pm-analytics
Product Analytics Skill
How this skill behaves (read first)
This is a generative process skill (it plans or performs an analysis, and can critique one). Analytics is where an AI assistant produces confident, wrong conclusions: Claude reports vanity metrics (page views, total signups), treats correlation as causation, reacts to a two-day spike or an n=20 sample as if it were a trend, quotes the average (which hides every interesting subgroup), and stops at "here are the numbers" instead of a decision. A dashboard nobody acts on, or an insight built on a bias, is worse than no analysis.
So this skill gates:
- Establish context — the decision/question this informs, the goal it ladders to, and the data available (these set what to measure and which method).
- Apply the always-true core — question first, actionable metrics, signal vs. noise, segment, quant+qual, end in a recommendation.
- Surface the context-dependent decisions (analysis method, leading vs. lagging, tooling, attribution model, cohort type, retention window) with trade-offs; let the user choose.
Then it hands off to pm-okr-metric-validity-audit (are the chosen metrics valid, not vanity?) and pm-assumption-rigor-audit (do the causal claims and read-outs survive scrutiny?).
Scope: this skill owns the analysis process — what to measure, funnels/cohorts/segments/journeys, reading signal from noise, and the data-to-decision story. It defers the rigorous validity of a metric's definition to pm-okr-metric-validity-audit, the statistics of a controlled change (sample size, significance, guardrails) to pm-experimentation-ab, OKR/KPI artifacts to pm-okrs-kpis, and the discovery research that generates qualitative "why" to pm-discovery.