nguyenphp/antigravity-marketing/ab-test-dashboard

Name: ab-test-dashboard
Author: nguyenphp

Claude Code and agent skill by nguyenphp/antigravity-marketing

Trust tier 3

community

By nguyenphp
Marketing
nguyenphp/antigravity-marketing
templates/.agent/skills/ab-test-dashboard/SKILL.md
Updated 22 Sep 2026
Version 1.0.01 version recorded

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Build and analyze A/B test dashboards, calculate statistical significance, and track experiment results.

Install

npx vskill@latest install nguyenphp/antigravity-marketing/ab-test-dashboard

SKILL.md

A/B Test Dashboard

Track experiments, measure results, and make data-driven decisions.

🎯 What This Skill Does

Capability	Description
Design Tests	Structure proper A/B experiments
Track Results	Monitor test performance
Calculate Stats	Statistical significance checks
Visualize Data	Dashboard creation
Report Findings	Communicate results clearly

1. A/B Test Basics

What is an A/B Test?

Term	Meaning
Control (A)	Your current version
Variant (B)	The change you're testing
Sample size	Number of visitors needed
Significance	Confidence results aren't random
Conversion	The action you're measuring

What to Test (Priority)

High Impact	Medium Impact	Low Impact
Headlines	Button text	Font size
CTAs	Images	Colors
Pricing	Form fields	Spacing
Offers	Layout	Icons
Landing pages	Navigation	Animations

2. Sample Size Calculator

Quick Formula

Sample Size = (Z² × p × (1-p)) / E²
 
Where:
- Z = 1.96 for 95% confidence
- p = expected conversion rate
- E = margin of error (±)

Simple Reference Table

Current Rate	Lift to Detect	Sample per Variation
1%	20%	~40,000
2%	20%	~20,000
5%	20%	~8,000
10%	15%	~4,000
20%	10%	~3,000

Online Calculators

Tool	URL
Optimizely	optimizely.com/sample-size-calculator
AB Tasty	abtasty.com/sample-size-calculator
Evan Miller	evanmiller.org/ab-testing/sample-size

3. Dashboard Metrics

Core Metrics to Track

Metric	Formula
Conversion Rate	Conversions / Visitors × 100
Relative Lift	(B - A) / A × 100
Confidence Level	Statistical p-value calculation
Visitors per Day	Total visitors / Days running
Days to Significance	Required sample / Daily visitors

Dashboard Layout

┌─────────────────────────────────────────────┐
│  TEST: [Name]                Status: RUNNING │
├─────────────────────────────────────────────┤
│                                             │
│   Control (A)        Variant (B)            │
│   ┌─────────┐       ┌─────────┐            │
│   │  2.5%   │       │  3.1%   │            │
│   │  Conv   │       │  Conv   │            │
│   └─────────┘       └─────────┘            │
│                                             │
│   Visitors: 5,432    Visitors: 5,489       │
│   Conversions: 136   Conversions: 170      │
│                                             │
│   ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━      │
│   Lift: +24%    Confidence: 87%            │
│   ⚠️ Not yet significant (need 95%)        │
│                                             │
├─────────────────────────────────────────────┤
│  Progress: ████████░░░░░░ 68% complete      │
│  Est. completion: 4 more days               │
└─────────────────────────────────────────────┘

4. Statistical Significance

What Does It Mean?

Confidence	Meaning
95%	5% chance results are random (standard)
99%	1% chance results are random (high stakes)
90%	10% chance results are random (quick tests)

Quick Significance Check

Scenario	Likely Significant?
Big difference + many visitors	✅ Yes
Small difference + few visitors	❌ No
Big difference + few visitors	⚠️ Wait
Small difference + many visitors	⚠️ Maybe real, but small

Avoid Common Mistakes

Mistake	Problem	Solution
Stopping early	False positives	Wait for sample size
Peeking too much	Bias decisions	Set check schedule
Testing too many things	Can't attribute cause	One change at a time
Running too short	Misses weekly patterns	Run 1-2 full weeks

5. Simple Dashboard with Google Sheets

Setup Steps

1. Create new Google Sheet
2. Tab 1: Raw data (date, variant, visitors, conversions)
3. Tab 2: Summary calculations
4. Tab 3: Charts

Formula Examples

Metric	Google Sheets Formula
Conversion Rate A	`=SUMIF(B:B,"A",D:D)/SUMIF(B:B,"A",C:C)`
Conversion Rate B	`=SUMIF(B:B,"B",D:D)/SUMIF(B:B,"B",C:C)`
Lift	`=(B_Rate-A_Rate)/A_Rate`
Daily Visitors	`=SUMIF(A:A,TODAY()-1,C:C)`

Simple Significance Formula

For approximation (use proper calculator for real tests):
 
Z = (pB - pA) / SQRT(p*(1-p)*(1/nA + 1/nB))
 
Where:
- pA, pB = conversion rates
- p = pooled rate = (convA + convB) / (nA + nB)
- nA, nB = sample sizes
 
If |Z| > 1.96, result is significant at 95%

6. Test Documentation

Pre-Test Template

markdown

## Test: [Name]
 
### Hypothesis
If we [change], then [metric] will [improve] because [reason].
 
### Test Details
-Page/Element: 
-Control: [Description]
-Variant: [Description]
-Primary metric: 
-Secondary metrics:
 
### Sample Size
-Current conversion rate: X%
-Minimum detectable effect: Y%
-Required visitors per variant: Z
 
### Timeline
-Start date:
-Expected end date:
-Check-in dates:

Post-Test Template

markdown

## Test Results: [Name]
 
### Summary
-Winner: [Control/Variant/No difference]
-Confidence: X%
-Lift: +/-Y%
 
### Data
| Metric | Control | Variant | Lift |
|--------|---------|---------|------|
| Visitors | | | |
| Conversions | | | |
| Conv Rate | | | |
 
### Learning
What did we learn?
 
### Next Steps
-Implement winner?
-Follow-up test?
-Share with team?

7. Tools for A/B Testing

Free/Cheap Tools

Tool	Best For	Cost
Google Optimize	Websites (sunset, but alternatives exist)	Free
Splitbee	Simple tests	Free tier
Growthbook	Open source	Free
Posthog	Feature flags + analytics	Free tier

Paid Tools

Tool	Best For	Cost
VWO	Full CRO platform	$199+/mo
Optimizely	Enterprise	$$$$
AB Tasty	Mid-market	$$$
Convert	SMB friendly	$99+/mo

8. Visualization Best Practices

Chart Types

Data	Chart Type
Rate comparison	Bar chart
Trend over time	Line chart
Sample progress	Progress bar
Segment breakdown	Pie/donut
Confidence range	Error bars

Dashboard Colors

Status	Color
Winning (significant)	Green
Losing (significant)	Red
Running (not significant)	Yellow/Gray
Winner	Blue (neutral)

Remember: Statistical significance ≠ practical significance. Always ask: "Is this lift worth the effort to implement?"

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Source

SKILL.md in the repositoryCheckingtemplates/.agent/skills/ab-test-dashboard/SKILL.md

Popularity

Repository stars: 34
Repository forks: 11
7-day trend score: 18

Stars and forks are for the whole GitHub repository, not this skill alone. Not refreshed yet.

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