claude

pm-data-analytics

Data analytics skills for PMs: SQL query generation, cohort analysis, A/B test analysis, funnel analysis, event tracking planning, metric definition, and North Star metric definition.

9 on GitHubby Tarun Narangv1.2.0MITupdated 1 month agoproduct-management
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Plugin pm-data-analytics from marketplace pm-skills in tarunccet/pm-skills. Commands read from the repo's manifest — 1 month ago.

What it installs

Skills (7)

  • ab-test-analysis

    Analyze A/B test results with statistical significance, sample size validation, confidence intervals, and ship/extend/stop recommendations. Use when evaluating experiment results, checking if a test reached significance, interpreting split test data, or deciding whether to ship a variant.

  • cohort-analysis

    Perform cohort analysis on user engagement data — retention curves, feature adoption trends, and segment-level insights. Use when analyzing user retention by cohort, studying feature adoption over time, investigating churn patterns, or identifying engagement trends.

  • event-tracking-plan

    Design an analytics instrumentation plan — define key events, properties, naming conventions, and produce a tracking spec document that engineers can implement. Use when setting up analytics for a new product, redesigning a tracking schema, or onboarding engineers to instrumentation work.

  • funnel-analysis

    Analyze a conversion funnel — identify drop-off points, calculate stage-by-stage conversion rates, generate leakage hypotheses, and recommend improvement experiments. Use when diagnosing funnel performance, prioritizing optimization work, or designing experiments to improve conversion.

  • metric-definition

    Define and distinguish operational, vanity, and actionable metrics. Write complete metric specs with name, definition, data source, calculation formula, owner, and review cadence. Use when formalizing metrics, building a metrics dictionary, or aligning a team on how metrics are measured.

  • product-metrics

    Define a complete product metrics framework: North Star metric selection, input metrics, dashboard design, alert thresholds, and — for AI products — a four-layer AI metrics stack (model quality, operational, product-level, business). Use when setting up metrics for a new product, choosing a North St

  • sql-queries

    Generate SQL queries from natural language descriptions. Supports BigQuery, PostgreSQL, MySQL, and other dialects. Reads database schemas from uploaded diagrams or documentation. Use when writing SQL, building data reports, exploring databases, or translating business questions into queries.

Slash commands (7)

  • /pm-data-analytics:analyze-cohorts

    Perform cohort analysis on user data — retention curves, feature adoption, and engagement trends

  • /pm-data-analytics:analyze-test

    Analyze A/B test results — statistical significance, sample size validation, and ship/extend/stop recommendations

  • /pm-data-analytics:data-driven-discovery

    Run a full data-driven product discovery cycle — from defining metrics through funnel analysis and experiments to actionable insights

  • /pm-data-analytics:design-funnel

    Analyze a conversion funnel — identify drop-off points, calculate conversion rates at each stage, generate leakage hypotheses, and recommend improvement experiments

  • /pm-data-analytics:north-star

    Define your North Star Metric and supporting input metrics — classify the business game and validate against best practices

  • /pm-data-analytics:plan-tracking

    Design an analytics event tracking plan — define events, properties, naming conventions, and produce an engineer-ready tracking spec

  • /pm-data-analytics:write-query

    Generate SQL queries from natural language — supports BigQuery, PostgreSQL, MySQL, and more

View source on GitHub

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