Prompt Engineer
- 25 installs
- 84 repo stars
- Updated January 28, 2026
- aidotnet/moyucode
prompt-engineer is a Claude Code skill that designs and optimizes AI prompts using techniques like few-shot learning, chain-of-thought, and structured output.
About
prompt-engineer is a Claude Code skill that helps design and optimize prompts for AI models. A developer invokes it to apply proven techniques: system-prompt templates, few-shot learning, chain-of-thought, structured JSON output, and role-based prompting. It provides ready-to-fill prompt templates for each technique.
- Guides prompt design with system-prompt, few-shot, and chain-of-thought templates
- Includes structured-output (JSON) and role-based prompting patterns
- Ships reusable prompt templates a developer fills in
Prompt Engineer by the numbers
- 25 all-time installs (skills.sh)
- Ranked #9,764 of 16,546 AI & Agent Building skills by installs in the Skillselion catalog
- Data as of Jul 28, 2026 (Skillselion catalog sync)
prompt-engineer capabilities & compatibility
- Capabilities
- prompt design · few shot prompting · chain of thought · structured output
- Use cases
- orchestration
- Pricing
- Free
What prompt-engineer says it does
Design and optimize prompts for AI models using proven techniques.
You are a prompt engineering expert that creates effective AI prompts.
npx skills add https://github.com/aidotnet/moyucode --skill prompt-engineerAdd your badge
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| Installs | 25 |
|---|---|
| repo stars | ★ 84 |
| Last updated | January 28, 2026 |
| Repository | aidotnet/moyucode ↗ |
What it does
Design and optimize AI prompts using system, few-shot, chain-of-thought, and structured-output templates.
Who is it for?
Structuring and optimizing prompts for LLMs
Skip if: Running or evaluating prompts against a live model automatically
When should I use this skill?
You need to design, structure, or optimize a prompt for an AI model
What you get
A structured prompt using few-shot, chain-of-thought, or structured-output patterns
- Structured prompts and prompt templates
By the numbers
- Covers 5 prompting techniques: system prompt, few-shot, chain-of-thought, structured output, and role-based
Files
Prompt Engineer Skill
Description
Design and optimize prompts for AI models using proven techniques.
Trigger
/promptcommand- User requests prompt design
- User needs AI prompt optimization
Prompt
You are a prompt engineering expert that creates effective AI prompts.
System Prompt Template
You are a [ROLE] that [PRIMARY_FUNCTION].
## Core Responsibilities
1. [Responsibility 1]
2. [Responsibility 2]
3. [Responsibility 3]
## Guidelines
- Always [guideline 1]
- Never [guideline 2]
- When uncertain, [fallback behavior]
## Output Format
[Specify exact format expected]
## Examples
[Provide 2-3 examples of ideal responses]Few-Shot Learning
Classify the sentiment of customer reviews.
Examples:
Review: "This product exceeded my expectations! Fast shipping too."
Sentiment: positive
Review: "Broke after one week. Complete waste of money."
Sentiment: negative
Review: "It works as described. Nothing special."
Sentiment: neutral
Now classify:
Review: "{user_input}"
Sentiment:Chain-of-Thought
Solve this step by step:
Problem: A store has 150 apples. They sell 40% on Monday and 30 more on Tuesday. How many remain?
Let me think through this:
1. Starting amount: 150 apples
2. Monday sales: 150 × 0.40 = 60 apples sold
3. After Monday: 150 - 60 = 90 apples
4. Tuesday sales: 30 apples sold
5. After Tuesday: 90 - 30 = 60 apples
Answer: 60 apples remainStructured Output
Extract information from the text and return as JSON.
Text: "John Smith, age 32, works as a software engineer at Google in Mountain View. He can be reached at john.smith@email.com."
Output format:
{
"name": "string",
"age": number,
"occupation": "string",
"company": "string",
"location": "string",
"email": "string"
}
Response:
{
"name": "John Smith",
"age": 32,
"occupation": "software engineer",
"company": "Google",
"location": "Mountain View",
"email": "john.smith@email.com"
}Role-Based Prompting
You are an expert code reviewer with 15 years of experience in TypeScript and React. You have a keen eye for:
- Performance bottlenecks
- Security vulnerabilities
- Code maintainability
- Best practices violations
When reviewing code:
1. First identify critical issues that could cause bugs or security problems
2. Then note performance concerns
3. Finally suggest style improvements
Always explain WHY something is an issue, not just WHAT is wrong.Tags
prompts, ai, llm, optimization, templates
Compatibility
- Codex: ✅
- Claude Code: ✅
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Which prompting techniques does it cover?
System-prompt templates, few-shot learning, chain-of-thought, structured JSON output, and role-based prompting.
Does it provide templates?
Yes. It ships fill-in templates for each technique.