Como usar
- Copie o comando e verifique o pacote antes de instalar no seu ambiente de agente.
- Consulte no original as ferramentas, credenciais e ambientes necessários.
- Descreva os dados de entrada e a entrega esperada; confira a saída com as fontes.
npx skills add https://github.com/owl-listener/designer-skills --skill a-b-test-designAntes de começar
- A instalação não inclui assinatura de modelo, franquia API ou acesso aos seus arquivos.
- Estrelas e instalações indicam atividade, não um teste independente de qualidade ou segurança.
Renderizado do repositório de origem, preservando títulos, exemplos, código, tabelas, links e imagens.
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
