ab-testing
Plans and designs A/B and multivariate tests, with hypotheses, duration and significance, or builds a growth experimentation program.
- Kind
- Skill
- Works with
- Claude Code
- Source
- Marketing Skills (Corey Haines) @ 88c79f6528a9
- Path
- skills/ab-testing/SKILL.md
- Jobs
- Testing, Planning
- Topics
- Ads & growth
Credit
- Made by
- coreyhaines31
- Original
- github.com/coreyhaines31/marketingskills/blob/88c79f6528a9347…
- License
- MIT
Listed by Skill Manifest; not made by us.
InstallInstall it with npx.
npx skills add coreyhaines31/marketingskills --skill ab-testing
Install factsScanned 2 Nov 2026
- Adds
- .claude/skills/ab-testing/ (the skill folder)
- 2 file(s) the skill refers to
- Runs scripts
- None found
- Uses the network
- None found
- Needs secrets
- None found
- Source revision
- 88c79f6528a9
- Licence
- MIT
- Last activity
- 31 Oct 2026
- Review
- Reviewed
Found by a static scan of the stored text; nothing is run. “None found” is not a guarantee, so read what you install before you run it.
- Only the main file is read; other files in the folder are not scanned
# A/B Test Setup 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 **Check for product marketing context first:** If `.agents/product-marketing.md` exists (or `.claude/product-marketing.md`, or the legacy `product-marketing-context.md` filename, in older setups), read it before asking questions. Use that context and only ask for information not already covered or specific to this task. Before designing a test, understand: 1. **Test Context** - What are you trying to improve? What change are you considering? 2. **Current State** - Baseline conversion rate? Current traffic volume? 3. **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 …