Skill UI

Guides developers through strict test-driven development: write failing tests first, iterate red-green-refactor, and keep tests passing before refactoring to ensure reliable features.

This skill provides a systematic, multi-step approach to identify and resolve all failing unit tests within a codebase. It guides the user through smart error grouping (e.g., grouping by ImportError or AttributeError), prioritizing fixes from infrastructure to business logic, and ensuring thorough verification to prevent regressions. Ideal for use after major refactoring, debugging CI/CD failures, or when explicitly tasked with making the test suite pass.

A powerful agentic workflow for complex software development. Cascade Write mode acts as an autonomous coding agent, allowing users to build features, refactor code across multiple files, run terminal commands, and debug errors—all from a single natural language prompt. Ideal for full-stack development, complex system building, and large-scale code refactoring.

This skill enables complex, coordinated multi-file editing operations within the Windsurf IDE using Cascade AI. It ensures atomic changes, maintaining consistency when performing cross-file refactoring, renaming symbols, or modifying code across project boundaries. Use this when needing to make large, interdependent changes across multiple source files.

This skill manages complex, large-scale refactoring operations that span hundreds or thousands of files. It provides phased execution with checkpoints, comprehensive rollback capabilities, and AI-assisted planning. Use it for codebase migrations, major architecture refactoring, or system-wide updates.

Comprehensive guide and expert assistance for building, refactoring, and validating AWS cloud infrastructure using the AWS Cloud Development Kit (CDK). Covers best practices for TypeScript and Python, including resource naming conventions, Lambda function development, and multi-layer pre-deployment validation using tools like cdk-nag.

An expert agent focused on enhancing code clarity, consistency, and maintainability. It simplifies complex code structures, eliminates redundant logic, and applies project-specific best practices, all while guaranteeing that the original functionality and behavior remain absolutely unchanged. Ideal for code reviews, cleanup tasks, or improving readability.

This comprehensive guide provides patterns and strategies for migrating existing imperative TypeScript code to functional programming patterns using fp-ts. Learn how to replace traditional structures like try/catch blocks, null checks, and callbacks with robust, type-safe functional equivalents (e.g., Either, Option, Task). It is essential for developers aiming to increase code reliability and maintainability in large TypeScript codebases.

A set of behavioral guidelines derived from Andrej Karpathy's observations to help users writing, reviewing, or refactoring code with Large Language Models (LLMs). It guides developers to avoid overcomplication, make surgical changes, explicitly surface assumptions, and define verifiable success criteria, promoting clean, simple, and robust code development.

A powerful code review utility designed for SwiftUI projects. It comprehensively analyzes code quality, ensuring adherence to modern Swift APIs, optimal performance, robust data flow management, and compliance with Apple's Human Interface Guidelines and accessibility standards (like VoiceOver and Dynamic Type). Use this tool when writing, refactoring, or reviewing complex SwiftUI implementations to maintain high code hygiene and maintainability.

This utility converts a single, high-level objective into a comprehensive, step-by-step build plan suitable for complex, multi-agent, or multi-session engineering projects. It automatically handles dependency mapping, detects parallel steps, and structures each step with self-contained context briefs, ensuring any new agent can execute it without prior knowledge. Ideal for large-scale refactoring or feature development spanning multiple PRs.

This command executes the entire AgentHub lifecycle in a single, chained invocation. It automates complex development tasks by sequentially handling initialization, capturing performance baselines, spawning multiple specialized agents, evaluating the results (via metrics or LLM judging), and finally presenting a winner for merging. Use this when running an end-to-end competition or complex refactoring task.