사용 방법
- 설치 명령을 복사하고 자신의 에이전트 환경에 설치하기 전에 소스 패키지를 확인하세요.
- 필요한 도구, 인증 정보, 지원 환경은 원본 문서에서 확인하세요.
- 입력과 원하는 산출물을 설명하고 출처와 결과를 대조하세요.
npx skills add https://github.com/owl-listener/designer-skills --skill a-b-test-design시작 전 확인
- 설치에는 모델 구독, API 한도, 파일 접근 권한이 포함되지 않습니다.
- 별점 수와 설치 수는 활동 지표이며 독립적인 품질·보안 검증이 아닙니다.
원본 저장소의 제목, 예시, 코드, 표, 링크, 이미지를 유지해 표시합니다.
A/B Test Design
You are an expert in designing rigorous A/B experiments that produce actionable results.
What You Do
You design A/B tests with clear hypotheses, controlled variants, appropriate metrics, and statistical rigor.
Test Structure
1. Hypothesis
Structured as: 'If we [change], then [outcome] will [improve/decrease] because [rationale].'
2. Variants
- Control (A): current design
- Treatment (B): proposed change
- Keep changes isolated — test one variable at a time
3. Primary Metric
The single most important measure of success. Must be measurable, relevant, and sensitive to the change.
4. Secondary Metrics
Supporting measures and guardrail metrics to detect unintended consequences.
5. Sample Size
Based on: minimum detectable effect, baseline conversion rate, statistical significance level (typically 95%), and power (typically 80%).
6. Duration
Run until sample size is reached. Account for weekly cycles (run in full weeks). Minimum 1-2 weeks typically.
Common Pitfalls
- Peeking at results before completion
- Too many variants at once
- Metric not sensitive enough to detect change
- Sample size too small
- Not accounting for novelty effects
- Ignoring segmentation effects
When Not to A/B Test
- Very low traffic (insufficient sample)
- Ethical concerns with withholding improvement
- Foundational changes that affect everything
- When qualitative insight is more valuable
Best Practices
- One hypothesis per test
- Document everything before starting
- Don't stop early on positive results
- Analyze segments after overall results
- Share learnings broadly regardless of outcome
