AI Agent Skills

Design a statistically rigorous A/B or multivariate test plan — If/Then/Because hypothesis, control and variant specs, required sample size per variant (absolute vs rela…

Design, plan, and analyze A/B tests with statistical rigor. Use when the user asks about A/B testing, split testing, experiment design, statistical significance, sample…

Use when designing or analyzing a controlled experiment — falsifiable hypothesis, sample size from an MDE, reading significance/CI/power, CUPED, or rescuing tests that w…

Compare two versions of an LLM-directed document - an original (teacher) and a candidate (student) - across a transfer set and return a per-case behavioural-equivalence…

Compares A/B variants of a WebAsk survey and checks whether the difference is real or within noise. Use when someone asks which variant performs better or is about to ma…

Analyze A/B test results with statistical significance, sample size validation, confidence intervals, and ship/extend/stop recommendations. Use when evaluating experimen…

Generate A/B test variants for affiliate content. Triggers on: "create A/B test", "test my headline", "optimize my CTA", "generate variants", "split test ideas", "improv…

Reliable browser automation and testing engine for web agent tasks.

When the user wants to set up, improve, or audit analytics tracking and measurement. Also use when the user mentions "set up tracking," "GA4," "Google Analytics," "conve…

When the user wants to set up, improve, or audit analytics tracking and measurement. Also use when the user mentions "set up tracking," "GA4," "Google Analytics," "conve…

Email marketing automation - campaign creation, sequence building, A/B testing, deliverability optimization, and analytics

Entry point and router for the lyric writing library (作词工作流与路由). The two workflows that fork at the very first question - music-first versus lyrics-first - and their dif…

Evaluate systematic strategy ideas through signal testing, statistical validation, decay, execution costs, and model-risk review.

Validating A/B test implementations including traffic splitting accuracy, statistical significance calculation, metric tracking, and experiment cleanup.