prompt-engineer

Original：🇺🇸 English

Translated

Expert in designing, optimizing, and evaluating prompts for Large Language Models. Specializes in Chain-of-Thought, ReAct, few-shot learning, and production prompt management. Use when crafting prompts, optimizing LLM outputs, or building prompt systems. Triggers include "prompt engineering", "prompt optimization", "chain of thought", "few-shot", "prompt template", "LLM prompting".

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Source404kidwiz/claude-supercode-skills

Added on2026-02-15

NPX Install

npx skill4agent add 404kidwiz/claude-supercode-skills prompt-engineer

SKILL.md Content

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Prompt Engineer

Purpose

Provides expertise in designing, optimizing, and evaluating prompts for Large Language Models. Specializes in prompting techniques like Chain-of-Thought, ReAct, and few-shot learning, as well as production prompt management and evaluation.

When to Use

Designing prompts for LLM applications
Optimizing prompt performance
Implementing Chain-of-Thought reasoning
Creating few-shot examples
Building prompt templates
Evaluating prompt effectiveness
Managing prompts in production
Reducing hallucinations through prompting

Quick Start

Invoke this skill when:

Crafting prompts for LLM applications
Optimizing existing prompts
Implementing advanced prompting techniques
Building prompt management systems
Evaluating prompt quality

Do NOT invoke when:

LLM system architecture → use
```
/llm-architect
```
RAG implementation → use
```
/ai-engineer
```
NLP model training → use
```
/nlp-engineer
```
Agent performance monitoring → use
```
/performance-monitor
```

Decision Framework

Prompting Technique?
├── Reasoning Tasks
│   ├── Step-by-step → Chain-of-Thought
│   └── Tool use → ReAct
├── Classification/Extraction
│   ├── Clear categories → Zero-shot + examples
│   └── Complex → Few-shot with edge cases
├── Generation
│   └── Structured output → JSON mode + schema
└── Consistency
    └── System prompt + temperature tuning

Core Workflows

1. Prompt Design

Define task clearly
Choose prompting technique
Write system prompt with context
Add examples if few-shot
Specify output format
Test with diverse inputs

2. Chain-of-Thought Implementation

Identify reasoning requirements
Add "Let's think step by step" or equivalent
Provide reasoning examples
Structure expected reasoning steps
Test reasoning quality
Iterate on step guidance

3. Prompt Optimization

Establish baseline metrics
Identify failure patterns
Adjust instructions for clarity
Add/modify examples
Tune output constraints
Measure improvement

Best Practices

Be specific and explicit in instructions
Use structured output formats (JSON, XML)
Include examples for complex tasks
Test with edge cases and adversarial inputs
Version control prompts
Measure and track prompt performance

Anti-Patterns

Anti-Pattern	Problem	Correct Approach
Vague instructions	Inconsistent output	Be specific and explicit
No examples	Poor performance on complex tasks	Add few-shot examples
Unstructured output	Hard to parse	Specify format clearly
No testing	Unknown failure modes	Test diverse inputs
Prompt in code	Hard to iterate	Separate prompt management