data

Answer data questions -- from quick lookups to full analyses

Build an interactive HTML dashboard with charts, filters, and tables

Create publication-quality visualizations with Python

Profile and explore a dataset to understand its shape, quality, and patterns

QA an analysis before sharing -- methodology, accuracy, and bias checks

Generate typed data contracts from an OpenAPI spec so a UI agent can build data-driven interfaces without Claude ever seeing the underlying data. Triggers: "data contract", "API schema for UI", "wire up the API", "generate types from OpenAPI", "connect UI to API", "API integration layer" Use when the user wants Claude to understand an API's shape and produce typed interfaces, endpoint metadata, or fetch blueprints that a separate generative-UI agent can consume. Claude reads the OpenAPI spec, extracts endpoint signatures and response schemas, and outputs data contracts — never making live API calls or accessing real data.

Generate or improve a company-specific data analysis skill by extracting tribal knowledge from analysts. BOOTSTRAP MODE - Triggers: "Create a data context skill", "Set up data analysis for our warehouse", "Help me create a skill for our database", "Generate a data skill for [company]" → Discovers schemas, asks key questions, generates initial skill with reference files ITERATION MODE - Triggers: "Add context about [domain]", "The skill needs more info about [topic]", "Update the data skill with [metrics/tables/terminology]", "Improve the [domain] reference" → Loads existing skill, asks targeted questions, appends/updates reference files Use when data analysts want Claude to understand their company's specific data warehouse, terminology, metrics definitions, and common query patterns.

Profile and explore datasets to understand their shape, quality, and patterns before analysis. Use when encountering a new dataset, assessing data quality, discovering column distributions, identifying nulls and outliers, or deciding which dimensions to analyze.

QA an analysis before sharing with stakeholders — methodology checks, accuracy verification, and bias detection. Use when reviewing an analysis for errors, checking for survivorship bias, validating aggregation logic, or preparing documentation for reproducibility.

Create effective data visualizations with Python (matplotlib, seaborn, plotly). Use when building charts, choosing the right chart type for a dataset, creating publication-quality figures, or applying design principles like accessibility and color theory.