Skill UI

A comprehensive guide detailing advanced performance optimization patterns for React 18/19 and Next.js. Adapted from Vercel Engineering's best practices, this skill organizes 70+ critical rules across eight categories—including eliminating waterfalls, optimizing bundle size, improving server-side rendering, and managing client-side re-renders. Use this when developing, reviewing, or refactoring high-performance React applications.

A comprehensive guide to Redis best practices for backend development. Learn how to implement critical features such as Cache-Aside, Write-Through caching, rate limiting, distributed locks, and reliable messaging using Pub/Sub and Redis Streams. Essential for building scalable, high-performance production systems.

This skill provides deep insights into idiomatic Rust development. It covers core concepts like ownership and borrowing to prevent data races, robust error handling using `anyhow` and `thiserror`, advanced pattern matching with enums, and utilizing traits and generics for safe abstraction. Master these patterns when writing or refactoring complex, high-performance, and reliable Rust applications.

This tool manages agent memory stored in the RVF (Ruflo Vector Format). It facilitates the backup, transfer, and sharing of complex agent knowledge, including vector embeddings, metadata, and causal relationships, across different projects and platforms. Users can list, export, import, migrate, and analyze memory statistics to ensure knowledge continuity.

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 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 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 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.

This skill generates high-dimensional vector embeddings (384-dim) from text, code, or documents using the ruvector package. It utilizes the ONNX all-MiniLM-L6-v2 model, making it ideal for implementing advanced semantic search, similarity comparison, and data clustering. The resulting vectors are normalized and can be efficiently stored and indexed using HNSW, significantly enhancing retrieval accuracy in RAG and knowledge systems.

This comprehensive tool automates the process of translating non-English official documents (images) for visa applications. It handles image preprocessing (conversion and rotation), performs robust OCR using multiple frameworks, translates the extracted text into professional English while maintaining document structure, and generates a certified bilingual PDF containing both the original image and the formatted translation.