Agent skill · data analytics · alirezarezvani
product-analytics
Use when defining product KPIs, building metric dashboards, running cohort or retention analysis, or interpreting feature adoption trends across product stages.
Why this skill is useful
Provides domain-specific frameworks and methodologies for product metrics that the AI wouldn't reliably generate on its own.
What it needs
About 3k tokens when loaded. Last updated 2026-08-05. 23,939 stars on the source repository.
What this skill does
Product Analytics Define, track, and interpret product metrics across discovery, growth, and mature product stages. When To Use Use this skill for: Metric framework selection (AARRR, North Star, HEART) KPI definition by product stage (pre-PMF, growth, mature) Dashboard design and metric hierarchy Cohort and retention analysis Feature adoption and funnel interpretation Workflow 1. Select metric framework AARRR for growth loops and funnel visibility North Star for cross-functional strategic alignment HEART for UX quality and user experience measurement 2. Define stage-appropriate KPIs Pre-PMF: activation, early retention, qualitative success Growth: acquisition efficiency, expansion, conversion velocity Mature: retention depth, revenue quality, operational efficiency 3. Design dashboard layers Executive layer: 5-7 directional metrics Product health layer: acquisition, activation, retention, engagement Feature layer: adoption, depth, repeat usage, outcome correlation 4. Run cohort + retention analysis Segment by signup cohort or feature exposure cohort Compare retention curves, not single-point snapshots Identify inflection points around onboarding and first value moment 5. Interpret and act Connect metric movement to product changes and release timeline Distinguish signal from noise using period-over-period context Propose one clear product action per major metric risk/opportunity KPI Guidance By Stage Pre-PMF Activation rate Week-1 retention Time-to-first-value Problem-solution fit interview score Growth Funnel conversion by stage Monthly retained users Feature adoption among new cohorts Expansion / upsell proxy metrics Mature Net revenue retention aligned product metrics Power-user share and depth of use Churn risk indicators by segment Reliability and support-deflection product metrics Dashboard Design Principles Show trends, not isolated point estimates. Keep one owner per KPI. Pair each KPI with target, threshold, and decision rule. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills alirezarezvani/product-analytics--0460d5