ClaudeSuperPower

product-analytics-setup

Skill

How to actually instrument product analytics correctly. Event taxonomy, property design, naming conventions, schema versioning, identity stitching, funnel design, retention cohorts, North Star metric selection, dashboard hygiene, instrumentation debt, and the failure modes that produce data nobody t

Install

git clone https://github.com/rampstackco/claude-skills.git ~/.claude/skills/product-analytics-setup

What is product-analytics-setup?

How to actually instrument product analytics correctly. Event taxonomy, property design, naming conventions, schema versioning, identity stitching, funnel design, retention cohorts, North Star metric selection, dashboard hygiene, instrumentation debt, and the failure modes that produce data nobody trusts. Triggers on product analytics setup, event taxonomy, tracking plan, instrumentation, schema versioning, North Star metric, retention cohorts, funnel design, naming conventions, instrument new feature, audit existing analytics, dashboard reconciliation, instrumentation debt, Mixpanel setup, Amplitude setup, PostHog setup, warehouse-native analytics. Also triggers when the team has data but cannot trust it, or when designing instrumentation for a new feature, or when auditing an existing setup that has drifted.

What this can do

Capabilities declared in this component's own frontmatter — not inferred.

Inherit all session tools

Declares no tool restrictions — inherits every session tool

~205 tokens of context used while enabled, before you invoke anything

Documentation

README · ~14 min read

Product Analytics Setup

A senior PM and analyst's playbook for instrumenting product analytics correctly the first time.

Most product analytics setups are some combination of inherited mistakes, dashboard sprawl, and events nobody trusts. The team launches a new feature; instrumentation gets bolted on under deadline pressure; naming drifts; properties are inconsistent; six months later nobody can answer simple questions because the answer depends on which event you trust.

This skill is the discipline that prevents that. It assumes you have answered the strategic questions about what to measure (see analytics-strategy). It assumes you have a tool connected (Mixpanel, Heap, PostHog, Amplitude, or warehouse-native via BigQuery, Snowflake, or dbt). The hard part is the systematic execution: naming conventions, property design, schema versioning, funnel construction, cohort definitions, retention measurement.

When to use this skill: setting up product analytics from scratch, auditing an existing instrumentation, fixing a "we have data but cannot trust it" problem, or designing instrumentation for a new feature.


What this skill is for

This skill spans instrumentation execution. It does not cover measurement strategy (use analytics-strategy), experimentation result interpretation (use experimentation-analytics), paid media analytics (use ads-performance-analytics), or platform decisions (use experimentation-platform-orchestrator). Pair this skill with the relevant integrations microsite for your specific tool.

The clean distinction from analytics-strategy. That skill (Growth category) is strategic: what to measure and why, KPI hierarchy, dashboard architecture, attribution models. This skill (Product category) is execution: how to actually instrument the product correctly. The two compose. Read analytics-strategy first to decide what matters; read this skill to instrument it.


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