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pm-analytics

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Run a product-analytics analysis that produces a decision, not a dashboard — start from the question, pick actionable over vanity metrics, separate signal from noise, segment (averages lie), pair quant with qual, and end in an insight→impact→action story. Catches the defaults Claude misses: vanity metrics, correlation-as-causation, reacting to small samples/short windows, survivorship/selection bias, unsegmented averages. Trigger when asked to analyze product data/metrics, plan what to measure, build a funnel/cohort/segment/retention analysis, read analytics, or turn data into a recommendation.

pm/processes/analytics/SKILL.md

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Jul 2, 2026
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# 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:

1. **Establish context** — the decision/question this informs, the goal it ladders to, and the data available (these set what to measure and which method).
2. **Apply the always-true core** — question first, actionable metrics, signal vs. noise, segment, quant+qual, end in a recommendation.
3. **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?).
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