Skill UI

This skill guides the architectural definition of complex business domains using Domain-Driven Design (DDD). It helps in defining core subdomains, separating services by bounded contexts, and establishing a unified ubiquitous language. It is essential for decomposing monoliths, structuring microservices, and aligning team ownership with clear domain boundaries.

This skill guides the planning and execution of complex business domains using Domain-Driven Design (DDD) principles. It helps developers move from strategic modeling (defining subdomains and bounded contexts) to tactical implementation patterns like CQRS, Event Sourcing, and Saga orchestration. Use it when dealing with complex, high-variability business rules and needing explicit architectural boundaries.

This skill establishes a rigorous framework for building deep architectural context by performing ultra-granular, line-by-line analysis of code. It applies methodologies like First Principles and 5 Whys to maintain a stable, evolving mental model of the entire system, making it essential for pre-vulnerability security auditing, threat modeling, and complex architecture reviews.

This skill implements the BDI (Belief-Desire-Intention) architecture for advanced cognitive agent modeling. It allows agents to reason about context by transforming external knowledge (like RDF triples) into internal mental states (beliefs, desires, intentions) and vice versa. Key features include goal-directed planning, explainable reasoning chains, and T2B2T (Triples-to-Beliefs-to-Triples) flow, making it ideal for multi-agent systems and augmenting LLMs with formal cognitive structures.

Automates the scaffolding of a complete Aggregate Root structure within a specified Bounded Context, adhering strictly to Domain-Driven Design (DDD) principles. It generates necessary files including the entity, supporting value objects, repository interface, domain event definitions, and comprehensive unit test stubs, significantly accelerating the modeling of new business concepts and enforcing domain invariants.

A comprehensive guide for planning rigorous scientific studies in Earth and Planetary Sciences, covering sampling, analytical campaigns, experimental runs, and modeling. It ensures that research designs are robust, addressing critical elements like sample context, traceability to reference materials, replication levels, and falsifiability before data collection begins. Essential for high-impact scientific publishing.

This guide provides methodological rigor for designing studies—including manipulative experiments, observational gradient analyses, and process modeling—in the context of global change biology. It helps researchers anticipate and address critical reviewer concerns regarding scale, replication, confounding factors, and the limits of causal inference, ensuring the design can support strong claims of driver-to-response relationships.

This guide provides a rigorous framework for advanced econometric modeling in quantitative economics. It covers time series, panel data, cointegration, and mixed-frequency techniques (VAR, VECM, GMM, MIDAS). It emphasizes proper model diagnostics, stationarity testing, and grounding models in specific data properties and economic contexts.