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Found 196 Skills
Advanced Domain-Driven Design (DDD) Integration Patterns. Use this skill for implementing CQRS, Event Sourcing, the Outbox Pattern, and Anti-Corruption Layers (ACL) in distributed systems.
Guidance for recovering PyTorch model architectures from state dictionaries, retraining specific layers, and saving models in TorchScript format. This skill should be used when tasks involve reconstructing model architectures from saved weights, fine-tuning specific layers while freezing others, or converting models to TorchScript format.
Validate at every layer data passes through to make bugs impossible. Use when invalid data causes failures deep in execution, requiring validation at multiple system layers.
AI trustworthiness testing using OWASP AI Testing Guide v1. Execute 44 test cases across 4 layers (Application, Model, Infrastructure, Data) with practical payloads and remediation.
Use when designing data ownership, validation boundaries, consistency models, or configuration strategy in Python. Also use when encountering unclear ownership across modules, shared mutable state leaking between layers, validation gaps at ingress, cross-module transactional coupling, or config drift between environments.
Generate Flutter applications using Clean Architecture with feature-first structure, Riverpod state management, Dio + Retrofit for networking, and fpdart error handling. Use this skill when creating Flutter apps, implementing features with clean architecture patterns, setting up Riverpod providers, handling data with Either type for functional error handling, making HTTP requests with type-safe API clients, or structuring projects with domain/data/presentation layers. Triggers include "Flutter app", "clean architecture", "Riverpod", "feature-first", "state management", "API client", "Retrofit", "Dio", "REST API", or requests to build Flutter features with separation of concerns.
Security patterns for authentication, defense-in-depth, input validation, OWASP Top 10, LLM safety, and PII masking. Use when implementing auth flows, security layers, input sanitization, vulnerability prevention, prompt injection defense, or data redaction.
Guidance for implementing tensor parallelism in PyTorch, including ColumnParallelLinear and RowParallelLinear layers. This skill should be used when implementing distributed tensor parallel operations, sharding linear layers across multiple GPUs, or simulating collective operations like all-gather and all-reduce for parallel computation.
Load PROACTIVELY when task involves building a complete feature across multiple layers. Use when user says "build a feature", "add user profiles", "create a dashboard", or any request spanning database, API, UI, and tests. Orchestrates multi-agent work sequentially: schema and migrations, API endpoints, UI components, tests, and review. Handles dependency ordering and cross-layer type sharing.
Effect-TS best practices for services, errors, layers, schemas, and testing. Use when writing/reviewing Effect code, implementing services, handling errors, or composing layers.
Build complete React features with proper layered architecture including UI components, business logic, API integration, and state management. Use this skill when users request implementing features like "user authentication", "shopping cart", "product listing", "file upload", or any complete functionality that requires UI + business logic + data fetching. Generates all layers - presentation (components), business logic (hooks/stores/validation), and data access (API calls/React Query). Integrates with react-component-generator for UI and provides production-ready, maintainable code following best practices.
Full pipeline audio system generator. Use when building complete game audio systems that span MetaSounds + Blueprint + Wwise layers, generating AAA project structures, or orchestrating multi-layer audio from a natural language description.