Skill UI

A comprehensive tool that analyzes complex codebases to generate detailed, structured architectural blueprints. It automatically detects and documents the entire technology stack, including primary languages (.NET, Java, Python, etc.), dependencies, versions, licensing, and implementation patterns. The output provides actionable documentation covering coding conventions, usage examples (API, data access), and visual architecture maps, ensuring consistency across guided software development projects.

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 the setup of isolated development environments using Git worktrees and best practices. It ensures that all development work is kept separate from the main repository branch, preventing accidental commits or state corruption. The process involves checking the current worktree status, creating new isolated environments (prioritizing native tools), installing project dependencies (e.g., Node, Python, Go), and running essential baseline tests to guarantee a clean, stable starting state.

WizTelemetry Logging is a core extension component within the KubeSphere Observability Platform, designed for comprehensive log collection, processing, and storage. It supports advanced log collection architectures, including collecting container stdout/stderr logs (via vector-logging) and collecting logs directly from the container filesystem (via logsidecar-injector). Use this guide to understand its configuration, dependencies, and how to query logs using the dedicated API.

A comprehensive checklist and script guide for validating all dependencies before deploying Apple Notes automation on a production macOS machine. It covers TCC permissions, iCloud sync health, application configuration, error handling robustess, and cross-OS compatibility checks, ensuring zero missed steps when automating notes management.

A powerful pre-action hook designed to enforce rigorous checks before AI agents modify code, create files, or execute destructive commands. Instead of relying on superficial self-evaluation, GateGuard forces the LLM to gather concrete facts (like dependencies, data schemas, and impacted modules), significantly improving code reliability and architectural depth. It provides multiple gates for edits, writes, and destructive bash commands.

This skill acts as a rigorous, Socratic interviewer designed to stress-test a proposed plan against an established domain model and existing project documentation. It forces the user to sharpen terminology, resolve cross-cutting dependencies, and identify inconsistencies between stated requirements, code implementations, and documented decisions. It ensures the resulting architecture is precise, consistent, and well-documented by automatically updating the context and generating Architecture Decision Records (ADRs).

This skill is designed to comprehensively edit and improve existing articles. It first analyzes the content, dividing it into logical sections based on headings and considering the dependencies between information points. Subsequently, it rewrites each section to enhance clarity, coherence, and flow, ensuring the prose is tighter and more professional. Use this when you need to revise a draft, academic paper, or long-form content.

This skill acts as an intensive sparring partner, rigorously questioning any provided plan, design, or concept. It guides the user through every decision branch, resolving dependencies one by one until a comprehensive and shared understanding is achieved. Ideal for stress-testing ideas, identifying blind spots, and ensuring the robustness of complex projects before implementation.

This comprehensive tool analyzes a detailed specification by synthesizing data from multiple source files (JSON, Markdown, etc.). It tracks phase completion, identifies task progress, maps boundary contexts (upstream/downstream dependencies), and proactively highlights necessary revalidation points or blockers, providing a single, unified report of the spec's completeness and current development state.

This assistant guides users through the structured process of creating comprehensive product briefs, PRDs, and feature specifications. It ensures that all critical elements—including problem statements, user value, success metrics, scope boundaries (in/out), and dependencies—are clearly defined before development begins, enabling effective product scoping and communication.

This guide provides detailed instructions for migrating code from the legacy `google-search-results` package to the modern `serpapi` client library. It covers major API changes, including structural updates in Python and Node.js, transitioning from callback-based or class-based calls to modern, promise/async-await structures. Use this when upgrading dependencies or updating search logic.