Skill UI

A comprehensive guide covering advanced optimization techniques for Golang, including allocation reduction, CPU cycle efficiency, memory layout tuning, Garbage Collection (GC) management, pooling, and caching strategies. This skill provides a systematic methodology (Measure -> Hypothesize -> Improve -> Benchmark) for resolving bottlenecks identified through profiling tools like pprof or fgprof, ensuring optimizations are targeted and validated.

Provides a measurement-driven workflow for improving web application performance, covering when to optimize, how to profile front-end and back-end bottlenecks, and how to monitor Core Web Vitals before and after fixes.

A comprehensive workflow for the full lifecycle management of AI skills. This tool guides users through creating skills from scratch, modifying existing ones, and rigorously benchmarking performance. It supports iterative refinement, quantitative evaluation, and optimizing skill descriptions for maximum triggering accuracy.

This comprehensive guide provides best practices for developing robust and efficient data access layers using Entity Framework Core. It covers critical areas including DbContext design, performance optimization (e.g., AsNoTracking, Select projection), secure querying techniques, migration strategies, and testing methodologies, ensuring the creation of scalable and maintainable applications.

Analyzes IaC files and live Azure resources to validate SKUs, usage, and costs, then generates GitHub issues (one per recommendation plus an EPIC) so teams can track implementation of prioritized savings.

Provides an end-to-end kit for building Microsoft 365 Copilot declarative agents across basic, enterprise, and validation workflows, with TypeSpec definitions, JSON schema validation, and Agents Toolkit integration for planning, testing, deployment, and optimization.

Explains Fabric Lakehouse storage, data organization, security, shortcuts, and optimization practices so you can design, build, and monitor Lakehouse solutions leveraging Microsoft Fabric best practices.

Assistant for PostgreSQL developers that highlights native features, advanced data types, performance tuning patterns, and extension usage to optimize queries, indexes, and analytics in PostgreSQL projects.

A universal assistant for conducting deep code reviews across major SQL databases (MySQL, PostgreSQL, SQL Server, Oracle). It rigorously analyzes SQL code for critical vulnerabilities (like SQL injection), performance bottlenecks, maintainability issues, and adherence to industry best practices. Features include security audits, index optimization recommendations, query structure analysis, and schema design validation, ensuring robust and secure database development.

Cross-database SQL performance optimization assistant offering query tuning, indexing strategies, pagination, batch operations, and monitoring guidance for MySQL, PostgreSQL, SQL Server, Oracle, and other relational systems.

This skill acts as an expert GPU optimization engineer, transforming slow, CPU-bound Python code (NumPy, Pandas, ML pipelines, simulations, etc.) into highly optimized, GPU-accelerated code. It utilizes specialized libraries like CuPy, Numba CUDA, and Warp to achieve dramatic speedups (10x to 1000x) for complex scientific computing, physics simulations, machine learning, geospatial analysis, and large-scale data processing on NVIDIA GPUs.

Pymoo is a comprehensive Python framework designed for solving both single-objective and multi-objective optimization problems. It implements state-of-the-art evolutionary algorithms (like NSGA-II, NSGA-III, MOEA/D) and is specialized in identifying Pareto-optimal fronts. This tool is essential for engineering design and scientific problems requiring the analysis of trade-offs among multiple conflicting objectives.