modeling-product-usage-metrics

modeling-product-usage-metrics is a Product analytics & metrics skill for AI agents, published by PostHog in the posthog.com repository on GitHub. Skillspace rates modeling-product-usage-metrics D (62/100) and it passed the safety scan; the rating is based on static analysis only.

Build reusable product-usage and engagement models — retention, stickiness, and lifecycle — on either PostHog data-warehouse views (HogQL) or an external dbt project. Use when the user wants to model, define, or compute whether users come back (retention / churn), how frequently they engage (stickiness / power users / DAU-WAU-MAU ratio), or the composition of the active base (new / returning / resurrecting / dormant lifecycle). These three are one engagement family sharing a start-event/return-event vocabulary and an interval granularity; this skill treats them together and helps pick the right lens: retention for the return-rate cohort matrix, stickiness for the frequency distribution, lifecycle for growth quality. On PostHog, model them in HogQL (mirroring query-retention / query-stickiness / query-lifecycle); in dbt, build fct_retention / fct_stickiness / fct_lifecycle marts with tests. Read modeling-warehouse-foundations first; feeds the retention validation used by…

Grade
D
Quality
62 / 100
Safety
Passed
GitHub stars
1,138

Unproven: rated by static analysis only, not yet compared head-to-head.

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Our review

Architecture
78 / 100
Maturity
Structured: ships references or scripts
Security scan
No issues found
License
Not declared. Check the repository before reusing it.

Not checked yet

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