Skill UI

A comprehensive index of 46 production-grade test automation skills, covering E2E, unit, mobile, BDD, and cloud testing across 15+ languages. This skill equips AI coding assistants to act as senior QA architects, generating structured, real-world test code for major frameworks like Playwright, Selenium, Cypress, and Jest. It supports project setup, CI/CD integration, and cross-platform debugging.

This is a detailed offensive checklist for identifying and exploiting insecure deserialization vulnerabilities across multiple programming languages (Java, Python, PHP, .NET, Node.js, etc.). It covers techniques like gadget chain generation, magic method abuse, type confusion, and modern attack vectors, providing a deep methodology for penetration testing and vulnerability research.

This module provides core use case abstractions for interacting with a standardized language catalog. It offers two key functionalities: retrieving a specific language entry using its unique code (GetLanguageByCodeUseCase), and listing all available language entries (ListLanguagesUseCase). These abstractions ensure consistent and structured data access across the application.

An expert guide covering advanced exploitation techniques for Expression Language (EL) injection across multiple Java frameworks, including Spring (SpEL), Struts2 (OGNL), and JSP/JSF (Java EL). Details detection methods, sandbox bypasses, and real-world CVE chains for Remote Code Execution (RCE).

This tool searches a comprehensive database of over 2500 curated open-source repositories for tools related to Large Language Models (LLMs), ChatGPT, RAG, agents, and NLP. It interprets natural language queries, converts non-English inputs to core English keywords, and systematically filters results across multiple specialized categories (e.g., LangChain, Chatbots, CLIs). Use it whenever the user needs to find specific AI development libraries or open-source AI tooling.

A powerful skill that enables natural-language interaction with complex DeFi protocols across multiple EVM chains. It acts as a non-custodial agent, interpreting user prompts (e.g., 'swap X for Y') into simulated, multi-step transactions (like approve + swap). It utilizes account abstraction (EIP-7702/ERC-4337) to simulate and queue secure, user-signed transactions via the CLI, making advanced DeFi accessible from the terminal.

Flox is a declarative, Nix-based environment manager designed to create perfectly reproducible development environments. It handles system-level dependencies (like compilers, databases, and native libraries) and ensures that complex toolchains across multiple languages (Python, Node.js, Go, etc.) function identically across macOS and Linux. Ideal for solving 'works on my machine' issues and onboarding new team members instantly.

This guide provides comprehensive, structured error handling patterns for modern applications using TypeScript, Python, and general architectural principles. It covers essential concepts like typed custom errors, Result patterns, API error transformers, and React Error Boundaries. Learn how to implement fail-fast mechanisms, differentiate user-facing messages from developer logs, and ensure system resilience against unexpected failures.

An expert skill designed to author comprehensive, implementation-ready design system guidelines for editorial content like books, magazines, and reports. It structures visual language across tokens, typography, spacing, and component states, while rigorously adhering to WCAG accessibility standards and defining clear content tone and anti-patterns for developers and designers.

jscpd is a comprehensive code quality tool designed to detect copy-pasted or duplicated segments across programming source code. Supporting over 150 languages, it identifies redundant code blocks and calculates the overall duplication percentage. It offers multiple reporters (AI, HTML, JSON) and integrates with refactoring workflows to help developers maintain high code integrity.

An internal, language-agnostic probe designed for investigating complex stack traces when the source language is unknown. It analyzes the provided trace to output structured, language-neutral elements, including failure contract checks, minimal reproduction guidance, and specific patch targets. This tool is vital for robust failure analysis and debugging across diverse codebases.

This skill enforces a high standard for all generated code, prioritizing correctness, clarity, and idiomatic patterns across all languages. It helps minimize code bloat by removing unnecessary abstractions and verbose patterns, while also optimizing the agent's output by using targeted diffs and eliminating excessive conversational overhead. Use this for all code generation, refactoring, or review tasks.