Code generator skills that produce production-ready Swift code for common app components. Use when user wants to add logging, analytics, onboarding, review prompts, networking, authentication, paywalls, settings, persistence, error monitoring, CI/CD pipelines, localization, push notifications, deep linking, testing, accessibility, widgets, feature flags, app icons, image caching, pagination, HTTP caching, share cards, social export, subscription lifecycle, referral systems, watermarks, streak tracking, milestone celebrations, what's new screens, lapsed user re-engagement, usage insights, variable rewards, consent flows, account deletion, permission priming, force updates, state restoration, debug menus, offline queues, feedback forms, announcement banners, quick win sessions, Spotlight indexing, App Clips, screenshot automation, background processing, app extensions, data export, or SwiftUI preview sample data and variant matrices.
npx skills add https://github.com/rshankras/claude-code-apple-skills --skill generators
Production-ready code generators for iOS and macOS apps. Unlike advisory skills (review, audit), these skills generate working code tailored to your project.
Use this skill when the user:
Before generating code, skills will:
Provider-dependent code uses protocols for easy swapping:
protocol AnalyticsService { ... }
class TelemetryDeckAnalytics: AnalyticsService { ... }
class FirebaseAnalytics: AnalyticsService { ... }
// Change provider by swapping ONE line
Skills detect iOS vs macOS and App Store vs direct distribution to generate appropriate code.
Read relevant module files based on the user's needs:
Replace print() statements with structured os.log/Logger.
Protocol-based analytics with swappable providers.
Value-moment-first onboarding — races a new user to the first felt experience of the app's promised outcome.
Smart App Store review prompts.
Protocol-based API client with async/await.
Complete authentication flow.
StoreKit 2 subscription paywall.
Complete settings screen with modular sections.
SwiftData persistence with optional iCloud sync.
Protocol-based crash/error reporting.
CI/CD configuration for automated builds and deployment.
Internationalization (i18n) infrastructure for multi-language apps.
Push notification infrastructure with APNs setup.
Deep linking with URL schemes, Universal Links, and App Intents.
Test templates for unit, integration, and UI tests.
Sample data and a multi-variant #Preview matrix for SwiftUI views.
Accessibility infrastructure for inclusive apps.
WidgetKit widgets for home screen and lock screen.
Programmatic app icon generation using CoreGraphics.
Feature flag infrastructure with local and remote support.
ActivityKit Live Activity with Dynamic Island and Lock Screen.
TipKit inline and popover tips with rules and testing.
CKSyncEngine-based CloudKit sync (iOS 17+).
HTTP caching layer with Cache-Control, ETag, and offline fallback.
Pagination infrastructure with offset and cursor patterns.
Image loading pipeline with caching and CachedAsyncImage view.
Shareable image cards for social media sharing.
Export content to social platforms with correct formats.
StoreKit 2 subscription lifecycle management.
Referral and invite system with viral growth mechanics.
Watermark overlays for images with paywall integration.
Daily/weekly streak tracking with engagement mechanics.
Achievement celebrations with confetti and badges.
What's New screen shown after app updates.
Lapsed user detection and re-engagement.
User-facing usage statistics and activity summaries.
Variable reward system with gamification mechanics.
GDPR/CCPA/DPDP privacy consent management.
Apple-compliant account deletion flow.
Pre-permission priming screens for higher grant rates.
Minimum version enforcement with update prompts.
State preservation and restoration across app launches.
Developer debug menu (DEBUG builds only).
Offline operation queue with automatic retry.
In-app feedback collection with smart routing.
In-app announcement banners with remote configuration.
Guided first-action flows for fast time-to-value.
Core Spotlight indexing for system search integration.
App Clip target with invocation and upgrade flow.
Automated App Store screenshot generation.
Background task infrastructure with BGTaskScheduler.
App extension targets for Share, Action, Keyboard, and Safari.
Data export/import infrastructure with JSON, CSV, and PDF support.
After generation, always provide:
Assess Kubernetes workloads and cluster configuration for AKS Automatic compatibility. Identifies incompatibilities, generates fixes, and guides migration from AKS Standard to AKS Automatic. WHEN: migrate to AKS Automatic, check AKS Automatic readiness, validate manifests for Automatic, assess cluster for Automatic compatibility, fix deployment for Automatic compatibility, identify AKS Automatic migration blockers, is my cluster ready for AKS Automatic.
Discovers available Azure OpenAI model capacity across regions and projects. Analyzes quota limits, compares availability, and recommends optimal deployment locations based on capacity requirements. USE FOR: find capacity, check quota, where can I deploy, capacity discovery, best region for capacity, multi-project capacity search, quota analysis, model availability, region comparison, check TPM availability. DO NOT USE FOR: actual deployment (hand off to preset or customize after discovery), quota increase requests (direct user to Azure Portal), listing existing deployments.
Interactive guided deployment flow for Azure OpenAI models with full customization control. Step-by-step selection of model version, SKU (GlobalStandard/Standard/ProvisionedManaged), capacity, RAI policy (content filter), and advanced options (dynamic quota, priority processing, spillover). USE FOR: custom deployment, customize model deployment, choose version, select SKU, set capacity, configure content filter, RAI policy, deployment options, detailed deployment, advanced deployment, PTU deployment, provisioned throughput. DO NOT USE FOR: quick deployment to optimal region (use preset).
Unified Azure OpenAI model deployment skill with intelligent intent-based routing. Handles quick preset deployments, fully customized deployments (version/SKU/capacity/RAI policy), and capacity discovery across regions and projects. USE FOR: deploy model, deploy gpt, create deployment, model deployment, deploy openai model, set up model, provision model, find capacity, check model availability, where can I deploy, best region for model, capacity analysis. DO NOT USE FOR: listing existing deployments (use foundry_models_deployments_list MCP tool), deleting deployments, agent creation (use agent/create), project creation (use project/create).
Intelligently deploys Azure OpenAI models to optimal regions by analyzing capacity across all available regions. Automatically checks current region first and shows alternatives if needed. USE FOR: quick deployment, optimal region, best region, automatic region selection, fast setup, multi-region capacity check, high availability deployment, deploy to best location. DO NOT USE FOR: custom SKU selection (use customize), specific version selection (use customize), custom capacity configuration (use customize), PTU deployments (use customize).
This skill should be used when working with LaminDB, an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR. Use when managing biological datasets (scRNA-seq, spatial, flow cytometry, etc.), tracking computational workflows, curating and validating data with biological ontologies, building data lakehouses, or ensuring data lineage and reproducibility in biological research. Covers data management, annotation, ontologies (genes, cell types, diseases, tissues), schema validation, integrations with workflow managers (Nextflow, Snakemake) and MLOps platforms (W&B, MLflow), and deployment strategies.
Latch platform for bioinformatics workflows. Build pipelines with Latch SDK, @workflow/@task decorators, deploy serverless workflows, LatchFile/LatchDir, Nextflow/Snakemake integration.
Run Python code in the cloud with serverless containers, GPUs, and autoscaling. Use when deploying ML models, running batch processing jobs, scheduling compute-intensive tasks, or serving APIs that require GPU acceleration or dynamic scaling.
Take rshankras/generators from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.