ios-xcode

Xcode setup and tooling guidance for iOS 26 / Swift 6.2 clinic modular MVVM-C projects covering project configuration, SwiftData container wiring, testing, debugging, profiling, and distribution workflows. Use when configuring App-target infrastructure or day-to-day tooling around clinic architecture modules.

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저장소: pproenca/dot-skills
최근 소스 활동: 2026년 5월 27일 09:50
감지된 SKILL.md 언어: 영어
스타: 220
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SKILL.md

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name: ios-xcode
description: Xcode setup and tooling guidance for iOS 26 / Swift 6.2 clinic modular MVVM-C projects covering project configuration, SwiftData container wiring, testing, debugging, profiling, and distribution workflows. Use when configuring App-target infrastructure or day-to-day tooling around clinic architecture modules.

# iOS Xcode & Tooling Best Practices Comprehensive guide for Xcode project configuration, SwiftData persistence, testing, debugging, profiling, and app distribution. Contains 19 rules across 6 categories. ## Clinic Architecture Contract (iOS 26 / Swift 6.2) All guidance in this skill assumes the clinic modular MVVM-C architecture: - Feature modules import `Domain` + `DesignSystem` only (never `Data`, never sibling features) - App target is the convergence point and owns `DependencyContainer`, concrete coordinators, and Route Shell wiring - `Domain` stays pure Swift and defines models plus repository, `*Coordinating`, `ErrorRouting`, and `AppError` contracts - `Data` owns SwiftData/network/sync/retry/background I/O and implements Domain protocols - Read/write flow defaults to stale-while-revalidate reads and optimistic queued writes - ViewModels call repository protocols directly (no default use-case/interactor layer) ## When to Apply Reference these guidelines when: - Setting up Xcode projects with AppStorage, ScenePhase, or widgets - Implementing SwiftData models, queries, and CRUD operations - Writing tests with Swift Testing framework - Debugging with breakpoints and console output - Profiling performance with Instruments - Distributing apps via TestFlight - Building for visionOS or integrating ML features ## Rule Categories by Priority | Priority | Category | Impact | Prefix | |----------|----------|--------|--------| | 1 | SwiftData & Persistence | CRITICAL | `data-` | | 2 | Project & Platform | HIGH | `platform-` | | 3 | Testing | HIGH | `test-` | | 4 | Debugging & Profiling | MEDIUM-HIGH | `debug-`, `perf-` | | 5 | Distribution | MEDIUM | `dist-` | | 6 | Specialty Platforms | MEDIUM | `ml-`, `spatial-` | ## Quick Reference ### 1. Project & Platform (HIGH) - [`platform-app-storage`](references/platform-app-storage.md) - Use AppStorage for user preferences - [`platform-scene-phase`](references/platform-scene-phase.md) - Respond to app lifecycle with ScenePhase - [`platform-widget-integration`](references/platform-widget-integration.md) - Design for widget and Live Activity integration - [`platform-system-features`](references/platform-system-features.md) - Integrate system features natively ### 2. SwiftData & Persistence (CRITICAL) - [`data-model-macro`](references/data-model-macro.md) - Define models with @Model macro - [`data-query-for-fetching`](references/data-query-for-fetching.md) - Use @Query for fetching data - [`data-model-container`](references/data-model-container.md) - Configure model containers - [`data-relationships`](references/data-relationships.md) - Define model relationships - [`data-crud-operations`](references/data-crud-operations.md) - Implement CRUD operations ### 3. Testing (HIGH) - [`test-swift-testing`](references/test-swift-testing.md) - Use Swift Testing framework - [`test-preview-sample-data`](references/test-preview-sample-data.md) - Create preview sample data - [`test-preview-macro`](references/test-preview-macro.md) - Use #Preview macro for rapid iteration ### 4. Debugging & Profiling (MEDIUM-HIGH) - [`debug-breakpoints`](references/debug-breakpoints.md) - Use breakpoints for debugging - [`debug-console-output`](references/debug-console-output.md) - Use console output for debugging - [`perf-instruments-profiling`](references/perf-instruments-profiling.md) - Profile SwiftUI with Instruments ### 5. Distribution (MEDIUM) - [`dist-testflight`](references/dist-testflight.md) - Distribute via TestFlight - [`dist-app-icons`](references/dist-app-icons.md) - Design app icons for distribution ### 6. Specialty Platforms (MEDIUM) - [`ml-natural-language`](references/ml-natural-language.md) - Integrate Natural Language ML - [`spatial-visionos-windows`](references/spatial-visionos-windows.md) - Build for visionOS spatial computing ## How to Use Read individual reference files for detailed explanations and code examples: - [Section definitions](references/_sections.md) - Category structure and impact levels - [Rule template](assets/templates/_template.md) - Template for adding new rules ## Reference Files | File | Description | |------|-------------| | [references/_sections.md](references/_sections.md) | Category definitions and ordering | | [assets/templates/_template.md](assets/templates/_template.md) | Template for new rules |

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ray-llm

LLM workloads on open-source Ray (pinned to 2.57) — OpenAI-compatible serving with ray.serve.llm (vLLM-backed LLMConfig + build_openai_app) and batch inference with ray.data.llm (build_processor). Corrects the stale defaults a model produces — the archived ray-llm repo and its YAML configs, hand-rolled vLLM engines inside plain Serve deployments, the removed build_llm_processor name, deprecated boolean stage flags, top-level LLMServer/LLMRouter imports, free-form accelerator strings, one-deployment-per-LoRA-adapter designs — with the 2.57 idioms (stage configs, placement_group_config over hand-rolled PGs, deployment_config autoscaling, dynamic LoRA multiplexing, prefix-cache-affinity routing, the full OpenAI endpoint surface). Use when writing, reviewing, or productionizing LLM serving or batch inference on Ray. Classic-ML Ray (Train/Tune/Data/Serve/clusters) lives in the sibling ray skill.

2026-08-15

ray

Production Ray (open-source, pinned to 2.57) for classic-ML workloads from training to serving — Ray Train, Tune, Data, Serve, Core, and cluster deployment on KubeRay. Corrects the older-corpus defaults a model reaches for (ray.air session reporting, Trainer-inside-Tuner, tune.run, map_batches concurrency=, DatasetPipeline/to_torch, max_concurrent_queries, RayServeHandle + ray.get, Deployment.deploy, ray.state, ray.get-in-a-loop) with the 2.57 idioms that replaced them (Train V2 defaults, driver-function tuning, compute strategies, streaming datasets, DeploymentHandle/DeploymentResponse, serve build/deploy, ray.util.state, KubeRay CRDs and Jobs API). Use when writing, reviewing, or productionizing Python code that touches Ray distributed training, data pipelines, hyperparameter tuning, model serving, or Ray cluster operations. LLM serving/batch-inference on Ray lives in the sibling ray-llm skill.

2026-08-15

mlflow-3

MLflow 3 (open-source, pinned to 3.15) for classic-ML MLOps — logging and registering models, promoting versions across dev/staging/prod, standing up a tracking server, evaluating with gates, and serving. Corrects the MLflow 2-era defaults a model reaches for (artifact_path, registry stages and get_latest_versions, top-level mlflow.evaluate with baseline_model, runs-URI registration, pickle serialization, mlruns file stores, MLServer serving) with the MLflow 3 idioms that replaced them (named LoggedModels, aliases and copy_model_version, models.evaluate plus validate_evaluation_results, skops/torch.export defaults, database backends, the FastAPI scoring server). Use when writing, reviewing, or migrating Python code that touches MLflow tracking, the model registry, evaluation, or serving.

2026-08-15

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