Skill UI

This guide provides comprehensive patterns for behavior-focused testing of React components, custom hooks, and pages. It covers best practices using React Testing Library (RTL), including accessibility assertions, simulating user interactions (userEvent), and network mocking with MSW. It also helps developers define clear test boundaries between unit component tests and full E2E flows.

A guide for security researchers focused on finding exploitable, remote-reachable vulnerabilities for bug bounties or responsible disclosure. It emphasizes high-signal flaws such as SSRF, RCE, and SQL injection, while guiding users through the professional triage and PoC creation process for penetration testing.

An advanced tool for network intelligence that calculates weighted graph scores to map the optimal path to a target. It identifies potential introducers, measures 'bridge value' across connections, and helps determine whether a connection requires a warm introduction or direct outreach on platforms like X and LinkedIn. Ideal for users needing the raw ranking methodology.

This comprehensive guide demonstrates how to achieve highly testable Swift code using protocol-based dependency injection. By abstracting external concerns—such as file systems, networking, and external APIs—into small, focused protocols, developers can write deterministic unit tests that mock side effects and error paths, ensuring robust architecture across different environments (App, Test, Preview).

This tool automates the entire process of optimizing investment portfolio allocations using a sophisticated mean-variance engine. Users can set specific risk targets, assess detailed risk metrics (variance, correlation), predict expected returns using neural networks, and generate actionable, step-by-step rebalancing plans. It is designed for quantitative traders managing complex asset baskets and historical pattern analysis.

This tool detects the current market regime (e.g., bull, bear, ranging, volatile) by leveraging advanced neural network models and technical indicators. It provides a classification, confidence score, and a recommended trading strategy, making it essential for informed decision-making, strategic planning, and enhancing the context before running complex backtests or signals.

This skill provides a comprehensive workflow for training advanced neural network models, including LSTM, Transformer, and N-BEATS, using historical market data. It facilitates model training, prediction generation with confidence intervals, performance comparison across different model types, and the structured storage of results for further analysis and SONA training.