Skill UI

Performs comprehensive historical backtesting for trading strategies using the high-performance neural-trader engine (Rust/NAPI). This skill supports walk-forward validation and captures key performance metrics. Critically, it Ed25519-signs the final artifact to provide cryptographic tamper evidence, ensuring data integrity for promotion from paper to live trading environments.

Dispatches computationally intensive quantitative financial jobs (such as multi-year walk-forward backtesting, large Monte-Carlo simulations, parameter sweeps, and deep model training using neural-trader) to a managed cloud agent. This is necessary for workloads that exceed local computational limits, providing robust, scalable infrastructure for professional quantitative research.

This skill provides regulator-grade feature attribution for complex trading signals (LSTM/Transformer). By constructing a feature-contribution graph and running single-entry PageRank, it identifies the top-K features that most drove a prediction. This is crucial for compliance with regulations like the EU AI Act and SEC Reg-AI, providing a robust, auditable audit trail for algorithmic trading systems.

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 skill solves the mean-variance portfolio optimization problem (Σ · x = μ) using the highly efficient Conjugate Gradient (CG) method. It significantly outperforms legacy Neumann series methods, providing state-of-the-art speedup (40-60x) and accuracy for calculating optimal investment weights. It is designed for quantitative finance and risk management applications where computational performance is critical.

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 generates sophisticated trading signals by employing the neural-trader anomaly detection engine. It utilizes Z-score scoring to classify market movements into distinct patterns, such as spikes (breakout), drifts (trend forming), and oscillations (range-bound). The process integrates neural prediction and historical pattern searching to provide highly ranked, actionable signals, making it invaluable for algorithmic and quantitative trading strategies.

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.

This powerful tool utilizes Playwright to record high-quality, professional web application demo videos. It simulates natural user interactions, injecting a visible cursor overlay and adhering to specific pacing guidelines. It is ideal for creating comprehensive tutorials, feature walk-throughs, or documentation for stakeholders.

This tool automatically converts design screenshots or visual exports into structured, functional Vue 3 Composition API components. It supports popular UI libraries like Vant, Element Plus, and Ant Design Vue. By batch converting screenshots, it streamlines the development process from raw design assets to reusable, production-ready code, significantly boosting frontend development efficiency.

A comprehensive reference for managing Uncloud, a decentralized self-hosting platform. This guide details using the `uc` CLI to deploy services, configure Caddy ingress, manage ports (HTTP/HTTPS/TCP/UDP), scale workloads, and inspect logs across a WireGuard mesh cluster. Ideal for setting up resilient, self-owned microservices infrastructure.

Design and implement a unified operational workflow for managing notifications sourced from disparate tools like GitHub, Linear, local hooks, and chat platforms. This skill addresses notification fragmentation, alert noise, and redundancy by establishing clear severity levels, ownership, routing channels, and next operational actions into a single, cohesive pipeline.