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.

Zur Installation springen Dateien prüfen

Repository: pproenca/dot-skills
Letzte Quellaktivität: 27. Mai 2026 um 09:50
Erkannte Sprache von SKILL.md: Englisch
Sterne: 215
Forks: 19

Installationsoptionen

Standardmäßig ist der Prompt ausgewählt, der zuerst die Quelle prüft. Sie können zu einem direkten Befehl wechseln oder eine lokale Kopie herunterladen.

Mit einem KI-Assistenten installieren

Diesen Prompt kopieren und in den verwendeten KI-Assistenten einfügen.

Follow https://skillsmp.com/skill-install/prompt.md to install only the Agent Skill at https://github.com/pproenca/dot-skills/tree/master/skills/.experimental/ios-xcode.

Ein direkter Befehl überspringt den Prüf-Prompt. Prüfen Sie die Quelle, bevor Sie ihn ausführen.

npx skills add https://github.com/pproenca/dot-skills --skill ios-xcode

Der Befehl bleibt in einer Zeile. Scrollen Sie horizontal, um ihn vor dem Kopieren vollständig zu prüfen.

Sie bevorzugen eine lokale Kopie? Laden Sie die Dateien herunter, die SkillsMP derzeit vorliegen.

Quelldateien prüfen

Lesen Sie SKILL.md und alle von SkillsMP angezeigten Begleitdateien, bevor Sie sich für eine Installation entscheiden.

Datei-Explorer

23 Dateien

SKILL.md

Quellanweisungen · Schreibgeschützte Vorschau

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 |

Auf GitHub ansehen

Zurück zur Suche Zurück zur Installation

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

Alle aus diesem Repository ansehen

ios-xcode

Quellinformationen

Installationsoptionen

Mit einem KI-Assistenten installieren

Quelldateien prüfen

Mehr aus diesem Repository

Mit einem KI-Assistenten installieren

Mehr aus diesem Repository