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

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

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

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

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

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

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.

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.

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.

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.

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.

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 Star, designing a dashboard, or defining KPIs for an AI feature.

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.