experiment-design

# Experiment Design Skill > Hypothesis-design 의 후속. recommended hypothesis 를 검증 가능한 실험으로 변환. ## 입력 - recommended hypothesis (XYZ format) from `skills/hypothesis-design` - team OKR (어떤 KR 에 영향?) ## 산출 — `<org>/experiments/<id>/manifest.yaml` ```yaml experiment_id: exp-YYYY-MM-DD-<slug> hypothesis: xyz: "[X%] of [Y] will [Z] within [T], because [R]" source_pr: "..." variants: - id: control description: "current behavior" traffic_pct: 50 - id: treatment description: "..." traffic_pct: 50 metrics: primary: name: "..." formula: "..." direction: increase | decrease threshold: "..." secondary: [...] guardrail: [...] design: type: ab_test | feature_flag | concierge | wizard_of_oz | fake_door duration_days: 14 min_sample: 1000 power: 0.8 alpha: 0.05 gates: start: - "instrumentation verified" - "guardrail metrics baselines stable" stop_early: - "guardrail breach" - "primary metric Δ p<0.01 (positive)" end: - "min_sample reached" - "duration reached" evidence_refs: - "<org>/workflows/.../PRD.md" - "<org>/memory/open-questions/<task>.json" ``` ## ≥ 2 Designs Rule experiment design 도 항상 ≥2 후보: - AB test vs concierge MVP - feature flag vs cohort - 다른 traffic split 가장 cheap 한 valid design 우선 권고. ## HARD GATE ```markdown - [ ] primary metric 1개 + threshold + window - [ ] sample size 충분성 (power=0.8, α=0.05) - [ ] guardrail metric ≥1 (regression 방지) - [ ] start/stop/end gate 모두 정의 - [ ] pro...