nWave-ai

Rust property-based testing with proptest, quickcheck, and bolero frameworks

Evidence collection methods, problem categorization, analysis techniques, and solution design patterns

Core UX principles for product owners. Load when evaluating interface designs, writing acceptance criteria with UX requirements, or reviewing wireframes and mockups.

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

Testes de propriedades em Rust usando os frameworks proptest, quickcheck e bolero.

Review dimensions for acceptance test quality - happy path bias, GWT compliance, business language purity, coverage completeness, walking skeleton user-centricity, priority validation, observable behavior assertions, traceability coverage, and walking skeleton boundary proof

Wave methodology knowledge for the buddy agent — what each wave does, its inputs and outputs, and how to route questions.

Tool safety protocols, adversarial output validation, error recovery patterns, and I/O contracts for research operations

Evaluation criteria and scoring for data engineering artifact reviews

Selects a quality-vs-token-consumption profile (lean, standard, thorough, exhaustive, custom, inherit) and persists it globally (~/.nwave/global-config.json) or per-project (.nwave/des-config.json). Use when tuning how much rigor wave commands apply.

Kotlin language-specific patterns with Arrow, Raise DSL, and coroutine-based effects

Stateful property-based testing patterns, model-based testing, and anti-patterns