ad-test-designer
FeaturedUse 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
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Quality Score: 99/100
Skill Content
Details
- Author
- aaron-he-zhu
- Repository
- aaron-he-zhu/aaron-marketing-skills
- Created
- 7 months ago
- Last Updated
- today
- Language
- Python
- License
- Apache-2.0
Bundled in these plugins
Similar Skills
Semantically similar based on skill content — not just same category
experiment
Design, run, and evaluate a controlled A/B test (split test) on your ad accounts with evidence discipline — one variable, a falsifiable hypothesis, a fixed window, and a per-variant outcome verdict. Use when the user asks to run an A/B test, split test, or experiment, to 'test whether X beats Y', to validate a creative-refresh or learning hunch properly, or requests A/Bテスト / スプリットテスト設計 / 実験を回したい / どちらが勝ったか評価して / 仮説を検証したい. Forces a designed experiment instead of an ad-hoc change, records the baseline in action_log, and forbids peeking-based decisions before the window closes.
a-b-test-designer
You are a conversion optimization expert. When given a conversion problem, design statistically valid A/B tests with clear hypotheses, variants, and success metrics. ## Process 1. Identify the conversion problem and current metrics 2. Formulate a clear, testable hypothesis 3. Design control and variant(s) 4. Define success metrics and statistical significance 5. Estimate sample size and test duration ## Output Format ## A/B Test Design ### Problem \[Current conversion rate and goal\] ### Hypothesis 'If we \[change\], then \[metric\] will improve because \[reasoning\].' ### Variants - Control (A): Current design - Variant (B): \[Specific change description\] ### Success Metrics - Primary: \[Main metric to track\] - Secondary: \[Supporting metrics\] - Guardrail: \[Metrics that shouldn't decrease\] ### Statistical Plan - Confidence level: 95% - Minimum detectable effect: X% - Estimated...
ab-test-plan
Design A/B and multivariate tests. Use when: sample size calculation, testing hypothesis, CRO experimentation.