Create standardized metadata for data assets. Use when documenting new datasets, building data catalogs, improving data discoverability, or creating data dictionaries for teams.
Structure analysis approach before starting work. Use when receiving new analysis requests, breaking down complex questions into steps, or planning iterative analysis workflows.
Comprehensive data quality assessment against business rules, schema constraints, and freshness expectations. Activate when validating production pipelines, onboarding new data sources, or when stakeholders report data quality concerns.
Identify distinct customer or user segments based on behavior, attributes, or value. Activate when you need to answer "who are our best customers?" or "what distinct groups exist in our user base?" and need data-driven profiles to inform strategy.
Create effective, publication-ready data visualizations. Use when choosing chart types, designing presentation visuals, building dashboard charts, or applying visual design best practices to data output.
Create a clear, transparent explanation of analytical methodology for any audience level. Activate when you deliver findings that require the audience to trust the method โ A/B tests, attribution models, forecasts, statistical analyses, or anything where "how did you get that?" is a likely question.
Analyse temporal patterns in data including trends, seasonality, anomalies, and forecasting. Activate when you need to understand trends over time, detect seasonality, identify anomalies in time series, or build simple forecasting models for planning.
Translate a SQL query into a plain-language explanation of what the business logic does. Use when documenting queries, onboarding analysts, preparing code reviews, or explaining logic to non-technical stakeholders.