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Found 217 Skills
Use when choosing Core Data vs SwiftData, setting up the Core Data stack, modeling relationships, or implementing concurrency patterns - prevents thread-confinement errors and migration crashes
Concurrency exploitation — race conditions, TOCTOU vulnerabilities, and parallel request abuse in web applications.
Idiomatic Rust patterns, ownership, error handling, traits, concurrency, and best practices for building safe, performant applications.
How WAL mechanics, checkpointing, concurrency rules, recovery work in tursodb
Use when needing thread-safe primitives for performance-critical code. Covers Mutex (iOS 18+), OSAllocatedUnfairLock (iOS 16+), Atomic types, when to use locks vs actors, deadlock prevention with Swift Concurrency.
Expert Haskell engineer specializing in advanced type systems, pure functional design, and high-reliability software. Use PROACTIVELY for type-level programming, concurrency, and architecture guidance.
Master iOS development foundations - Architecture, lifecycle, memory, concurrency
Expert Combine decisions for iOS/tvOS: when Combine vs async/await, Subject selection trade-offs, operator chain design, and memory management patterns. Use when implementing reactive streams, choosing between concurrency models, or debugging Combine memory leaks. Trigger keywords: Combine, Publisher, Subscriber, Subject, PassthroughSubject, CurrentValueSubject, async/await, AnyCancellable, sink, operators, reactive
Rust event-driven system programming best practices for async runtimes, channels, sockets, terminals, and concurrency. This skill should be used when writing, reviewing, or refactoring Rust applications with async I/O, multi-threading, terminal interfaces, or network communication. Triggers on tasks involving tokio, async/await, channels, sockets, TTY handling, signals, and streaming I/O.
Use when implementing async operations with Kotlin coroutines, Flow, StateFlow, or managing concurrency in Android apps.
Checklists and anti-patterns for reviewing Go code. Covers API design, error handling, concurrency, interfaces, safety, performance, naming, testing, functional options, logging, and deterministic simulation testing.
Production Python engineering patterns covering architecture, observability, testing, performance/concurrency, and core practices. Use when designing Python systems, implementing async/sync APIs, setting up monitoring, structuring tests, optimizing performance, or following Python best practices.