Skill UI

Guides developers through native iOS app creation using Swift 6, SwiftUI, UIKit, Combine, Core Data, networking, and App Store deployment best practices, ensuring architecture, performance, and security align with Apple ecosystem expectations.

Sets up Juicebox CI/CD integration in GitHub Actions to automate testing, branch protection, and deployment pipelines for Juicebox projects, ensuring secrets, workflows, and retries are configured for resilient builds.

Deploy Juicebox integrations straight to cloud platforms with structured configuration steps, secret management, and health checks so production-ready services can be rolled out consistently and monitored post-deployment.

Guides configuring Juicebox across development, staging, and production with isolated secrets, Kubernetes overlays, and environment-aware clients so teams can bootstrap secure, per-environment deployments and pipeline guards.

Guides the execution of the Juicebox production deployment checklist, covering readiness validation, go-live preparation, and post-launch output expectations to ensure a safe release.

Provides guidelines for implementing enterprise-grade Juicebox systems, covering architecture patterns, integrations, and deployment diagrams for recruiting and people search applications.

Guides teams through Juicebox SDK version upgrades, migration scripts, staged rollouts, and validation testing to handle breaking changes while keeping deployments stable.

Provides expert guidance on Julia 1.10+ development workflows, from multiple dispatch and performance tuning to testing, package management, and deployment. Use it when building high-performance scientific, ML, data, or web systems and you need modern tooling, checklists, and verification steps.

This skill provides step-by-step guidance for generating production-ready Kubernetes manifests. It covers essential infrastructure components including Deployments, Services, ConfigMaps, Secrets, and PersistentVolumeClaims. Use this skill when defining complex microservices infrastructure, ensuring robust network connectivity, managing configuration state, or setting up stateful workloads while adhering to Kubernetes best practices (e.g., resource limits, health checks).

Guides implementing Kling AI content-policy compliance with prompt screening, moderation checks, violation handling, and documentation to ensure generated responses stay within safety guidelines for production deployments.

A pre-launch Kling AI checklist that ensures video production deployments complete security audits, monitoring configuration, error handling, performance testing, and documentation while providing readiness reports, statuses, and action items.

Provides production-ready reference architectures for building scalable Kling AI video generation platforms, detailing microservices, event-driven pipelines, infrastructure planning, and container-ready deployment guidance.