By secondsky
Apply Swift 6+ best practices to iOS and macOS code while writing or reviewing, enforcing concurrency safety with async/await, actors, MainActor, Sendable protocols, typed throws, API design guidelines, and Swift 6 migration strategies.
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npx claudepluginhub secondsky/claude-skills --plugin swift-best-practicesExpert guidance on Swift Concurrency best practices, patterns, and implementation. Covers async/await, actors, tasks, Sendable conformance, data race prevention, and Swift 6 migration strategies.
Expert Swift 6.2 + SwiftUI for iOS 26, macOS 26, iPadOS 26, watchOS 26, visionOS 26, tvOS 26 with Liquid Glass, @Observable, actors, SwiftData inheritance
16 specialized Swift agents for Claude Code and VS Code Copilot. Covers concurrency, SwiftUI, Core ML, Foundation Models, MLX Swift, on-device AI, accessibility, security, testing, App Store review, Meta glasses SDK, SwiftData, visionOS, StoreKit 2, and performance.
Reviews Swift code for concurrency correctness, modern API usage, and common async/await pitfalls.
Elite iOS and macOS development expertise with automatic skill activation for Swift, SwiftUI, UIKit, Xcode, and Apple frameworks plus code formatting tools
A collection of agents and commands to aid in the planning, implementation, documentation and testing of Swift/TCA code.
This skill provides comprehensive guidance for SAP BTP Job Scheduling Service development, configuration, and operations. It should be used when creating, managing, or troubleshooting scheduled jobs on SAP Business Technology Platform. The skill covers service setup, REST API usage, schedule types and formats, OAuth 2.0 authentication, multitenancy, Cloud Foundry tasks, Kyma runtime integration, and monitoring with SAP Cloud ALM and Alert Notification Service. Keywords: SAP BTP, Job Scheduling, jobscheduler, cron, schedule, recurring jobs, one-time jobs, Cloud Foundry tasks, CF tasks, Kyma, OAuth 2.0, XSUAA, @sap/jobs-client, REST API, asynchronous jobs, action endpoint, run logs, SAP Cloud ALM, Alert Notification Service, multitenancy, tenant-aware, BC-CP-CF-JBS
Production-ready SAP BTP best practices for enterprise architecture, account management, security, and operations. Use when planning BTP implementations, setting up account hierarchies, configuring environments, implementing authentication, designing CI/CD pipelines, establishing governance, building Platform Engineering teams, implementing failover strategies, or managing application lifecycle on SAP BTP. Keywords: SAP BTP, account hierarchy, global account, directory, subaccount, Cloud Foundry, Kyma, ABAP, SAP Identity Authentication, CI/CD, governance, Platform Engineering, failover, multi-region, SAP BTP best practices
Guides development with SAP AI Core and SAP AI Launchpad for enterprise AI/ML workloads on SAP BTP. Use when: deploying generative AI models (GPT, Llama, Gemini, Mistral), building orchestration workflows with templating/filtering/grounding, implementing RAG with vector databases, managing ML training pipelines with Argo Workflows, configuring content filtering and data masking for PII protection, using the Generative AI Hub for prompt experimentation, or integrating AI capabilities into SAP applications. Covers service plans (Free/Standard/Extended), model providers (Azure OpenAI, AWS Bedrock, GCP Vertex AI, Mistral, IBM), orchestration modules, embeddings, tool calling, and structured outputs.
SAP BTP Cloud Integration Automation Service (CIAS) skill for guided integration workflows. Use when: setting up CIAS subscriptions, configuring destinations, assigning roles (CIASIntegrationAdministrator, CIASIntegrationExpert, CIASIntegrationMonitor), planning integration scenarios, working with My Inbox tasks, monitoring scenario execution, troubleshooting CIAS errors, creating OAuth2 instances, configuring identity providers for CIAS, understanding CIAS security architecture, or integrating SAP products (S/4HANA, SuccessFactors, BTP services, SAP Build, IBP).
SAP HANA Machine Learning Python Client (hana-ml) development skill. Use when: Building ML solutions with SAP HANA's in-database machine learning using Python hana-ml library for PAL/APL algorithms, DataFrame operations, AutoML, model persistence, and visualization. Keywords: hana-ml, SAP HANA, machine learning, PAL, APL, predictive analytics, HANA DataFrame, ConnectionContext, classification, regression, clustering, time series, ARIMA, gradient boosting, AutoML, SHAP, model storage