Skill UI

A comprehensive Python SDK guide for securely managing secrets, cryptographic keys, and certificates within Azure Key Vault. This tool allows developers to implement secure storage, perform cryptographic operations (encryption, decryption, signing, verification), and handle certificate lifecycle management, ensuring sensitive application data is protected across various development environments.

A comprehensive SDK guide for managing secrets and cryptographic keys within Azure Key Vault using TypeScript. It provides methods for creating, retrieving, listing, rotating, and performing advanced cryptographic operations such as encryption, decryption, signing, and key wrapping. Ideal for secure application development and infrastructure secret management.

This SDK provides comprehensive connectivity to Azure AI Search for .NET developers. It enables building sophisticated search applications, supporting full-text search, vector search, semantic understanding, and hybrid query capabilities. Developers can manage indexes, perform document CRUD operations, and implement advanced search patterns like faceted search and autocomplete.

The official SDK for interacting with Azure AI Search service using Python. It provides comprehensive functionality for advanced search retrieval, including full-text search, vector similarity search (HNSW), hybrid search, and semantic ranking. Use this tool to build sophisticated enterprise search applications that require deep content understanding and precise information retrieval.

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.

Utilize the Azure Key Vault Keys Java SDK to perform comprehensive key lifecycle management and cryptographic operations. This guide covers creating, retrieving, updating, and deleting various key types (RSA, EC, Symmetric) in both standard Azure Key Vault and Managed HSM. It demonstrates key operations such as encryption, decryption, signing, and verifying data using Java.

Creates design philosophies and companion .pdf/.png artworks when asked for posters or static art, emphasizing spatial storytelling, minimal text, and meticulously crafted visual execution.

Applies Anthropic’s official color palette, typographic hierarchy, and accent system to any artifact that needs the company’s visual identity, ensuring consistent styling across materials with smart fallbacks for system fonts.

Automates HIPAA compliance checks across codebases, infrastructure configurations, and documentation by leveraging the hipaa-compliance-checker plugin to flag privacy, security, and access-control risks for projects handling PHI.

This skill provides comprehensive guidance on leveraging d3.js (Data-Driven Documents) to create sophisticated, interactive, and publication-quality data visualizations. It covers core concepts like data binding, implementing various chart types (network graphs, geo-maps, etc.), handling complex transitions, and ensuring responsiveness across modern JavaScript frameworks (React, Vue, Vanilla JS). Learn the standard workflow for scale definition, axis creation, and drawing custom visual elements with fine-grained control.

A sophisticated, two-stage system designed to capture and internalize user corrections and learnings during ongoing conversations. It automatically detects patterns (e.g., "use X instead of Y") and queues them. Users can manually review and apply these learnings to a persistent knowledge base (CLAUDE.md), ensuring the AI continuously improves its knowledge and adherence to specific user context for future sessions.

Chroma is an open-source, AI-native embedding database designed for building sophisticated LLM applications. It allows users to store high-dimensional embeddings and associated metadata, facilitating powerful vector search and full-text filtering. Ideal for implementing Retrieval-Augmented Generation (RAG) systems, semantic search, and document retrieval in local or self-hosted environments, making it perfect for open-source AI projects and prototyping.