AGENT SKILLBY MANOJBAJAJ95
A/B test setup
AT A GLANCE
A/B test setup is a CRO agent skill from manojbajaj95. When the user wants to plan, design, or implement an A/B test or experiment. It is published in the manojbajaj95/claude-gtm-plugin GitHub repository (74 stars) under the MIT license. Install it with: npx skills add manojbajaj95/claude-gtm-plugin --skill ab-test-setup
- SOURCE REPOSITORY
- manojbajaj95/claude-gtm-plugin (opens GitHub in a new tab)
- REPO STARS
- SOURCE REPOSITORY
- LICENSE
- MIT
- CATEGORIES
- SKILL.MD SIZE
- 20.3 KB797 LINES
QUICK ANSWER
- WHAT IT IS01
- When the user wants to plan, design, or implement an A/B test or experiment. Also use when the user mentions "A/B test," "split test," "experiment," "test this change," "variant copy," "multivariate test," or. It stands out by giving A/B test setup a defined shape, so the agent asks for better context and returns a more usable result.
- INPUTS02
- Context the agent needs: your goals, audience, constraints, and any source material the skill asks for.
- OUTPUT03
- A ready-to-use result: the analysis, copy, or recommendations the agent produces.
INSTALL
One command adds this skill from its source repository.
npx skills add manojbajaj95/claude-gtm-plugin --skill ab-test-setupREAD THE SKILL.MD BELOW BEFORE YOU RUN IT. SKILLS INSTRUCT YOUR AGENT.
SKILL.MD
The full file, as published in the source repository. Copy it, download it or open it on GitHub.
ON THIS PAGE · 38
A/B Test Setup#
Workspace Context#
Read bootstrap context before asking questions: strategy/brand.md for brand, audience, offer, channels, tools, constraints, and metrics; about/me.md for personal voice; content/ideas.md and content/calendar.md for content planning. Use legacy product-marketing context files only as fallback. Save generated drafts to content/<platform>/drafts/YYYY-MM-DD_short-topic-slug.md, and route durable learnings back to strategy/brand.md, about/me.md, or content/ideas.md.
Operating Contract#
This skill is self-contained for its frontmatter scope: use its local instructions, references, scripts, and assets as the playbook; ask only for missing task-specific inputs; hand off to adjacent skills instead of expanding scope; and return an actionable artifact, decision, plan, draft, or diagnostic.
You are an expert in experimentation and A/B testing. Your goal is to help design tests that produce statistically valid, actionable results.
Initial Assessment#
Before designing a test, understand:
- Test Context - What are you trying to improve? What change are you considering?
- Current State - Baseline conversion rate? Current traffic volume?
- Constraints - Technical complexity? Timeline? Tools available?
Core Principles#
1. Start with a Hypothesis#
- Not just "let's see what happens"
- Specific prediction of outcome
- Based on reasoning or data
2. Test One Thing#
- Single variable per test
- Otherwise you don't know what worked
3. Statistical Rigor#
- Pre-determine sample size
- Don't peek and stop early
- Commit to the methodology
4. Measure What Matters#
- Primary metric tied to business value
- Secondary metrics for context
- Guardrail metrics to prevent harm
Hypothesis Framework#
Structure#
Because [observation/data],
we believe [change]
will cause [expected outcome]
for [audience].
We'll know this is true when [metrics].Example#
Weak: "Changing the button color might increase clicks."
Strong: "Because users report difficulty finding the CTA (per heatmaps and feedback), we believe making the button larger and using contrasting color will increase CTA clicks by 15%+ for new visitors. We'll measure click-through rate from page view to signup start."
Test Types#
| Type | Description | Traffic Needed |
|---|---|---|
| A/B | Two versions, single change | Moderate |
| A/B/n | Multiple variants | Higher |
| MVT | Multiple changes in combinations | Very high |
| Split URL | Different URLs for variants | Moderate |
Sample Size#
Quick Reference#
| Baseline | 10% Lift | 20% Lift | 50% Lift |
|---|---|---|---|
| 1% | 150k/variant | 39k/variant | 6k/variant |
| 3% | 47k/variant | 12k/variant | 2k/variant |
| 5% | 27k/variant | 7k/variant | 1.2k/variant |
| 10% | 12k/variant | 3k/variant | 550/variant |
Calculators:
For detailed sample size tables and duration calculations: See references/sample-size-guide.md (opens in a new tab)
Metrics Selection#
Primary Metric#
- Single metric that matters most
- Directly tied to hypothesis
- What you'll use to call the test
Secondary Metrics#
- Support primary metric interpretation
- Explain why/how the change worked
Guardrail Metrics#
- Things that shouldn't get worse
- Stop test if significantly negative
Example: Pricing Page Test#
- Primary: Plan selection rate
- Secondary: Time on page, plan distribution
- Guardrail: Support tickets, refund rate
Designing Variants#
What to Vary#
| Category | Examples |
|---|---|
| Headlines/Copy | Message angle, value prop, specificity, tone |
| Visual Design | Layout, color, images, hierarchy |
| CTA | Button copy, size, placement, number |
| Content | Information included, order, amount, social proof |
Best Practices#
- Single, meaningful change
- Bold enough to make a difference
- True to the hypothesis
Traffic Allocation#
| Approach | Split | When to Use |
|---|---|---|
| Standard | 50/50 | Default for A/B |
| Conservative | 90/10, 80/20 | Limit risk of bad variant |
| Ramping | Start small, increase | Technical risk mitigation |
Considerations:
- Consistency: Users see same variant on return
- Balanced exposure across time of day/week
Implementation#
Client-Side#
- JavaScript modifies page after load
- Quick to implement, can cause flicker
- Tools: PostHog, Optimizely, VWO
Server-Side#
- Variant determined before render
- No flicker, requires dev work
- Tools: PostHog, LaunchDarkly, Split
Running the Test#
Pre-Launch Checklist#
- Hypothesis documented
- Primary metric defined
- Sample size calculated
- Variants implemented correctly
- Tracking verified
- QA completed on all variants
During the Test#
DO:
- Monitor for technical issues
- Check segment quality
- Document external factors
DON'T:
- Peek at results and stop early
- Make changes to variants
- Add traffic from new sources
The Peeking Problem#
Looking at results before reaching sample size and stopping early leads to false positives and wrong decisions. Pre-commit to sample size and trust the process.
Analyzing Results#
Statistical Significance#
- 95% confidence = p-value < 0.05
- Means <5% chance result is random
- Not a guarantee—just a threshold
Analysis Checklist#
- Reach sample size? If not, result is preliminary
- Statistically significant? Check confidence intervals
- Effect size meaningful? Compare to MDE, project impact
- Secondary metrics consistent? Support the primary?
- Guardrail concerns? Anything get worse?
- Segment differences? Mobile vs. desktop? New vs. returning?
Interpreting Results#
| Result | Conclusion |
|---|---|
| Significant winner | Implement variant |
| Significant loser | Keep control, learn why |
| No significant difference | Need more traffic or bolder test |
| Mixed signals | Dig deeper, maybe segment |
Documentation#
Document every test with:
- Hypothesis
- Variants (with screenshots)
- Results (sample, metrics, significance)
- Decision and learnings
For templates: See references/test-templates.md (opens in a new tab)
Common Mistakes#
Test Design#
- Testing too small a change (undetectable)
- Testing too many things (can't isolate)
- No clear hypothesis
Execution#
- Stopping early
- Changing things mid-test
- Not checking implementation
Analysis#
- Ignoring confidence intervals
- Cherry-picking segments
- Over-interpreting inconclusive results
Task-Specific Questions#
- What's your current conversion rate?
- How much traffic does this page get?
- What change are you considering and why?
- What's the smallest improvement worth detecting?
- What tools do you have for testing?
- Have you tested this area before?
Related Skills#
- conversion-rate-optimization: For generating test ideas based on CRO principles
- data-and-funnel-analytics: For setting up test measurement
- copywriting-core: For creating variant copy
Supporting file: references/sample-size-guide.md#
Sample Size Guide#
Reference for calculating sample sizes and test duration.
Sample Size Fundamentals#
Required Inputs#
- Baseline conversion rate: Your current rate
- Minimum detectable effect (MDE): Smallest change worth detecting
- Statistical significance level: Usually 95% (α = 0.05)
- Statistical power: Usually 80% (β = 0.20)
What These Mean#
Baseline conversion rate: If your page converts at 5%, that's your baseline.
MDE (Minimum Detectable Effect): The smallest improvement you care about detecting. Set this based on:
- Business impact (is a 5% lift meaningful?)
- Implementation cost (worth the effort?)
- Realistic expectations (what have past tests shown?)
Statistical significance (95%): Means there's less than 5% chance the observed difference is due to random chance.
Statistical power (80%): Means if there's a real effect of size MDE, you have 80% chance of detecting it.
Sample Size Quick Reference Tables#
Conversion Rate: 1%#
| Lift to Detect | Sample per Variant | Total Sample |
|---|---|---|
| 5% (1% → 1.05%) | 1,500,000 | 3,000,000 |
| 10% (1% → 1.1%) | 380,000 | 760,000 |
| 20% (1% → 1.2%) | 97,000 | 194,000 |
| 50% (1% → 1.5%) | 16,000 | 32,000 |
| 100% (1% → 2%) | 4,200 | 8,400 |
Conversion Rate: 3%#
| Lift to Detect | Sample per Variant | Total Sample |
|---|---|---|
| 5% (3% → 3.15%) | 480,000 | 960,000 |
| 10% (3% → 3.3%) | 120,000 | 240,000 |
| 20% (3% → 3.6%) | 31,000 | 62,000 |
| 50% (3% → 4.5%) | 5,200 | 10,400 |
| 100% (3% → 6%) | 1,400 | 2,800 |
Conversion Rate: 5%#
| Lift to Detect | Sample per Variant | Total Sample |
|---|---|---|
| 5% (5% → 5.25%) | 280,000 | 560,000 |
| 10% (5% → 5.5%) | 72,000 | 144,000 |
| 20% (5% → 6%) | 18,000 | 36,000 |
| 50% (5% → 7.5%) | 3,100 | 6,200 |
| 100% (5% → 10%) | 810 | 1,620 |
Conversion Rate: 10%#
| Lift to Detect | Sample per Variant | Total Sample |
|---|---|---|
| 5% (10% → 10.5%) | 130,000 | 260,000 |
| 10% (10% → 11%) | 34,000 | 68,000 |
| 20% (10% → 12%) | 8,700 | 17,400 |
| 50% (10% → 15%) | 1,500 | 3,000 |
| 100% (10% → 20%) | 400 | 800 |
Conversion Rate: 20%#
| Lift to Detect | Sample per Variant | Total Sample |
|---|---|---|
| 5% (20% → 21%) | 60,000 | 120,000 |
| 10% (20% → 22%) | 16,000 | 32,000 |
| 20% (20% → 24%) | 4,000 | 8,000 |
| 50% (20% → 30%) | 700 | 1,400 |
| 100% (20% → 40%) | 200 | 400 |
Duration Calculator#
Formula#
Duration (days) = (Sample per variant × Number of variants) / (Daily traffic × % exposed)Examples#
Scenario 1: High-traffic page
- Need: 10,000 per variant (2 variants = 20,000 total)
- Daily traffic: 5,000 visitors
- 100% exposed to test
- Duration: 20,000 / 5,000 = 4 days
Scenario 2: Medium-traffic page
- Need: 30,000 per variant (60,000 total)
- Daily traffic: 2,000 visitors
- 100% exposed
- Duration: 60,000 / 2,000 = 30 days
Scenario 3: Low-traffic with partial exposure
- Need: 15,000 per variant (30,000 total)
- Daily traffic: 500 visitors
- 50% exposed to test
- Effective daily: 250
- Duration: 30,000 / 250 = 120 days (too long!)
Minimum Duration Rules#
Even with sufficient sample size, run tests for at least:
- 1 full week: To capture day-of-week variation
- 2 business cycles: If B2B (weekday vs. weekend patterns)
- Through paydays: If e-commerce (beginning/end of month)
Maximum Duration Guidelines#
Avoid running tests longer than 4-8 weeks:
- Novelty effects wear off
- External factors intervene
- Opportunity cost of other tests
Online Calculators#
Recommended Tools#
Evan Miller's Calculator https://www.evanmiller.org/ab-testing/sample-size.html (opens in a new tab)
- Simple interface
- Bookmark-worthy
Optimizely's Calculator https://www.optimizely.com/sample-size-calculator/ (opens in a new tab)
- Business-friendly language
- Duration estimates
AB Test Guide Calculator https://www.abtestguide.com/calc/ (opens in a new tab)
- Includes Bayesian option
- Multiple test types
VWO Duration Calculator https://vwo.com/tools/ab-test-duration-calculator/ (opens in a new tab)
- Duration-focused
- Good for planning
Adjusting for Multiple Variants#
With more than 2 variants (A/B/n tests), you need more sample:
| Variants | Multiplier |
|---|---|
| 2 (A/B) | 1x |
| 3 (A/B/C) | ~1.5x |
| 4 (A/B/C/D) | ~2x |
| 5+ | Consider reducing variants |
Why? More comparisons increase chance of false positives. You're comparing:
- A vs B
- A vs C
- B vs C (sometimes)
Apply Bonferroni correction or use tools that handle this automatically.
Common Sample Size Mistakes#
1. Underpowered tests#
Problem: Not enough sample to detect realistic effects Fix: Be realistic about MDE, get more traffic, or don't test
2. Overpowered tests#
Problem: Waiting for sample size when you already have significance Fix: This is actually fine—you committed to sample size, honor it
3. Wrong baseline rate#
Problem: Using wrong conversion rate for calculation Fix: Use the specific metric and page, not site-wide averages
4. Ignoring segments#
Problem: Calculating for full traffic, then analyzing segments Fix: If you plan segment analysis, calculate sample for smallest segment
5. Testing too many things#
Problem: Dividing traffic too many ways Fix: Prioritize ruthlessly, run fewer concurrent tests
When Sample Size Requirements Are Too High#
Options when you can't get enough traffic:
- Increase MDE: Accept only detecting larger effects (20%+ lift)
- Lower confidence: Use 90% instead of 95% (risky, document it)
- Reduce variants: Test only the most promising variant
- Combine traffic: Test across multiple similar pages
- Test upstream: Test earlier in funnel where traffic is higher
- Don't test: Make decision based on qualitative data instead
- Longer test: Accept longer duration (weeks/months)
Sequential Testing#
If you must check results before reaching sample size:
What is it?#
Statistical method that adjusts for multiple looks at data.
When to use#
- High-risk changes
- Need to stop bad variants early
- Time-sensitive decisions
Tools that support it#
- Optimizely (Stats Accelerator)
- VWO (SmartStats)
- PostHog (Bayesian approach)
Tradeoff#
- More flexibility to stop early
- Slightly larger sample size requirement
- More complex analysis
Quick Decision Framework#
Can I run this test?#
Daily traffic to page: _____
Baseline conversion rate: _____
MDE I care about: _____
Sample needed per variant: _____ (from tables above)
Days to run: Sample / Daily traffic = _____
If days > 60: Consider alternatives
If days > 30: Acceptable for high-impact tests
If days < 14: Likely feasible
If days < 7: Easy to run, consider running longer anywaySupporting file: references/test-templates.md#
A/B Test Templates Reference#
Templates for planning, documenting, and analyzing experiments.
Test Plan Template#
# A/B Test: [Name]
## Overview
- **Owner**: [Name]
- **Test ID**: [ID in testing tool]
- **Page/Feature**: [What's being tested]
- **Planned dates**: [Start] - [End]
## Hypothesis
Because [observation/data],
we believe [change]
will cause [expected outcome]
for [audience].
We'll know this is true when [metrics].
## Test Design
| Element | Details |
|---------|---------|
| Test type | A/B / A/B/n / MVT |
| Duration | X weeks |
| Sample size | X per variant |
| Traffic allocation | 50/50 |
| Tool | [Tool name] |
| Implementation | Client-side / Server-side |
## Variants
### Control (A)
[Screenshot]
- Current experience
- [Key details about current state]
### Variant (B)
[Screenshot or mockup]
- [Specific change #1]
- [Specific change #2]
- Rationale: [Why we think this will win]
## Metrics
### Primary
- **Metric**: [metric name]
- **Definition**: [how it's calculated]
- **Current baseline**: [X%]
- **Minimum detectable effect**: [X%]
### Secondary
- [Metric 1]: [what it tells us]
- [Metric 2]: [what it tells us]
- [Metric 3]: [what it tells us]
### Guardrails
- [Metric that shouldn't get worse]
- [Another safety metric]
## Segment Analysis Plan
- Mobile vs. desktop
- New vs. returning visitors
- Traffic source
- [Other relevant segments]
## Success Criteria
- Winner: [Primary metric improves by X% with 95% confidence]
- Loser: [Primary metric decreases significantly]
- Inconclusive: [What we'll do if no significant result]
## Pre-Launch Checklist
- [ ] Hypothesis documented and reviewed
- [ ] Primary metric defined and trackable
- [ ] Sample size calculated
- [ ] Test duration estimated
- [ ] Variants implemented correctly
- [ ] Tracking verified in all variants
- [ ] QA completed on all variants
- [ ] Stakeholders informed
- [ ] Calendar hold for analysis dateResults Documentation Template#
# A/B Test Results: [Name]
## Summary
| Element | Value |
|---------|-------|
| Test ID | [ID] |
| Dates | [Start] - [End] |
| Duration | X days |
| Result | Winner / Loser / Inconclusive |
| Decision | [What we're doing] |
## Hypothesis (Reminder)
[Copy from test plan]
## Results
### Sample Size
| Variant | Target | Actual | % of target |
|---------|--------|--------|-------------|
| Control | X | Y | Z% |
| Variant | X | Y | Z% |
### Primary Metric: [Metric Name]
| Variant | Value | 95% CI | vs. Control |
|---------|-------|--------|-------------|
| Control | X% | [X%, Y%] | — |
| Variant | X% | [X%, Y%] | +X% |
**Statistical significance**: p = X.XX (95% = sig / not sig)
**Practical significance**: [Is this lift meaningful for the business?]
### Secondary Metrics
| Metric | Control | Variant | Change | Significant? |
|--------|---------|---------|--------|--------------|
| [Metric 1] | X | Y | +Z% | Yes/No |
| [Metric 2] | X | Y | +Z% | Yes/No |
### Guardrail Metrics
| Metric | Control | Variant | Change | Concern? |
|--------|---------|---------|--------|----------|
| [Metric 1] | X | Y | +Z% | Yes/No |
### Segment Analysis
**Mobile vs. Desktop**
| Segment | Control | Variant | Lift |
|---------|---------|---------|------|
| Mobile | X% | Y% | +Z% |
| Desktop | X% | Y% | +Z% |
**New vs. Returning**
| Segment | Control | Variant | Lift |
|---------|---------|---------|------|
| New | X% | Y% | +Z% |
| Returning | X% | Y% | +Z% |
## Interpretation
### What happened?
[Explanation of results in plain language]
### Why do we think this happened?
[Analysis and reasoning]
### Caveats
[Any limitations, external factors, or concerns]
## Decision
**Winner**: [Control / Variant]
**Action**: [Implement variant / Keep control / Re-test]
**Timeline**: [When changes will be implemented]
## Learnings
### What we learned
- [Key insight 1]
- [Key insight 2]
### What to test next
- [Follow-up test idea 1]
- [Follow-up test idea 2]
### Impact
- **Projected lift**: [X% improvement in Y metric]
- **Business impact**: [Revenue, conversions, etc.]Test Repository Entry Template#
For tracking all tests in a central location:
| Test ID | Name | Page | Dates | Primary Metric | Result | Lift | Link |
|---------|------|------|-------|----------------|--------|------|------|
| 001 | Hero headline test | Homepage | 1/1-1/15 | CTR | Winner | +12% | [Link] |
| 002 | Pricing table layout | Pricing | 1/10-1/31 | Plan selection | Loser | -5% | [Link] |
| 003 | Signup form fields | Signup | 2/1-2/14 | Completion | Inconclusive | +2% | [Link] |Quick Test Brief Template#
For simple tests that don't need full documentation:
## [Test Name]
**What**: [One sentence description]
**Why**: [One sentence hypothesis]
**Metric**: [Primary metric]
**Duration**: [X weeks]
**Result**: [TBD / Winner / Loser / Inconclusive]
**Learnings**: [Key takeaway]Stakeholder Update Template#
## A/B Test Update: [Name]
**Status**: Running / Complete
**Days remaining**: X (or complete)
**Current sample**: X% of target
### Preliminary observations
[What we're seeing - without making decisions yet]
### Next steps
[What happens next]
### Timeline
- [Date]: Analysis complete
- [Date]: Decision and recommendation
- [Date]: Implementation (if winner)Experiment Prioritization Scorecard#
For deciding which tests to run:
| Factor | Weight | Test A | Test B | Test C |
|---|---|---|---|---|
| Potential impact | 30% | |||
| Confidence in hypothesis | 25% | |||
| Ease of implementation | 20% | |||
| Risk if wrong | 15% | |||
| Strategic alignment | 10% | |||
| Total |
Scoring: 1-5 (5 = best)
Hypothesis Bank Template#
For collecting test ideas:
| ID | Page/Area | Observation | Hypothesis | Potential Impact | Status |
|----|-----------|-------------|------------|------------------|--------|
| H1 | Homepage | Low scroll depth | Shorter hero will increase scroll | High | Testing |
| H2 | Pricing | Users compare plans | Comparison table will help | Medium | Backlog |
| H3 | Signup | Drop-off at email | Social login will increase completion | Medium | Backlog |FAQ
07 QUESTIONS
01What is the A/B test setup agent skill?
When the user wants to plan, design, or implement an A/B test or experiment. Also use when the user mentions "A/B test," "split test," "experiment," "test this change," "variant copy," "multivariate test," or. It is listed under CRO in the 100xLab agent skills directory.
02How do I install A/B test setup in Cursor, Claude Code, or Codex?
Run npx skills add manojbajaj95/claude-gtm-plugin --skill ab-test-setup in the project where you want it, then ask your agent for the skill by name. The --skill flag installs only A/B test setup, not every skill in the repository.
03What does the A/B test setup SKILL.md cover?
The SKILL.md includes sections on Workspace Context, Operating Contract, Initial Assessment, Core Principles, Start with a Hypothesis and Test One Thing. Read the full text on this page or in plain markdown at /resources/agent-skills/manojbajaj95-claude-gtm-plugin--ab-test-setup--13llxe4/raw.
04Which agents can use A/B test setup?
A/B test setup installs with the cross-agent skills installer (npx skills add), which sets it up for agents such as Claude Code, Cursor and Codex. Each agent loads the SKILL.md when you ask for the skill by name.
05What license is A/B test setup under?
The manojbajaj95/claude-gtm-plugin repository declares the MIT license. Read the license file in the repository before you copy or redistribute the skill.
06Is A/B test setup free to use?
Reading, copying and installing A/B test setup costs nothing: it is published in a public GitHub repository under the MIT license. Reuse and redistribution follow that license.
07Where is the source of A/B test setup?
A/B test setup comes from https://github.com/manojbajaj95/claude-gtm-plugin, which has 74 GitHub stars. The SKILL.md is at https://github.com/manojbajaj95/claude-gtm-plugin/blob/HEAD/skills/ab-test-setup/SKILL.md. The author is manojbajaj95.
Need an agent workflow built around your stack?
Tell us the workflow you want automated. We reply within 4 working hours (IST).
ATTRIBUTION
A/B test setup was written by its original authors and is published in manojbajaj95/claude-gtm-plugin (opens GitHub in a new tab) under the MIT license. All rights stay with the original authors.
100xLab did not write, maintain or endorse this skill. This page lists it for discovery. Repo stars belong to the source repository, not to the skill. Review any skill before you install it.