Skill UI

This skill provides comprehensive guidance on idiomatic Go patterns for building production-ready, robust applications. It covers best practices like functional options, proper constructor design, resource management (timeouts, graceful shutdown), error handling, concurrency patterns, and architectural styles (DDD, Hexagonal). Use it when structuring complex Go APIs, defining service boundaries, or auditing existing code for common anti-patterns and performance pitfalls.

A comprehensive guide for structuring robust Go applications, covering best practices for new projects, from CLI tools to complex microservices and monorepos. It details directory conventions (cmd, internal, pkg), naming rules, adopting 12-Factor App principles, and choosing appropriate architectures (Clean, DDD, Hexagonal).

This comprehensive guide outlines established best practices for developing robust, scalable, and high-quality enterprise applications using Spring Boot and Java. It covers structuring projects, implementing dependency injection, designing RESTful APIs, managing transactions, securing endpoints, and utilizing advanced testing strategies (Unit, Integration). Ideal for developers aiming for industry-standard code quality.

This comprehensive guide provides a detailed framework for authoring effective and structured SKILL.md files for AI coding agents. It covers best practices for structuring content, including frontmatter fields, defining natural language triggers, establishing core principles, and incorporating advanced elements like decision trees and verification checklists, ensuring agents receive clear, actionable, and consistent technical guidance.

Guides on structuring NUnit test projects, naming fixtures, arranging tests, and applying standard plus data-driven attributes along with assertions and mocking tactics to keep suites clear, independent, and repeatable.

A comprehensive guide covering best practices for writing effective unit tests using the TUnit framework for .NET. Topics include structuring tests using the Arrange-Act-Assert (AAA) pattern, implementing data-driven tests via attributes, utilizing fluent assertion syntax, managing complex test lifecycles (Class/Assembly level hooks), and migrating concepts from xUnit to TUnit. Learn how to write robust, isolated, and advanced tests.

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 generates a detailed, multi-phase plan for complex, multi-file refactoring projects. It ensures safety by first analyzing the codebase, identifying dependencies, and structuring changes in a safe sequence (e.g., Types first, then implementations, followed by tests and cleanup). It forces the AI to output a comprehensive plan, including rollback steps and verification points, and waits for user confirmation before making any actual code changes.

Applies proven testing patterns (AAA, Given-When-Then, builders, spies, doubles) to help write maintainable unit, integration, and end-to-end suites, covering structuring, naming, TDD/BDD, and mocks across frameworks like Jest, Vitest, or pytest.

A comprehensive guide to writing, structuring, and reviewing high-quality unit and widget tests for Flutter and Dart applications. Learn best practices for ensuring test validity, distinguishing between unit and widget scope, mastering mocking, and optimizing test files for robust regression detection.

This skill provides comprehensive guidelines for contributing and managing commands within VS Code extensions. It covers crucial best practices, including naming conventions, defining visibility (e.g., Command Palette vs. Side Bar), and structuring commands for optimal user experience. Use this when adding, updating, or understanding the architectural requirements for extension commands.

This advanced skill optimizes LLM context usage by implementing strict rules for prompt caching, efficient log pruning, AST-based code inspection, and compact data structuring. It minimizes token consumption and maximizes information density, suitable for highly technical and iterative development workflows.