investigate-metric

Diagnosing a metric change involves orchestrating breakdowns, actor drilldowns, and lifecycle partitioning across trends and funnels. This skill automates variance checks against priors to isolate root causes.

Investigating anomalies in product analytics dashboards is best handled by applying multi-step root-cause analysis. This includes cross-segment comparisons, path analysis, and automated breakdown attribution to surface why the metric shifted.

Yes, you can diagnose why a PostHog trend metric dropped by executing SQL queries and reading data schemas. The skill uses these alongside trend and funnel queries to perform variance checks and isolate the drop.

Performing cohort breakdown attribution for metric variance requires comparing current segments against prior periods. Helper scripts automate this variance check and attribute the metric change to specific cohort breakdowns.

Root-cause analysis works directly with saved insights, dashboards, and pasted queries. You describe the metric change and time window, and the analysis orchestrates breakdowns and comparisons across those inputs.

You should use lifecycle partitioning to investigate a metric change when you need to isolate causes based on user retention and lifecycle stages. It helps cross-segment comparisons by categorizing actors into new, returning, and resurrected groups.