Dados e Análise

This skill should be used when the user asks to "optimize SQL queries", "explore database schemas", "generate migration SQL", "analyze query performance", or "document database structure".

Expert data science covering machine learning, statistical modeling, experimentation, predictive analytics, and advanced analytics. Use when selecting ML algorithms, engineering features, designing A/B tests, evaluating model performance, or building predictive pipelines.

Provides expert-level database design with schema analysis, index optimization, and migration generation. Supports PostgreSQL, MySQL, MongoDB, and DynamoDB. Use when designing schemas, optimizing queries, planning migrations, or analyzing database performance.

Expert business intelligence covering dashboard design, data visualization, reporting automation, and executive insights delivery. Use when designing dashboards, building KPI frameworks, automating scheduled reports, creating data stories for stakeholders, or optimizing BI tool performance.

Design relational database schemas from requirements with normalization, migration planning, ERD generation, RLS policies, index strategies, and type generation. Supports PostgreSQL, MySQL, and SQLite with Drizzle, Prisma, and Alembic migrations. Use when designing new features, reviewing schemas, or adding multi-tenancy.

Data-driven sprint retrospectives with velocity analytics, contributor insights, code quality trends, and actionable improvement recommendations. Analyzes git history to produce comprehensive retrospective reports with session detection, churn hotspots, work pattern analysis, and sprint-over-sprint comparison dashboards.

Expert analytics engineering covering data modeling, dbt development, data transformation, and semantic layer management. Use when building dbt models, designing star schemas, writing staging or mart SQL, configuring data tests, defining semantic-layer metrics, or optimizing warehouse query performance.

This skill should be used when the user asks to "design REST APIs", "optimize database queries", "implement authentication", "build microservices", "review backend code", "set up GraphQL", "handle database migrations", or "load test APIs". Use for Node.js/Express/Fastify development, PostgreSQL optimization, API security, and backend architecture patterns.

Use when designing data architectures, building batch or streaming pipelines, implementing data quality frameworks, optimizing ETL/ELT performance, working with Airflow/dbt/Spark/Kafka, or troubleshooting data pipeline failures. Provides pipeline generation, data quality validation, and SQL/Spark performance optimization.

Design a product metrics dashboard — North Star + input metrics + guardrails — that the team actually uses to make decisions. Distinct from north-star-metric (defines THE one number) by focusing on the full dashboard architecture: layers, owners, cadence, visualization, and the discipline that prevents 30-chart dashboards.

Product analytics framework for PMs and analytics engineers on instrumenting products, defining metrics, building activation/retention funnels, running cohort analyses, and translating product data into decisions. Use when designing a product metric tree, instrumenting a new feature, auditing existing instrumentation, defining North Star + counter-metrics, or building an analytics roadmap.

Audit data quality across pipelines, warehouses, and operational stores. Use when designing a DQ program from scratch, defining DQ dimensions (completeness, accuracy, consistency, timeliness, validity, uniqueness) for a dataset, building rule-based checks (Great Expectations / dbt tests / Soda / custom), detecting schema drift, monitoring freshness SLAs, responding to a DQ incident, or auditing an