Skill UI

A comprehensive Rust client library for integrating with Azure Service Bus. It allows developers to send and receive messages reliably using both point-to-point queues (for competing consumers) and publish-subscribe topics. This skill is essential for building robust, event-driven, and highly available distributed applications in Rust that require durable message passing within the Azure cloud environment.

This official Rust client library provides comprehensive functionality for interacting with Azure Queue Storage. It allows developers to reliably send, receive, manage, and peek messages within an Azure cloud environment. Use it in Rust applications that require asynchronous message queuing, ensuring robust communication and data synchronization between services. Supports full lifecycle management including deletion and peeking.

This comprehensive tool performs an architectural audit of a codebase, mapping module dependencies, checking layering integrity, and flagging structural decay. Drawing on classic engineering principles (like Conway's Law and Testability Seams), it provides a deep assessment of the project's health and structure. Ideal for large-scale code reviews or onboarding new developers.

This skill provides a comprehensive tech debt assessment, identifying, classifying, and prioritizing core maintainability issues. By drawing on classic engineering principles, it helps teams build a structured refactoring roadmap, optimizing code health and development velocity.

This skill acts as a comprehensive maintenance orchestrator for the brooks-lint plugin repository. It executes a rigorous, sequential subagent pipeline—including authoring, evaluation, QA, trigger auditing, and release management. Use this when adding, editing, or updating skills to ensure all related components (manifests, README, CHANGELOG) are synchronized and adhere to strict quality gates throughout the development lifecycle.

This skill provides comprehensive pull request (PR) code reviews that go beyond simple syntax checks. It aggressively surfaces deep-rooted decay risks, architectural design smells, and maintainability issues. Findings are reported using a structured Symptom → Source → Consequence → Remedy framework, drawing insights from classic engineering principles. Ideal for reviewing complex or crucial code modules.

This advanced skill performs a full-sweep analysis, providing a unified assessment of code quality across multiple dimensions: code decay, architecture integrity, technical debt, and test coverage. It automatically applies safe fixes directly to the codebase, while requiring confirmation for risky changes. It is ideal for comprehensive pre-release code health audits and automated refactoring.

This skill provides a comprehensive test quality review, drawing on established principles from classic engineering texts like xUnit Test Patterns and How Google Tests Software. It diagnoses structural flaws—such as brittleness, decay, and poor coverage—in existing test suites, helping ensure that the codebase and its tests are robust, maintainable, and adhere to high industry standards.

A systematic, non-mutating investigation designed for complex software failures, including bugs, regressions, crashes, and flaky behavior. This skill launches four specialized, read-only sub-agents in parallel to perform deep root-cause analysis, trace code paths, pinpoint the smallest failing seam, and predict the quickest path to confirming or fixing the issue. It is designed purely for diagnosis and hypothesis generation.

This skill provides a comprehensive set of core UI components (e.g., Box, Button, Input, Card) and implements a structured design system. It guides developers on using design tokens (like `$textPrimary` or `$4`) instead of hard-coded values, ensuring visual consistency and scalability across applications. Ideal for any project requiring standardized, maintainable UI development.

This skill provides comprehensive best practices for designing modern and robust React user interfaces. It covers critical patterns for managing asynchronous data, including optimal loading state indicators (skeletons vs. spinners), structured error handling hierarchies, and designing contextual empty states. Essential for improving user experience during data fetching and operations.

A comprehensive, four-phase methodology for debugging complex software issues. It guides users through root cause investigation, pattern analysis, hypothesis testing, and controlled implementation. The core principle emphasizes finding the underlying cause rather than applying quick, symptomatic fixes, ensuring robust and reliable code improvements across various failure scenarios.