Skill UI

This skill executes the final two phases (Refinement and Completion) of the structured SPARC development methodology. It systematically elevates an implemented feature to production-grade quality by performing detailed code reviews (verifying compliance against specifications, architecture, and quality standards), maximizing test coverage (unit, integration, and edge cases), validating performance, and generating comprehensive documentation and traceability matrices. It ensures the feature is fully ready for deployment.

A comprehensive CI/CD quality gate checklist for Spring Boot projects. It guides developers through mandatory steps including building, static analysis (SpotBugs, PMD), running unit/integration tests, measuring code coverage, performing security scans (OWASP), and reviewing diffs before merging or deploying.

This tool initializes a structured, anti-drift multi-agent swarm environment. It is designed for coordinating complex, multi-file software development tasks, such as implementing new features, performing cross-module refactoring, or conducting security audits, requiring coordination among three or more specialized agents. It supports various topologies (hierarchical, mesh, etc.) to manage safe and parallel workstreams.

A comprehensive guide covering modern best practices for building high-performance, declarative user interfaces in SwiftUI. Topics include using the advanced @Observable framework for state management, achieving type-safe navigation with NavigationStack, effective view composition, and optimizing rendering performance for iOS and macOS platforms.

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.

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 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.

Tree Ring Memory is a local-first, framework-agnostic layer for managing durable knowledge in AI agents. It provides structured methods for recalling past decisions, recording evidence, auditing project state, and deliberately forgetting information, ensuring the core project memory remains clean and actionable without relying on raw conversation transcripts.

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.