What makes Ad Test Designer notable?

1.2K downloads. Trust evidence available

How should Ad Test Designer be evaluated before use?

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agentCLAWHUBUnverified

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 practica... Skill: Ad Test Designer Owner: aaron-he-zhu Summary: 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 practica... Tags: latest:19.0.0 Version history: v19.0.0 | 2026-07-24T14:13:34.336Z | auto ad-test-designer v19.0.0 - Version updated to 19.0.0 with metadata and skill contract version bump. - Added distribution-manife

OpenClaw

View on ClawHub Homepage

Rank

Safety

Downloads

1.2k

Updated

Oct 11, 2026

Version

19.0.0

Source

CLAWHUB

About

What it does, and when to use it.

Capability contract not published. No trust telemetry is available yet. 1.2K downloads reported by the source. Last updated 10/11/2026.

Avoid when

Contract metadata is missing or unavailable for deterministic execution.

Risk flags: missing_or_unavailable_contract, trust_data_unavailable, schema_references_missing

Public facts

Every fact links back to the source it came from.

Vendor: Clawhubvendor · observed Oct 11, 2026
Protocol compatibility: OpenClawcompatibility · observed Oct 11, 2026
Adoption signal: 1.2K downloadsadoption · observed Oct 11, 2026
Latest release: 19.0.0release · observed Jul 24, 2026
Handshake status: UNKNOWNsecurity

Install and run

Setup complexity: low.

clawhub skill install s17e1tg8pjra8dn1dvtq21sahx83hrxj:ad-test-designer

Setup complexity is classified as HIGH. You must provision dedicated cloud infrastructure or an isolated VM. Do not run this directly on your local workstation.
Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data.

Contract: missing

curl -s "https://www.xpersona.co/api/v1/agents/clawhub-aaron-he-zhu-ad-test-designer/snapshot"

Documentation

CLAWHUB

123,146 characters of source documentation, loaded on request.

Extracted files

5 files captured from the source.

SKILL.md

---
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: "19.0.0"
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": "19.0.0", "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

```text
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.
```
```text
I have 4 RSA creative variants to test on a prospecting set. Build the variant matrix, sample size, and run duration.
```
```text
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, practi

_meta.json

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  "slug": "ad-test-designer",
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}

references/test-design-guide.md

# Ad Test Design Guide

Detail pack for [ad-test-designer](../SKILL.md). Use the stdlib `experiment.py` helper for deterministic calculations or show the same inputs and formulas manually; do not introduce a hidden notebook/library result.

## Variant matrix template

| Variant | One changed variable | What's held constant | Destination |
|---------|---------------------|----------------------|-------------|
| A (control) | — (baseline) | everything | current LP/URL |
| B | the single test change | all else = control | same or split URL |
| C, D (A/B/n, ≤ 4 total) | a different single change each | all else = control | same |

Rules: one variable per variant; keep a control/holdout; cap A/B/n at 4 variants so traffic isn't split too thin; same audience + budget logic across arms.

### Test structures

- **Creative A/B** — vary one creative element (headline / hook / image). Primary metric usually CTR or CVR.
- **Landing-page A/B / split-URL** — vary one page element (hero, CTA, proof). Primary metric CVR; guardrail bounce.
- **Incrementality (geo / holdout)** — a treated group gets the change, a matched holdout does not. Measures lift over the counterfactual, not just relative variant performance. Needs a clean, comparable holdout (geo split or audience holdout) and a longer window.

## Sample-size lookup (per variant, two-sided α = 0.05, power = 0.80)

Approximate exposures **per variant** to detect a relative lift on a binary metric (CVR/CTR). Interpolate; for A/B/n add ~20–30% headroom for multiple comparisons.

| Baseline rate | 10% lift | 20% lift | 50% lift |
|---------------|----------|----------|----------|
| 1% | ~150k | ~39k | ~6k |
| 3% | ~47k | ~12k | ~2k |
| 5% | ~27k | ~7k | ~1.2k |
| 10% | ~12k | ~3k | ~550 |

**Duration** = (per-variant sample × number of variants) ÷ (traffic/day reaching the test). Floor at one full business cycle (≥ 1–2 weeks) to absorb day-of-week effects. Pre-commit to the sample size; **do not peek and stop early** — early stopping inflates false positives.

**Power note**: power (1−β) is the chance of detecting a true effect of the stated size. The table is built at 0.80; if the user wants 0.90, sizes rise ~30%. State the assumed baseline, minimum detectable effect, α, and power in the design.

## Significance methods

### Two-proportion z-test (CVR / CTR)

For control rate p₁ = x₁/n₁ and variant rate p₂ = x₂/n₂:

1. Pooled rate `p = (x₁ + x₂) / (n₁ + n₂)`.
2. Standard error `SE = sqrt( p·(1−p)·(1/n₁ + 1/n₂) )`.
3. `z = (p₂ − p₁) / SE`.
4. Compare the two-sided p-value with the **precommitted alpha**. `|z| ≥ 1.96` corresponds only to the common `alpha=.05` reference case.

Report p₁, p₂, the relative lift `(p₂−p₁)/p₁`, and the z value with its inputs shown.

### Mann-Whitney U (non-normal continuous metrics)

Use for revenue-per-user, order value, or time-on-page where the distribution is skewed. Compare at the declared alpha and report the effect alongside U; `experiment.py continuous` provides the determin

skill-card.md

## Description:

Designs paid-ad creative, landing-page, and incrementality tests, then reads user-owned exported results for effect size, uncertainty, guardrails, and owner-governed decision status.

This skill is ready for commercial/non-commercial use.

## Publisher:

[aaron-he-zhu](https://clawhub.ai/user/aaron-he-zhu)

### License/Terms of Use:

MIT-0

## Use Case:

Marketers, growth teams, and analytics practitioners use this skill to design A/B/n or incrementality tests for paid advertising and to interpret completed tests from their own exported performance data.

### Deployment Geography for Use:

Global

## Known Risks and Mitigations:

Risk: The skill may process ad test briefs or exported performance CSVs that contain sensitive business data.

Mitigation: Use only data the user is comfortable sharing with the agent and keep exported inputs within the user's normal data-handling workflow.

Risk: Statistical read-outs can be mistaken for authorization to change live campaigns.

Mitigation: Treat output as design and interpretation support; require the named decision owner and precommitted action rule before making campaign changes.

## Reference(s):

- [Ad Test Design Guide](references/test-design-guide.md)
- [ClawHub skill page](https://clawhub.ai/aaron-he-zhu/skills/ad-test-designer)
- [Project homepage](https://github.com/aaron-he-zhu/aaron-marketing-skills)

## Skill Output:

**Output Type(s):** [text, markdown, guidance, shell commands]

**Output Format:** [Markdown with structured test design or read-out sections]

**Output Parameters:** [1D]

**Other Properties Related to Output:** [May include a handoff summary, documented assumptions, calculated statistical fields, guardrail status, and decision UNDECIDED when owner approval or precommitted action rules are missing.]

## Skill Version(s):

19.0.0 (source: server release evidence and skill frontmatter)

## Ethical Considerations:

Users should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment.

distribution-manifest.json

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