Individual skill
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.
dist/codex/.agents/skills/product-analytics-setup/SKILL.md
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- Last checked
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- Repository last updated
- Oct 27, 2026
- License
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- claude Repository topic
- claude-skills Repository topic
- anthropic Repository topic
- mcp Repository topic
- seo Repository topic
- web-development Repository topic
- agent-skills Repository topic
- claude-code Repository topic
- product-management Repository topic
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- data Derived category
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SKILL.mdcore · 23,480 bytesSKILL.mdcore · 23,480 bytesSKILL.mdcore · 23,480 bytesreferences/_claude-frontmatter-extras.yamlasset · 557 bytesreferences/cohort-definition-patterns.mddoc · 7,519 bytesreferences/common-failures.mddoc · 9,554 bytesreferences/event-taxonomy-template.mddoc · 7,741 bytesreferences/funnel-design-templates.mddoc · 7,741 bytesreferences/instrumentation-audit-checklist.mddoc · 7,398 bytesreferences/naming-convention-reference.mddoc · 5,956 bytesreferences/north-star-metric-selection.mddoc · 8,027 bytesreferences/property-design-patterns.mddoc · 6,781 bytesreferences/schema-versioning-patterns.mddoc · 7,061 bytes
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# 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.
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## What this skill is for