send-experiment-designer
FeaturedUse when the user asks to "design an email A/B test", "set up a multivariate subject/CTA test", "run a send-time test", "build a hold-out group", or "is this email result statistically and practically material?"; produces a falsifiable hypothesis, one-variable-per-cell matrix, sample-size/MDE/duration/power plan, and an effect/uncertainty read from own ESP data. Applies only a precommitted owner-approved action rule; the helper never chooses a business action. Not for EQS/vetoes or writing the email. 邮件AB测试设计/多变量测试/发送时间测试/留出组/显著性判定
Install
Quality Score: 98/100
Skill Content
Details
- Author
- aaron-he-zhu
- Repository
- aaron-he-zhu/aaron-marketing-skills
- Created
- 8 months ago
- Last Updated
- today
- Language
- Python
- License
- Apache-2.0
Bundled in these plugins
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ad-test-designer
Use when the user asks to "design an A/B test", "set up a creative/landing test", "run an incrementality test", or "is this result statistically and practically material?"; produces a hypothesis, variant matrix, sample-size/duration/power plan, and a documented effect/uncertainty read from own exported results. It applies only a precommitted owner-approved action rule; the statistical helper never chooses a business action. Not for producing variants — use ad-creative-builder; not for reading back one shipped change — use paid-measurement-loop. 广告AB测试设计/实验设计/显著性判定/增效测试
experiment-designer
Turn an assumption or a growth bet into an experiment brief with a mechanism-stated hypothesis, one primary metric, guardrails with floors, an exposure design, a sample size reasoned from the minimum detectable effect, and stop rules written before launch. Use when a growth plan bet needs a test, when an assumption register row is low confidence and high impact, when a price change should be tried on a slice first, or when someone says "let us just A/B it". Takes the assumption, the metric candidate with its baseline, the eligible traffic, and the guardrail candidates; returns the test card, the filled brief, the sizing record with its calculator inputs, and the pre-committed decision rule.
experiment-design
A discipline for designing experiments (A/B tests, multivariate, holdouts) so the results actually answer the question you asked. Hypothesis writing, sample size, duration, segment analysis, running discipline, matching a result to a pre-committed decision rule, and the common failure modes that produce confidently wrong shipping decisions. Use this skill whenever the user is planning a test that has not run yet: framing a hypothesis, sizing the sample, setting duration, choosing guardrails, or deciding whether something is worth testing at all. Triggers on design an experiment, experiment plan, A/B test, split test, multivariate test, holdout, experiment hypothesis, sample size, minimum detectable effect, MDE, test duration, guardrail metric, no peeking, pre-committed decision rule, is this worth testing. Use `experimentation-analytics` instead when the test has already run and the question is how to read the result panel.