ab-testing
Use when designing experiments for subject lines, offers, cadences, or journeys.
Best use case
ab-testing is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Use when designing experiments for subject lines, offers, cadences, or journeys.
Teams using ab-testing should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/ab-testing/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How ab-testing Compares
| Feature / Agent | ab-testing | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Use when designing experiments for subject lines, offers, cadences, or journeys.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Experimentation & A/B Testing Skill ## When to Use - Validating new subject lines or creative. - Testing segmentation hypotheses (persona vs behavior). - Optimizing cadence, timing, or automation triggers. ## Framework 1. **Hypothesis** – define expected uplift + rationale. 2. **Metric Selection** – primary (open/click/conv) + guardrails (unsubs, spam). 3. **Sample Sizing** – ensure stat significance (min 500 recipients per variant or use power calculator). 4. **Execution** – randomize, keep variants isolated, limit simultaneous tests. 5. **Analysis** – use z-test or Bayesian uplift; document learnings. ## Templates - Experiment brief (hypothesis, segments, KPI, risk guardrails). - Variant table (control vs test inputs, creative asset links, owner). - Calculator sheet for minimum detectable effect + sample size. - Post-test debrief doc capturing learnings + rollout plan. ## Experiment Ideas - Subject line vs preview text combos. - CTA placement (hero vs footer). - Personalization depth (basic vs dynamic modules). - Wait times between touches. ## Tips - Run no more than two tests per journey simultaneously. - Recycle learnings into playbooks + automation templates. - Segment results by persona to catch hidden signals. ---
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