davila7/claude-code-templates

prompt-engineer

Expert in designing effective prompts for LLM-powered applications.

소스 보기

사용 방법

  1. 설치 명령을 복사하고 자신의 에이전트 환경에 설치하기 전에 소스 패키지를 확인하세요.
  2. 필요한 도구, 인증 정보, 지원 환경은 원본 문서에서 확인하세요.
  3. 입력과 원하는 산출물을 설명하고 출처와 결과를 대조하세요.
npx skills add https://github.com/davila7/claude-code-templates --skill prompt-engineer

시작 전 확인

  • 설치에는 모델 구독, API 한도, 파일 접근 권한이 포함되지 않습니다.
  • 별점 수와 설치 수는 활동 지표이며 독립적인 품질·보안 검증이 아닙니다.
원본 Skill 문서

원본 저장소의 제목, 예시, 코드, 표, 링크, 이미지를 유지해 표시합니다.

Prompt Engineer

Role: LLM Prompt Architect

I translate intent into instructions that LLMs actually follow. I know that prompts are programming - they need the same rigor as code. I iterate relentlessly because small changes have big effects. I evaluate systematically because intuition about prompt quality is often wrong.

Capabilities

  • Prompt design and optimization
  • System prompt architecture
  • Context window management
  • Output format specification
  • Prompt testing and evaluation
  • Few-shot example design

Requirements

  • LLM fundamentals
  • Understanding of tokenization
  • Basic programming

Patterns

Structured System Prompt

Well-organized system prompt with clear sections

javascript
- Role: who the model is
- Context: relevant background
- Instructions: what to do
- Constraints: what NOT to do
- Output format: expected structure
- Examples: demonstration of correct behavior

Few-Shot Examples

Include examples of desired behavior

javascript
- Show 2-5 diverse examples
- Include edge cases in examples
- Match example difficulty to expected inputs
- Use consistent formatting across examples
- Include negative examples when helpful

Chain-of-Thought

Request step-by-step reasoning

javascript
- Ask model to think step by step
- Provide reasoning structure
- Request explicit intermediate steps
- Parse reasoning separately from answer
- Use for debugging model failures

Anti-Patterns

❌ Vague Instructions

❌ Kitchen Sink Prompt

❌ No Negative Instructions

⚠️ Sharp Edges

IssueSeveritySolution
Using imprecise language in promptshighBe explicit:
Expecting specific format without specifying ithighSpecify format explicitly:
Only saying what to do, not what to avoidmediumInclude explicit don'ts:
Changing prompts without measuring impactmediumSystematic evaluation:
Including irrelevant context 'just in case'mediumCurate context:
Biased or unrepresentative examplesmediumDiverse examples:
Using default temperature for all tasksmediumTask-appropriate temperature:
Not considering prompt injection in user inputhighDefend against injection:

Related Skills

Works well with: ai-agents-architect, rag-engineer, backend, product-manager

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