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This SDK provides a comprehensive solution for centralized application configuration management in Azure. It allows developers to store, manage, and dynamically refresh application settings, including implementing feature flags and integrating secrets from Azure Key Vault. By decoupling configuration from code, teams can modify application behavior and deploy features without requiring service redeployment.

This SDK provides a comprehensive solution for centralized application configuration management in Azure. It allows developers to store, manage, and dynamically refresh application settings, including implementing feature flags and integrating secrets from Azure Key Vault. By decoupling configuration from code, teams can modify application behavior and deploy features without requiring service redeployment.

This robust SDK is the official client library for .NET applications to interact with Azure Key Vault and Managed HSM. It provides comprehensive functionality for managing the full lifecycle of cryptographic keys, including creation (RSA, EC, Symmetric), version control, property updates, secure deletion, and backup/restore. It supports core cryptographic operations such as encryption, decryption, digital signing, verification, and key wrapping.

This SDK provides comprehensive Java support for managing secrets within Azure Key Vault. It enables developers to securely store, retrieve, update, list, delete, and recover sensitive configuration data, such as API keys, database passwords, and connection strings. It is essential for building secure, production-ready applications that comply with best security practices.

This skill provides comprehensive guidance on deploying Expo applications to production across multiple platforms, including iOS, Android, and Web, utilizing Expo Application Services (EAS). It covers the full lifecycle, from building and configuring profiles (eas.json) to submitting builds to App Store Connect, Google Play, and TestFlight. Furthermore, it details deploying web bundles, managing app versions, optimizing App Store metadata, and implementing automated CI/CD workflows.

This skill guides the end-to-end development, deployment, and secure integration of complex AI features into Firebase applications. It covers setting up Cloud Functions, Firestore/Storage rules, Authentication, and connecting robustly with Vertex AI/Gemini for advanced AI capabilities. Focuses on building production-grade, highly secure, data-intensive AI systems.

Manages Firebase/Firestore CRUD, queries, batch jobs, and security for applications while extending to A2A agent messaging, MCP server integration, and Cloud Run coordination for advanced workflows.

Map web inputs for reflected or stored HTML injection, send crafted tags via URLs, POST bodies, or visible fields, and document appearance/phishing manipulations for authorized security assessments.

A comprehensive guide for building professional, high-performance native iOS applications. Covers modern frameworks like SwiftUI and Swift 6, alongside deep expertise in UIKit, Core Data, networking (REST/GraphQL), performance profiling, and full App Store lifecycle management. Ideal for developers needing guidance on modern Apple ecosystem best practices.

This module implements comprehensive Role-Based Access Control (RBAC) for multi-tenant LangChain applications. It provides granular permission models for controlling model access, tool execution, and chain invocation. Key features include multi-tenant data isolation in vector stores, enforcing usage quotas (token limits), and integrating middleware for robust security enforcement. Ideal for building secure, scalable, and compliant enterprise LLM systems.

A comprehensive guide to implementing robust, multi-layered API response caching using distributed stores like Redis and Memcached, alongside CDN integration. Learn advanced strategies such as tag-based cache invalidation, read-through/write-through patterns, TTL management, and serving stale-while-revalidate content to drastically boost application performance and scalability.

Memory provides persistent context across multiple coding sessions. It stores crucial project decisions, user preferences (like preferred package managers or specific libraries), and architectural conventions in a local memory file. This ensures that the AI maintains consistency, recalls past choices automatically, and applies project-specific rules without needing constant reminders, significantly improving workflow continuity and reliability.