Overview
- Skill Key
- boleyn/ocms-ab-test-setup
- Author
- boleyn
- Source Repo
- openclaw/skills
- Version
- -
- Source Path
- skills/boleyn/ocms-ab-test-setup
- Latest Commit SHA
- 2da0d1e5455f6ca448fe00b13fee148160dd8920
Official OpenClaw
rules
36%
ab-test-setup
When the user wants to plan, design, or implement an A/B test or experiment. Also use when the user mentions "A/B test," "split test," "experiment," "test this change," "variant copy," "multivariate test," or "hypothesis." For tracking implementation, see analytics-tracking.
Stars
0
Installs
0
Status
ACTIVE
Visibility
PUBLIC
安装方式
直接复制以下提示词，发送给你的 AI 助手即可完成安装。
请先检查是否已安装 SkillHub 商店，若未安装，请根据 https://skillhub-1388575217.cos.ap-guangzhou.myqcloud.com/install/skillhub.md 安装 SkillHub 商店，然后安装 ab-test-setup 技能。 若已安装，则直接安装 ab-test-setup 技能。
Extracted Content
SKILL.md excerpt
# A/B Test Setup You are an expert in experimentation and A/B testing. Your goal is to help design tests that produce statistically valid, actionable results. ## Initial Assessment **Check for product marketing context first:** If `.agents/product-marketing-context.md` exists (or `.claude/product-marketing-context.md` in older setups), read it before asking questions. Use that context and only ask for information not already covered or specific to this task. Before designing a test, understand: 1. **Test Context** - What are you trying to improve? What change are you considering? 2. **Current State** - Baseline conversion rate? Current traffic volume? 3. **Constraints** - Technical complexity? Timeline? Tools available? --- ## Core Principles ### 1. Start with a Hypothesis - Not just "let's see what happens" - Specific prediction of outcome - Based on reasoning or data ### 2. Test One Thing - Single variable per test - Otherwise you don't know what worked ### 3. Statistical Rigor - Pre-determine sample size - Don't peek and stop early - Commit to the methodology ### 4. Measure What Matters - Primary metric tied to business value - Secondary metrics for context - Guardrail metrics to prevent harm --- ## Hypothesis Framework ### Structure ``` Because [observation/data], we believe [change] will cause [expected outcome] for [audience]. We'll know this is true when [metrics]. ``` ### Example **Weak**: "Changing the button color might increase clicks." **Strong**: "Because users report difficulty finding the CTA (per heatmaps and feedback), we believe making the button larger and using contrasting color will increase CTA clicks by 15%+ for new visitors. We'll measure click-through rate from page view to signup start." --- ## Test Types | Type | Description | Traffic Needed | |------|-------------|----------------| | A/B | Two versions, single change | Moderate | | A/B/n | Multiple variants | Higher | | MVT | Multiple changes in combinations | Very hi...
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