metric-design-experimentationlisted

## Purpose Produce a complete Measurement Framework — metric hierarchy (North Star → L1 → L2 → input), leading/lagging indicator pairs with temporal lag classification, counter-metric design that resists Goodhart's Law, experiment plans with statistical validity, and retention cohort methodology. The output is not a dashboard mockup or a list of KPIs — it is a metric *engineering* system: instrumented to detect problems early, paired to resist gaming, and validated causally. The artifact a PM cannot produce unaided. ## When to Use / When NOT to Use **Use this skill when:** - Launching a new product or feature and need to define what success looks like *before* building - Designing an A/B test or experiment plan with proper statistical rigor - An existing metric feels "off" — you suspect proxy divergence, gaming, or Simpson's paradox - Building a metric hierarchy for a team or org (North Star → team-level → input metrics) - Setting up retention cohort analysis to detect PMF erosion early - Evaluating whether a metric improvement is real or an artifact of denominator shift **Do NOT use this skill when:** - You need SaaS finance metric definitions (MRR, ARR, CAC, LTV formulas → use a finance metrics reference) - You need dashboard layout or visualization design (that's a BI/design task) - You need to analyze experiment results that already exist (use the computation scripts directly) - You need customer research methodology (→ Discovery & Research skill — that's primary rese