| name | ad-test-designer |
| slug | aaron-ad-test-designer |
| displayName | Ad Test Designer · 广告AB测试设计 |
| summary | 广告AB测试设计/实验设计/显著性判定/增效测试 |
| description | 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 |
| version | 19.2.0 |
| license | Apache-2.0 |
| compatibility | Claude Code and compatible agent-skill hosts |
| homepage | https://github.com/aaron-he-zhu/aaron-marketing-skills |
| when_to_use | Use when designing a creative/landing A/B/n or incrementality test, or when reading effect size, uncertainty, and guardrails from a finished own-data test. Apply a business action only when its owner and decision rule were precommitted; otherwise return decision UNDECIDED. Not for generating variants (use ad-creative-builder) or reading back one already-shipped change (use paid-measurement-loop). |
| argument-hint | <what to test / results CSV> [profile: direct-response|prospecting|incremental-profit] [baseline] [alpha/power/MDE] |
| metadata | {"author":"aaron-he-zhu","version":"19.2.0","discipline":"ad","phase":"orchestrate","geo-relevance":"low","hermes":{"tags":["marketing","ad","orchestrate"],"category":"ad"},"openclaw":{"emoji":"🎯","homepage":"https://github.com/aaron-he-zhu/aaron-marketing-skills"}} |
Ad Test Designer
Designs paid-ad creative/landing A/B/n and incrementality tests and reads them out: hypothesis, variant matrix, sample-size/duration/power plan, effect size, uncertainty, practical-effect status, and guardrail state. This skill owns experiment design + statistical interpretation. It may apply an owner-approved, precommitted action rule, but it never treats a p-value or helper output as an automatic business decision. It does not produce variants (ad-creative-builder), read back one already-shipped change (paid-measurement-loop), or do cross-channel reporting (performance-analyzer).
Quick Start
Design an A/B test for two landing-page hero variants. Baseline CVR is 3%, I want to detect a 15% lift. Goal is DR.
I have 4 RSA creative variants to test on a prospecting set. Build the variant matrix, sample size, and run duration.
Here's my finished test results CSV (variant, sessions, conversions). Is the winner significant — promote or kill?
Skill Contract
- Expected output: a test design (hypothesis, variant matrix, primary/secondary/guardrail metrics, sample-size + duration + power plan) and/or a read-out (effect estimate, interval, statistical flag, practical-effect flag, guardrails, and either an owner-governed recommendation or
decision: UNDECIDED).
- Reads: what the user wants to test, the ROAS profile (
direct-response|prospecting|incremental-profit), baseline CVR/CTR and traffic volume; for a read-out, the user's own exported results CSV (variant, sessions/impressions, conversions/clicks).
- Writes: a user-facing test-design or read-out doc plus a
### Handoff Summary.
- Promotes: the chosen hypothesis, design parameters, calculated read-out, and any explicitly owner-approved action (ask before writing memory).
- Done when: a falsifiable hypothesis is stated; the matrix isolates one variable per variant; baseline, MDE, alpha, power, multiplicity/sequential policy, duration, and guardrails are declared; and a read-out reports effect/interval/statistical/practical flags with
Calculated provenance. Without a precommitted action rule and owner, return decision: UNDECIDED.
- Primary next skill: ad-creative-builder (to produce the winning direction) or .
