Skill UI

Scans installed skills to distill cross-domain principles into rule drafts by collecting skill and rule inventories, coordinating LLM judgments, and managing user-reviewed outputs for ongoing rule maintenance.

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.

Evaluate interfaces against 168 research-backed UX/UI principles, detect antipatterns, and inject UX context during AI-assisted design or coding sessions to surface severity-tagged findings and actionable remediation.

Analyzes ARIS usage logs to spot harness inefficiencies and recommend tweaks to SKILL.md prompts, defaults, and workflow ordering, guided by Meta-Harness principles for better outer-loop orchestration.

A comprehensive guide covering modern database design methodologies, moving beyond simple SQL patterns. It teaches best practices for schema design, relationship modeling, indexing strategies for performance tuning, ORM selection (Prisma, Drizzle, etc.), and query optimization (N+1, EXPLAIN ANALYZE). Suitable for architects and developers building robust, scalable data systems.

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.

A comprehensive code review tool designed for C#/.NET projects. It evaluates the implementation of key design patterns (Command, Factory, Repository) and adherence to SOLID principles. The review covers architecture, best practices (async/await, logging), security, testability, and clean code principles, providing actionable recommendations for improvement.

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.