Skill UI

This skill performs deep, line-by-line scanning of an entire codebase to understand its architecture, patterns, and dependencies. It systematically identifies critical issues across security vulnerabilities (SQL injection, secrets), performance bottlenecks (N+1 queries, caching), architectural flaws, and code quality gaps. It then automatically refactors and fixes all identified issues, transforming the code into professional, production-grade, enterprise-level quality.

Profiles application performance by analyzing CPU usage, memory allocation, and execution time with the application-profiler plugin, then identifies bottlenecks and suggests code-level optimizations for faster execution.

This skill specializes in designing, optimizing, and evaluating prompts for Large Language Models (LLMs). It allows users to generate optimized prompt templates, structured output schemas (e.g., JSON mode, function calling), and comprehensive test suites. Use this skill when building new LLM applications, implementing complex reasoning patterns like Chain-of-Thought, defining system guardrails, or building robust evaluation frameworks to measure and improve model performance.

Provides prioritized performance optimization recommendations across frontend, backend, and infrastructure, offering bottleneck analysis, impact estimates, and phased implementation roadmaps when users request speed improvements or reviews.

A comprehensive guide for profiling, analyzing, and optimizing Python code. It addresses identifying performance bottlenecks related to CPU usage, excessive memory consumption, I/O operations, and slow data processing. Use this skill to improve application speed and efficiency in production environments.

This skill simulates the role of a quantitative analyst, providing comprehensive tools for building complex financial models, backtesting trading strategies, and analyzing market data. It covers advanced topics like portfolio optimization (Markowitz, Black-Litterman), calculating risk metrics (VaR, Sharpe ratio), and performing statistical arbitrage. Ideal for quantitative finance professionals needing rigorous data analysis, robust backtesting, and risk-adjusted performance evaluation.

A comprehensive optimization guide maintained by Vercel, covering 45 rules for React and Next.js. Use this resource when building new components, implementing complex data fetching (client or server-side), refactoring code, or systematically improving bundle size and overall load times for high-performance web applications.

Automated assistance for building React components, offering best practices, production-ready code generation, and validation guidance for frontend patterns like accessibility and performance optimization.

A comprehensive architectural guide for building production-grade flowcharts using ReactFlow. This pattern set covers key challenges including hierarchical tree navigation (expandable/collapsible nodes), performance optimization via incremental rendering and memoization, and advanced state management using Redux-like reducer patterns with history tracking (undo/redo).

A comprehensive toolset for the full backend development lifecycle. This suite enables developers to scaffold API routes from OpenAPI specifications, manage complex database migrations and optimization (PostgreSQL), and perform rigorous load/performance testing. It supports building robust microservices, securing REST APIs, and ensuring system scalability.

This skill covers the full lifecycle of deploying machine learning models into production environments. It focuses on establishing robust MLOps pipelines, including model deployment workflows (e.g., Docker, Canary releases), feature store management (Feast), and comprehensive drift monitoring. Furthermore, it details advanced integration patterns for Large Language Models (LLMs) and building Retrieval-Augmented Generation (RAG) systems, emphasizing operational stability, performance validation, and cost optimization.

A comprehensive guide on optimizing Sentry observability costs. Learn techniques such as SDK-level error sampling, fine-grained performance tracing control, and event filtering using the `beforeSend` hook. These methods significantly reduce the volume of error and transaction data sent, allowing users to manage quotas and prevent overage charges without losing visibility into mission-critical production errors.