ab-test-setup
by alirezarezvaniab-test-setup is a marketing experimentation skill for planning statistically sound A/B tests. Use it to define hypotheses, variants, primary and guardrail metrics, sample size assumptions, duration, decision rules, and pre-launch QA for conversion experiments.
This skill scores 82/100, making it a solid listing candidate for directory users who want an agent to plan and document statistically responsible A/B tests. The repository evidence shows clear triggers, substantive workflow guidance, reusable templates, and a working calculator script, though users should note the lack of path-level install instructions and likely need to adapt implementation details to their testing stack.
- Strong triggerability: the description explicitly covers A/B tests, split tests, experiments, variant copy, hypotheses, statistical significance, and related phrases.
- Operationally useful content: the skill includes an initial assessment flow, core experimentation principles, constraints, and practical workflow guidance rather than placeholder text.
- Good reusable assets: it ships with a sample size guide, test plan/templates reference, and a no-dependency Python sample size calculator.
- No install command or README is present in the skill path, so directory users may need to infer installation from the broader repository conventions.
- The excerpted workflow emphasizes planning and statistical rigor, but adoption details for specific experimentation platforms appear limited; implementation may still require tool-specific knowledge.
Overview of ab-test-setup skill
What ab-test-setup is built to do
ab-test-setup is a marketing experimentation skill for planning A/B tests that are specific, measurable, and statistically defensible. It helps turn a vague conversion idea such as “test a new signup CTA” into a structured experiment with a hypothesis, variants, metrics, sample size assumptions, duration, success criteria, and pre-launch checks.
Best-fit users and decisions
The ab-test-setup skill is most useful for growth marketers, product managers, lifecycle teams, CRO specialists, and founders who need to decide whether a conversion change is worth testing before handing work to design, engineering, or an experimentation platform. It is especially relevant for landing pages, signup flows, pricing pages, onboarding steps, email funnels, and feature adoption tests.
What makes it different from a generic prompt
A generic A/B testing prompt may produce a checklist. This skill pushes for the parts that make an experiment decision-ready: one test variable, a clear hypothesis, primary and guardrail metrics, baseline conversion rate, minimum detectable effect, traffic constraints, and no early stopping. The repository also includes references/sample-size-guide.md, references/test-templates.md, and scripts/sample_size_calculator.py, which give the agent practical structure beyond copywriting advice.
When it may not be enough
Use ab-test-setup for experiment design, not full analytics implementation. If you need event instrumentation, warehouse modeling, tag manager configuration, or dashboard setup, pair it with an analytics or tracking workflow. It also cannot rescue a test with too little traffic, unclear success metrics, multiple simultaneous changes, or no baseline data.
How to Use ab-test-setup skill
ab-test-setup install and repository path
Install the skill from the GitHub repository with:
npx skills add alirezarezvani/claude-skills --skill ab-test-setup
The source path is marketing-skill/skills/ab-test-setup. After installation, review SKILL.md first, then open references/test-templates.md for planning structure, references/sample-size-guide.md for sample size reasoning, and scripts/sample_size_calculator.py if you want a local stdlib Python calculator with no pip dependencies.
Inputs the skill needs for strong output
For useful ab-test-setup usage, provide the business goal, test surface, current conversion rate, estimated daily eligible traffic, proposed change, audience, tooling constraints, and the smallest lift that would matter commercially. If you have .claude/product-marketing-context.md, the skill is designed to read that first, so keep positioning, audience, funnel, and offer context there.
A weak request is: “Design an A/B test for my landing page.”
A stronger request is: “Use ab-test-setup for Conversion on our B2B SaaS pricing page. Baseline demo-request conversion is 4.8%, eligible traffic is 900 visitors/day, proposed change is replacing a feature-grid hero with ROI-focused copy and a new CTA. We can run for up to 4 weeks in VWO, 50/50 traffic split, primary metric is demo requests, guardrails are bounce rate and paid signup quality. Minimum meaningful lift is 15%.”
Suggested workflow from idea to launch plan
Start by asking the skill to validate whether the idea is testable. Then have it produce a one-page test plan using the repository template: hypothesis, control, variant, traffic allocation, sample size, duration, metrics, segmentation plan, and decision rules. Next, run or request sample size estimation using the baseline and MDE. Finally, ask for a pre-launch QA checklist covering targeting, mutual exclusivity, event tracking, variant rendering, and what not to change during the test.
Practical prompt patterns that improve quality
Ask the skill to separate “test design” from “implementation details” so the plan does not blur strategy with tool setup. Tell it whether you want A/B, A/B/n, or multivariate testing; otherwise it should default to testing one primary variable. If traffic is low, ask it to recommend alternatives such as a larger MDE, longer duration, qualitative validation, or testing a higher-traffic step instead of pretending significance is easy.
ab-test-setup skill FAQ
Is ab-test-setup only for website conversion tests?
No. It fits any controlled experiment where you can define a population, variants, exposure, and measurable outcome. It works well for landing pages, checkout flows, onboarding screens, email subject lines, lifecycle messages, and in-product prompts. It is less suitable for brand awareness campaigns where attribution is indirect and exposure cannot be cleanly controlled.
Can beginners use this skill?
Yes, but beginners should supply real numbers. The skill can explain hypotheses, MDE, power, confidence, and guardrail metrics, but it still needs baseline conversion and traffic estimates to avoid fantasy planning. If you do not know the baseline, ask it first for a measurement plan or a “data needed before launch” checklist.
How does it compare with using an experimentation tool template?
Experimentation tools help launch and monitor tests, but they do not always challenge whether the test is worth running. The ab-test-setup skill is useful before tool configuration because it clarifies what you are testing, why it should matter, how long it may take, and what result will count as a win, loss, or inconclusive outcome.
When should I not use ab-test-setup?
Do not use it when you want to change several major page elements and still claim to know which one caused the result. Avoid it for tests with insufficient eligible traffic, untracked primary metrics, unstable pages, seasonal anomalies, or teams that will stop the experiment as soon as early numbers look favorable.
How to Improve ab-test-setup skill
Improve ab-test-setup results with better baselines
The most important improvement is better input data. Provide the actual conversion denominator, not just “we get leads.” For example, say “420 demo requests from 8,750 pricing-page visitors in the last 30 days” instead of “about 5% conversion.” Include exclusions such as internal traffic, returning customers, bot filtering, and whether the metric is session-based, user-based, or account-based.
Watch for common failure modes
The most common failures are oversized ambitions, low-traffic tests, vague hypotheses, and success metrics that do not map to business value. Another failure is over-segmentation: asking for mobile, desktop, new users, returning users, industry, source, and plan type analysis when the total sample is barely enough for the primary metric. Ask the skill to prioritize segments rather than analyze everything.
Iterate after the first plan
After the first output, ask the skill to critique the plan as an experiment reviewer. Useful follow-up prompts include: “What would make this result inconclusive?”, “Which assumption is weakest?”, “Is the MDE realistic for our traffic?”, “What should be frozen during the test?”, and “What decision should we make if the primary metric improves but lead quality drops?”
Extend the skill for your team
To make ab-test-setup more valuable, add your standard experimentation platform, naming conventions, event taxonomy, QA checklist, and approval process to local context. If your team repeatedly tests the same funnel, maintain reusable examples for pricing, signup, checkout, email, and onboarding experiments so the skill can produce plans that match your operating model instead of generic CRO documentation.