Explorar skills

Data architecture patterns (warehouse, lake, lakehouse, mesh), ETL/ELT pipelines, streaming architectures, scaling strategies, and schema design patterns

Review dimensions and scoring for root cause analysis quality assessment

Domain modeling with algebraic data types, smart constructors, and type-level error handling

Gerencie favoritos, coleções, tags e destaques do Raindrop.io via API. Use quando o usuário mencionar Raindrop, favoritos, salvar links, organizar URLs, coleções de favoritos ou destaques da web.

Terminal UI and CLI design patterns for product owners. Load when designing command-line tools, interactive terminal applications, or writing CLI-specific acceptance criteria.

Requirements quality critique dimensions for peer review - confirmation bias detection, completeness validation, clarity checks, testability assessment, and priority validation

All /nw-* commands — what they do, when to use them, which agent they invoke. For the buddy agent to help users pick the right command.

Creates a phased roadmap.json for a feature goal with acceptance criteria and TDD steps. Use when planning implementation steps before execution.

Revisar as dimensões e a pontuação para a avaliação da qualidade da análise de causa raiz.

BDD patterns for acceptance test design - Given-When-Then structure, scenario writing rules, pytest-bdd implementation, anti-patterns, and living documentation

Question-first approach to understanding user journeys. Load when starting a new journey design or when the discovery phase needs deepening.

TypeScript/JavaScript property-based testing with fast-check framework and arbitraries