Skill UI

Captures coding session learnings by extracting corrections, successes, and anti-patterns with confidence scoring so teams can track, reinforce, and reuse technical knowledge across projects.

Summarizes the Test-Driven Development workflow with RED/GREEN/REFACTOR phases, the AAA pattern, prioritization cues, anti-patterns, and AI-augmented agent roles to guide implementation.

A comprehensive collection of agent skills for rigorously auditing digital interfaces and user flows. It evaluates designs against 168 research-backed UX/UI principles, detects common design antipatterns, and provides specialized review for AI-powered interfaces, ensuring adherence to modern best practices.

Showcases Django project layout, ORM/query tuning, DRF serializer validation, signal usage, middleware timing and anti-patterns to keep apps maintainable and performant.

This comprehensive guide outlines best practices for writing high-quality, scalable, and maintainable code in .NET/C#. It covers core architectural principles, including SOLID, dependency injection, and design patterns (e.g., Command Handler). Furthermore, it addresses modern challenges such as structured logging, async/await patterns, secure coding, robust testing standards, and advanced AI integration using Semantic Kernel.

Provides a comprehensive reference for migrating deprecated and unsafe class component lifecycle methods (such as componentWillMount, componentWillReceiveProps, and componentWillUpdate) to modern, React 18 compliant patterns. This guide is essential when updating legacy codebases, helping developers correctly identify the semantic purpose of the method and choose the correct replacement hook (e.g., componentDidMount or getDerivedStateFromProps), thus preventing common migration errors.

This skill provides comprehensive guidance for building and refining applications, plugins, and function-calling flows using the Semantic Kernel framework. It supports multiple languages, including .NET and Python. Guidance emphasizes consulting the latest official documentation, adhering to best practices (like async patterns and strong typing), and correctly handling multi-ecosystem development.

A comprehensive guide for creating, updating, refactoring, and reviewing solutions built on the Microsoft Agent Framework. This skill provides unified best practices for both .NET and Python environments, serving as a modern successor to Semantic Kernel and AutoGen. It emphasizes adherence to the latest official documentation, async patterns, robust error handling, and structured agent/workflow orchestration for complex AI applications.

Analyzes PostgreSQL snippets or entire projects to flag anti-patterns, enforce best practices, optimize JSONB/array usage, schema design, custom types, triggers, extensions, and security policies like RLS.

Guides Python authors from commit to release with end-to-end steps for structuring src layouts, choosing setuptools/hatchling/flit/poetry backends, enforcing SOLID+type-hinting patterns, orchestrating lint/test/CI, managing PEP 440 semantics, and publishing trusted releases to PyPI.

This skill systematically reviews test code to identify and correct common anti-patterns. It detects issues such as flaky tests, overly mocked dependencies (over-mocking), brittle assertions, excessive test size (The Giant), and structural dependencies. It explains the flaw, provides best practices, and suggests refactored, reliable implementations, helping maintain high test quality and stability.

This skill guides users on how to search for physical monuments, memorials, or landmarks by linking them to their originating historical events. The core technique involves treating the historical event (e.g., a battle, a protest, a significant date) as the primary search anchor, and then locating the associated memorial or structure. It provides structured query templates and anti-patterns to ensure accurate, context-aware geographical and historical information retrieval.