Results for “model-architecture”

Generate architecture documentation using C4 model Mermaid diagrams. Use when asked to create architecture diagrams, document system architecture, visualize software structure, create C4 diagrams, or generate context/container/component/deployment diagrams. Triggers include "architecture diagram", "C4 diagram", "system context", "container diagram", "component diagram", "deployment diagram", "document architecture", "visualize architecture".

Models threats for a service, feature, endpoint, integration, or architecture: assets, attackers, boundaries, flows, and abuse cases.

Generate architecture documentation using C4 model Mermaid diagrams. Use when asked to create architecture diagrams, document system architecture, visualize software structure, create C4 diagrams, or generate context/container/component/deployment diagrams. Triggers include "architecture diagram", "C4 diagram", "system context", "container diagram", "component diagram", "deployment diagram", "document architecture", "visualize architecture".

Guides project-level decisions for LLM-powered systems: task-model fit, pipeline architecture, token and cost estimation, and agent-assisted iteration.

Train Mixture of Experts (MoE) models using DeepSpeed or HuggingFace. Use when training large-scale models with limited compute (5× cost reduction vs dense models), implementing sparse architectures like Mixtral 8x7B or DeepSeek-V3, or scaling model capacity without proportional compute increase. Covers MoE architectures, routing mechanisms, load balancing, expert parallelism, and inference optimization.

Train Mixture of Experts (MoE) models using DeepSpeed or HuggingFace. Use when training large-scale models with limited compute (5× cost reduction vs dense models), implementing sparse architectures like Mixtral 8x7B or DeepSeek-V3, or scaling model capacity without proportional compute increase. Covers MoE architectures, routing mechanisms, load balancing, expert parallelism, and inference optimization.

Provides domain knowledge on the Causal Dynamics Architecture (CDA), an alternative AI computing architecture based on causal graphs and Hamiltonian dynamics, with references for deep dives.

Train and run Mamba state-space models with O(n) complexity, achieving faster inference and longer context than Transformers.

Serverless is a billing-and-scaling model, not a badge of modernity.

Analyzes codebases in depth and produces structured markdown analysis documents covering architecture, module design, call flows, data models, and design patterns.

Estructura aplicaciones Flutter en módulos independientes y reutilizables con flutter_modular, incluyendo diseño de paquetes, dependencias y configuración de Melos.

Train machine learning models end-to-end, covering data loading, preprocessing, architecture selection, training loops, validation, and checkpointing. Use when the user requests model training or provides relevant inputs for this workflow.

Architectural decision-making framework.

Compare tradeoffs and recommend architectural patterns — dependency injection, service-oriented architecture, repository, domain events, circuit breaker, and anti-corruption layer. Use when choosing between design patterns, planning microservices boundaries, evaluating system design alternatives, or asking 'which pattern should I use' for a specific coupling or resilience problem.

Explain and apply core ML/AI concepts — model types, training pipelines, evaluation metrics, and neural architectures.

Design high-level system architecture, create Architecture Decision Records (ADRs), evaluate technology trade-offs, and plan for scalability.

Application architecture patterns and code organization — clean architecture, hexagonal architecture, feature-based structure, dependency inversion, module boundaries, SOLID principles at module scale. Activate on "clean architecture", "hexagonal architecture", "feature-based structure", "dependency inversion", "module boundaries", "architecture pattern", "folder structure", "fat controller", "circular dependency", "IoC container". NOT for microservice decomposition (use microservices-patterns) or database schema design (use database-design-patterns).

BDI (Beliefs-Desires-Intentions) agency framework for designing autonomous agents with mental state architectures

Applies SOLID principles, clean architecture layers, dependency injection, and patterns like CQRS and event sourcing to design scalable, maintainable systems.

Performs STRIDE-A threat model analysis of repositories and systems, producing architecture overviews, DFD diagrams, prioritized findings, and executive assessments. Supports both single analysis and incremental updates with change tracking.

Evaluates Power BI data model architecture, relationships, storage modes, and performance to identify optimization opportunities and ensure adherence to best practices.

Manage machine learning models in the Agent Platform Model Registry: list, describe, upload, update, and delete models and their versions.

Use when designing a new system, choosing between architectural patterns, planning how components will connect, evaluating scalability and reliability trade-offs, or reviewing an existing system for structural problems. For system design, service boundaries, database design, API structure, monolith vs microservices decisions, and architecture decision records (ADRs). Activate when user says "how should I structure this", "design the system", "is this architecture right", or "plan the backend".

Designs bounded contexts, creates domain models, uses aggregates, and implements value objects for complex domain logic.

Designs the data model, API contracts, and structural foundation of the system.

Agent skill for architecture - invoke with $agent-architecture

Guides module boundaries, dependency direction, and domain structure. Do not use for local edits that leave architecture unchanged.

Architectural decision-making framework. Requirements analysis, trade-off evaluation, ADR documentation. Use when making architecture decisions or analyzing system design.

Architectural decision-making framework. Requirements analysis, trade-off evaluation, ADR documentation. Use when making architecture decisions or analyzing system design.

Enforces system design, project structure, dependency management, error handling, testing, and documentation standards at principal-engineer level.