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GDPR-compliant data handling with consent management, data subject rights, and privacy controls. \n \n Implements consent management with audit trails, data subject access requests (DSARs), erasure, portability, and rectification workflows \n Provides data retention policies with legal basis tracking, anonymization options, and automated enforcement \n Includes breach notification handling with 72-hour authority reporting and affected individual notification workflows \n Covers privacy-by-design

PostgreSQL-backed GraphQL backend with auto-generated type-safe SDKs for web, mobile, and Flutter. \n \n Define GraphQL schema with @table decorators and relationships; Data Connect generates SQL and GraphQL operations automatically \n Write queries and mutations in GraphQL with filtering, ordering, pagination, and upsert support; transactions available via @transaction \n Secure operations with @auth levels (PUBLIC, USER, NO_ACCESS) and row-level controls using @check and @redact \n Generate ty

Before writing queries, consult references/api-reference.md for endpoints, ID conventions, and data shapes.

The Data Layer coordinates data from multiple sources.

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

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

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

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

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.