Skill UI

A comprehensive tool that analyzes existing codebases to generate detailed, definitive architectural blueprints. It automatically detects technology stacks, identifies architectural patterns (e.g., Microservices, Clean Architecture), and documents implementation details, including dependency flow, cross-cutting concerns (security, logging), and data models. This ensures architectural consistency, aids in onboarding new team members, and guides future development efforts.

Provides comprehensive patterns and techniques for securing AI agent systems. Learn how to enforce governance policies, including tool allow/block lists, content filtering, rate limiting, and human approval workflows. Essential for building robust, compliant, and trustworthy agents in multi-agent or production environments that interact with external APIs and sensitive data.

Catalogs 42 cloud-agnostic design patterns that architects can apply when designing, reviewing, or operating distributed systems to boost reliability, performance, messaging, security, and deployment outcomes.

Generates customizable prompts to scan a codebase, identify high-quality exemplars, and document patterns across languages (.NET, Java, JavaScript, TypeScript, React, Angular, Python) with configurable depth, categorization, and documentation formats.

Converts text-based documentation into structured Markdown by following explicit instructions, documented options, or reference files, supporting header levels, patterns, platforms, and cleanup steps for polished technical documents.

Generates a copilot-instructions blueprint tailored to each repository by analyzing existing code patterns, versions, and architectural standards so GitHub Copilot stays aligned with project guidelines.

Guides an AI pair to document application needs, access patterns, and scaling concerns while producing Azure Cosmos DB NoSQL requirements and data model artifacts using established best practices in a structured workflow.

This tool acts as an expert technical documentation specialist, transforming lengthy, verbose official documentation from URLs into concise and actionable 'tldr' pages. It extracts the most common command patterns, adheres strictly to the tldr-pages markdown format, and provides clear, example-driven command references, making complex tools easy to reference.

Guides writing modern MSTest 3.x/4.x unit tests by outlining project setup, lifecycle hooks, and assertion APIs so .NET teams follow up-to-date patterns.

Provides guidance on organizing and writing focused XUnit unit tests, covering project setup, fact/theory patterns, assertions, mocking, and data-driven testing for reliable .NET test suites.

Produces production-ready Dataverse SDK code for Python, showcasing advanced error handling and retries, batch operations, OData query tuning, metadata lifecycle management, configuration/timeouts, cache invalidation, chunked file uploads, and Pandas integrations with references.

Guides architects through Dataverse SDK use cases by analyzing requirements, designing data models, selecting integration patterns, and producing production-ready Python code with best practices.