities and expertise areas
- Define scope of knowledge and limitations

## Behavioral Guidelines
- Specify interaction style and communication approach
- Define error handling and uncertainty protocols
- Establish quality standards and verification requirements

## Output Requirements
- Specify format expectations and structural requirements
- Define content inclusion and exclusion criteria
- Establish consistency and validation requirements

## Safety and Ethics
- Include content policy adherence
- Specify bias mitigation requirements
- Define harm prevention protocols

Implementation Workflows

Workflow 1: Create New Prompt from Requirements

1. **Analyze Requirements**

• Identify task complexity and reasoning requirements
• Determine target model capabilities and limitations
• Define success criteria and evaluation metrics
• Assess need for few-shot learning or CoT reasoning

2. **Select Pattern Strategy**

• Use few-shot learning for classification or transformation tasks
• Apply CoT for complex reasoning or multi-step problems
• Implement template systems for reusable prompt architec