Data

A structured step-by-step guide for setting up valid A/B tests, enforcing hypothesis locking, statistical power, and metric definition before implementation. Use when you need a rigorous experimentation framework to avoid peeking and invalid results.

Analyze cross-channel campaign data, quantify uncertainty, and propose evidence-labeled budget tests without overstating causality.

Implements event tracking scripts and validates data collection for marketing analytics. Use when setting up analytics on websites or applications.

Performs quality control on single-cell RNA-seq data using scverse best practices with MAD-based filtering and comprehensive visualizations. Use for QC analysis, filtering low-quality cells, or following scanpy best practices.

Answer data questions -- from quick lookups to full analyses. Use when looking up a single metric, investigating what's driving a trend or drop, comparing segments over time, or preparing a formal data report for stakeholders.

Build an interactive HTML dashboard with charts, filters, and tables. Use when creating an executive overview with KPI cards, turning query results into a shareable self-contained report, building a team monitoring snapshot, or needing multiple charts with filters in one browser-openable file.

Create publication-quality visualizations with Python. Turn query results or DataFrames into charts, select the right chart type, and generate plots for reports or presentations. Supports interactive charts with hover and zoom.

Extracts tribal knowledge from data analysts about their company's data warehouse, terminology, metrics, and query patterns, then generates a tailored data analysis skill with reference files.

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.

Profiles and explores a dataset to understand its structure, quality, and patterns. Use when encountering a new table or file to check null rates, column distributions, spot quality issues, and decide which dimensions and metrics to analyze.

Write correct, performant SQL across all major data warehouse dialects (Snowflake, BigQuery, Databricks, PostgreSQL, etc.). Use when writing queries, optimizing slow SQL, translating between dialects, or building complex analytical queries with CTEs, window functions, or aggregations.

Apply statistical methods including descriptive stats, trend analysis, outlier detection, and hypothesis testing. Use when analyzing distributions, testing for significance, detecting anomalies, computing correlations, or interpreting statistical results.