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, Am
Profile →npx skills add rampstackco/claude-skills --skill product-analytics-setup --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
What it does
Instruments product analytics execution, focusing on event taxonomy, property design, naming conventions, schema versioning, identity stitching, funnel design, retention cohorts, North Star metric selection, dashboard hygiene, instrumentation debt, and related failure modes. 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.
How it works
The skill provides an execution framework for instrumenting analytics from the ground up. It defines an instrumentation hierarchy where events are the atomic facts, properties describe events, identities map events to people, cohorts filter across events, funnels are sequences of events, and retention measures repeat behavior over time. It prescribes event taxonomy rules (past tense, object-action format, granular but non-redundant), distinguishes event-level vs user-level properties, and enforces naming conventions (snake_case, object-action format, verb-noun for user properties). It explains schema versioning practices (additive changes vs breaking changes, versioning with _v2, keeping old events during transitions), and outlines funnel design rules (order, time windows, interpretation of drop-offs, anchor events). It covers cohort definitions, retention measurement flavors, and North Star metric selection guidelines, including a minimal set of supporting metrics. It emphasizes trustable dashboard design with inline definitions, known data sources, caveats, and reproducibility, plus governance like owners and refresh cadence. It discusses instrumentation debt as the cost of delayed or skipped instrumentation and provides concrete pattern references for related topics.
When to use it
Trigger when setting up product analytics from scratch, auditing existing instrumentation, addressing a data trust issue, or designing instrumentation for a new feature.
What it can touch
Tools and setups mentioned include Mixpanel, Amplitude, PostHog, and warehouse-native analytics via BigQuery, Snowflake, or dbt. It also references dashboards, data sources (e.g., fct_orders, dim_customers), and typical analytics artifacts like events, properties, identities, cohorts, funnels, and NSM definitions.
Caveats
Describes risks and limitations around dashboard drift, schema changes, and the need for migrations during breaking changes. It emphasizes documenting definitions, data sources, and caveats, and it notes the importance of reproducibility and governance to prevent stale or misleading analytics.
# 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
- What this skill is for
- The instrumentation hierarchy
- Event taxonomy design
- Property design: event-level vs user-level
- Naming conventions
- Schema versioning
- Funnel design
- Cohort definitions
- Retention measurement
- North Star and supporting metrics
- The trustable dashboard principle
- Instrumentation debt
- Common failure modes
- The framework: 12 considerations for trustable product analytics
What does the product-analytics-setup skill do?
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, Am
How do I install it?
Run `npx skills add rampstackco/claude-skills --skill product-analytics-setup --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From rampstackco/claude-skills, a repository with 515 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.