experiment
SolidDesign, run, and evaluate a controlled A/B test (split test) on your ad accounts with evidence discipline — one variable, a falsifiable hypothesis, a fixed window, and a per-variant outcome verdict. Use when the user asks to run an A/B test, split test, or experiment, to 'test whether X beats Y', to validate a creative-refresh or learning hunch properly, to validate a breakdown exclusion or reallocation hypothesis before acting on it, or requests A/Bテスト / スプリットテスト設計 / 実験を回したい / どちらが勝ったか評価して / 仮説を検証したい. Forces a designed experiment instead of an ad-hoc change, records the baseline in action_log, and forbids peeking-based decisions before the window closes.
Install
Quality Score: 87/100
Skill Content
Details
- Author
- logly
- Repository
- logly/mureo
- Created
- 5 months ago
- Last Updated
- yesterday
- Language
- Python
- License
- Apache-2.0
Bundled in these plugins
Similar Skills
Semantically similar based on skill content — not just same category
experiment-design
Use when planning A/B tests, fake-door tests, beta rollouts, experiments, metric rules, or interpreting outcomes.
experiment
A/B testing, experimentation, statistical analysis. Triggers on: /godmode:experiment, "A/B test", "split test", "statistical significance", "Optimizely", "Statsig", "GrowthBook".
experiment-design
A discipline for designing experiments (A/B tests, multivariate, holdouts) so the results actually answer the question you asked. Hypothesis writing, sample size, duration, segment analysis, running discipline, matching a result to a pre-committed decision rule, and the common failure modes that produce confidently wrong shipping decisions. Use this skill whenever the user is planning a test that has not run yet: framing a hypothesis, sizing the sample, setting duration, choosing guardrails, or deciding whether something is worth testing at all. Triggers on design an experiment, experiment plan, A/B test, split test, multivariate test, holdout, experiment hypothesis, sample size, minimum detectable effect, MDE, test duration, guardrail metric, no peeking, pre-committed decision rule, is this worth testing. Use `experimentation-analytics` instead when the test has already run and the question is how to read the result panel.