data▌

A meta-skill that extracts company-specific data knowledge from analysts and generates tailored data analysis skills.

Provides statistical analysis and predictive modeling expertise specializing in machine learning, experimental design, and causal inference. Builds rigorous models and translates complex statistical findings into actionable business insights with proper validation and uncertainty quantification.

End-to-end analytics: set up tracking, interpret data, analyze funnels, measure product engagement, validate conversion paths, and calculate ROI.

Transform raw data into compelling narratives that drive stakeholder decisions and action. \n \n Three-pillar framework combining data evidence, narrative meaning, and visual clarity to structure insights for maximum impact \n Includes five story templates (Problem-Solution, Trend, Comparison, Executive Summary, One-Page Dashboard) with ready-to-use structures for common business scenarios \n Progressive reveal and annotation techniques to layer complexity, build understanding, and highlight key

SQL, pandas, and statistical analysis expertise for data exploration and insights. \n \n Covers SQL fundamentals through advanced techniques including JOINs, subqueries, CTEs, window functions, and query optimization \n Handles pandas workflows for data manipulation, transformation, grouping, filtering, pivoting, and time series analysis \n Provides statistical capabilities including descriptive statistics, hypothesis testing, correlation analysis, and basic predictive modeling \n Delivers code

Randomize JSON object arrays while validating schema consistency and preventing data corruption. \n \n Validates that all objects share identical property names and structure before shuffling, rejecting files with inconsistencies or nested objects in default mode \n Requires a JSON file as input; pauses and requests data if none is provided \n Supports variable overrides to customize which properties are ignored, which are required, or whether nesting is allowed \n Returns shuffled data with ori

Exploratory Data Analysis (EDA) is the critical first step in data science projects, systematically examining datasets to understand their characteristics, identify patterns, and assess data quality before formal modeling.

Test data generation creates realistic, consistent, and maintainable test data for automated testing. Well-designed test data reduces test brittleness, improves readability, and makes it easier to create diverse test scenarios.

This skill turns raw data and questions into validated, documented models ready for production:

Provides expertise in efficient CSV file processing, data cleaning, and transformation. Handles large files, encoding issues, malformed data, and performance optimization for tabular data workflows.