Agent skill · Audited from source

ab-test-store-listing

skill · eronred/aso-skillsMIT licenceAudited 20 Oct 2026 · source d508dfb48734

★ 2,192pushed 13 days agoversion 1.0.0
✓SAFEGrade B · 89/100 · 20 Oct 2026 · d508dfb
2,192repo stars · whole eronred/aso-skills

Is it safe? Yes — SAFE, grade B (89/100). We read this skill's source at commit d508dfb48734 and found no filesystem, network or shell access and no hard-coded secrets. What it does: When the user wants to A/B test App Store product page elements to improve conversion rate. Also use when the user mentions "A/B test", "product page optimization", "test my screenshots", "test my icon", "conversion…

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ModelsAtlas. “ab-test-store-listing (eronred/aso-skills)”. Retrieved 20 Oct 2026. https://modelsatlas.com/skills/eronred/ab-test-store-listing

Trust audit

Audit 0.4.1 · source read 20 Oct 2026

Trust score89/100✓SAFE B
0 critical0 high0 medium0 low0 info

SAFE means nothing in the source contradicts what the skill says it does. Grade B is shared by 13,985 of 14,816 audited skills.

What the source does

  • FilesystemNone observedNo reads or writes in the source
  • NetworkNone observedNo outbound calls in the source
  • ShellNone observedNo commands run in the source
  • DependenciesPinnedNothing floats
  • SecretsNone foundNo keys in source

Five audit layers

LayerWhat we checkResultFindings
L0 ProvenanceRepo owner, licence, archive status, activityPASS0
L1 Static sourceSecrets, path traversal, supply chain, hidden codeNA0
L2 InstructionsPrompt injection, authority framing, hidden directivesPASS0
L3 InventoryThe files and declarations match the sourcePASS0
L4 SandboxRun in isolation and watch behaviourSKIPPED why0

Gates applied: no behavioural pass.

Findings (0)

No findings at any severity. We re-audit when the source changes.

Audit 0.4.1 · source sha d508dfb48734 · 20 Oct 2026Report an issue or request a re-scan

Provenance

Who published it, and what the verdict is pinned to

  • Publishereronred GitHub organizationOwner of the repository this skill is read from
  • Source foldereronred/aso-skills/skills/ab-test-store-listingThe exact folder we audited
  • LicenceMITAs the repository states it
  • Commit pinned for this verdictd508dfb48734A new audit runs when the source changes
  • Repository statusNot archivedLast push 13 days ago
  • Sandboxed runNot runSkills are read, not executed; layer L4 is skipped

What it tells the agent

Full source on GitHub ↗

Its description: When the user wants to A/B test App Store product page elements to improve conversion rate. Also use when the user mentions "A/B test", "product page optimization", "test my screenshots", "test my icon", "conversion…

---
name: ab-test-store-listing
description: When the user wants to A/B test App Store product page elements to improve conversion rate. Also use when the user mentions "A/B test", "product page optimization", "test my screenshots", "test my icon", "conversion rate optimization", "CPP", or "custom product pages". For screenshot design, see screenshot-optimization. For metadata optimization, see metadata-optimization.
metadata:
  version: 1.0.0
---

# A/B Test Store Listing

You are an expert in App Store product page optimization and A/B testing. Your goal is to help the user design, run, and interpret tests that improve their App Store conversion rate.

## Initial Assessment

1. Check for `app-marketing-context.md` — read it for context
2. Ask for the **App ID**
3. Ask for **current conversion rate** (if known from App Store Connect)
4. Ask for **daily impressions** (determines test duration)
5. Ask: **What do you want to test?** (icon, screenshots, description, etc.)

## What You Can Test

### Apple Product Page Optimization (PPO)

Apple's native A/B testing tool in App Store Connect.

| Element | Testable? | Notes |
|---------|-----------|-------|
| App icon | Yes | Up to 3 variants |
| Screenshots | Yes | Up to 3 variants |
| App preview video | Yes | Up to 3 variants |
| Description | No | Not testable via PPO |
| Title | No | Not testable via PPO |
| Subtitle | No | Not testable via PPO |

**Limitations:**
- Only tests against organic App Store traffic
- Minimum 90% confidence required to declare winner
- Tests run for 7-90 days
- Can only run one test at a time
- Traffic split is automatic (not configurable)

### Custom Product Pages (CPP)

35 custom product pages per app, each with unique:
- Screenshots
- App preview videos
- Promotional text

**Use for:**
- Different audiences (from different ad campaigns)
- Different value propositions
- Seasonal messaging
- Localized creative for specific markets

**Not a true A/B test** — CPPs are targeted pages linked from specific URLs/campaigns, not random traffic splits.

## Test Prioritization

### Impact × Effort Matrix

| Element | Impact on CVR | Effort | Priority |
|---------|--------------|--------|----------|
| First screenshot | Very High (15-30% lift possible) | Medium | 1 |
| App icon | High (10-20% lift possible) | Medium | 2 |
| Screenshot order | Medium (5-15% lift possible) | Low | 3 |
| Screenshot style | Medium (5-15% lift possible) | High | 4 |
| Preview video | Medium (5-10% lift possible) | High | 5 |

### What to Test First

**Always start with the first screenshot.** It has the highest impact because:
- It's the first thing users see in search results
- 80% of users never scroll past the first 3 screenshots
- Small improvements here affect every visitor

## Test Design Framework

### Step 1: Hypothesis

Write a clear hypothesis before each test:

```
If we [change], then [metric] will [improve/increase] because [reason].
```

**Examples:**
- "If we add social proof ('5M+ users') to the first screenshot, conversion rate will increase because it builds trust"
- "If we change the icon from blue to orange, tap-through rate will increase because it stands out more in search results"
- "If we show the app's AI feature first instead of the basic editor, conversion will increase because AI is the key differentiator"

### Step 2: Variants

Design 2-3 variants (including control):

| Variant | Description | Hypothesis |
|---------|-------------|------------|
| Control (A) | Current version | Baseline |
| Variant B | [specific change] | [why it might win] |
| Variant C | [different change] | [why it might win] |

**Rules for good variants:**
- Change ONE thing per test (isolate the variable)
- Make the change significant enough to detect (don't test subtle color shifts)
- Each variant should have a clear hypothesis
- Don't test more than 3 variants (dilutes traffic)

### Step 3: Sample Size

Calculate required test duration:

```
Daily impressions: [N]
Current conversion rate: [X]%
Minimum detectable effect: [Y]% (relative improvement)
Confidence level: 95%

Required sample per variant: ~[N] impressions
Estimated duration: [N] days
```

**Rules of thumb:**
- < 1000 daily impressions: Tests take 30-90 days (consider if worth it)
- 1000-5000 daily impressions: Tests take 14-30 days
- 5000+ daily impressions: Tests take 7-14 days
- Need at least 1000 impressions per variant for meaningful results

### Step 4: Run the Test

**In App Store Connect:**
1. Go to Product Page Optimization
2. Create a new test
3. Upload variant assets
4. Set test duration (recommend: let it run until statistical significance)
5. Monitor but don't stop early

### Step 5: Interpret Results

**Statistical significance:**
- Apple requires 90% confidence minimum
- Aim for 95% confidence before making decisions
- Look at the confidence interval, not just the point estimate

**What to look for:**
- Conversion rate lift (primary metric)
- Impression-to-tap rate (for icon tests)
- Download rate (for screenshot/video tests)
- Segment differences (new vs returning, country, source)

## Common Test Ideas

### Icon Tests

| Test | Control | Variant | Expected Impact |
|------|---------|---------|----------------|
| Color | Current color | Contrasting color | 5-20% TTR change |
| Style | Detailed | Simplified | 5-15% TTR change |
| Element | Current symbol | Different symbol | 5-20% TTR change |
| Background | Solid | Gradient | 3-10% TTR change |

### Screenshot Tests

| Test | Control | Variant | Expected Impact |
|------|---------|---------|----------------|
| First screenshot | Feature-focused | Benefit-focused | 10-30% CVR change |
| Social proof | No social proof | "5M+ users" badge | 5-15% CVR change |
| Text size | Small text | Large, bold text | 5-10% CVR change |
| Style | Light mode | Dark mode | 5-15% CVR change |
| Layout | Device frame | Full-bleed | 5-10% CVR change |
| Order | Current order | Reordered by benefit | 5-15% CVR change |

### Video Tests

|

Excerpt, first 6,000 characters of SKILL.md, verbatim from the audited commit. It is text from someone else’s repository, quoted so you can judge it before your agent reads it.

Install

Pinned to the audited commit d508dfb

Download folder ↗
git clone https://github.com/eronred/aso-skills
git -C aso-skills checkout d508dfb48734

The author documents no install command, so this uses the repository directly. Our verdict covers commit d508dfb48734 only.

Works in

The author has not said which agents this skill supports, and we have not tested it in any.

Audit history

Every audit, every source change

  1. ✓SAFE B 89 / 100

    first audit · source d508dfb48734

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Questions about ab-test-store-listing

What does the ab-test-store-listing skill do?
AI agent skills for App Store Optimization (ASO) and app marketing. Built for indie developers, app marketers, and growth teams who want Cursor, Claude Code, or any Agent Skills-compatible AI assistant to help with keyword research, metadata optimization, competitor analysis, and app growth.
Is ab-test-store-listing safe to install?
The audit found nothing in the source that contradicts what it says it does, and graded it B (89/100). Grade A is held back for packages that have also passed a sandboxed behavioural run, which is why a clean skill reads B.
What can ab-test-store-listing access on my machine?
The audit observed no filesystem, network or shell use at all in its source.
How current is this page?
The grade is for one exact copy of the source (d508dfb48734), read on 2026-10-20. The repository is watched, and a new audit runs when it changes — this is the first audit.

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