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Found 2,417 Skills
VectorBT backtesting expert. Use when user asks to backtest strategies, create entry/exit signals, analyze portfolio performance, optimize parameters, fetch historical data, use VectorBT/vectorbt, compare strategies, position sizing, equity curves, drawdown charts, or trade analysis. Also triggers for openalgo.ta helpers (exrem, crossover, crossunder, flip, donchian, supertrend).
Deep Core Web Vitals and page speed audit. Use when the user asks about page speed, Core Web Vitals, LCP, CLS, INP, FCP, TTFB, Lighthouse scores, why a page is slow, performance optimization, or resource size analysis. For broader technical SEO issues, see diagnose-seo.
Analyze raw prompts, identify intent and gaps, match ECC components (skills/commands/agents/hooks), and output a ready-to-paste optimized prompt. Advisory role only — never executes the task itself. TRIGGER when: user says "optimize prompt", "improve my prompt", "how to write a prompt for", "help me prompt", "rewrite this prompt", or explicitly asks to enhance prompt quality. Also triggers on Chinese equivalents: "优化prompt", "改进prompt", "怎么写prompt", "帮我优化这个指令". DO NOT TRIGGER when: user wants the task executed directly, or says "just do it" / "直接做". DO NOT TRIGGER when user says "优化代码", "优化性能", "optimize performance", "optimize this code" — those are refactoring/performance tasks, not prompt optimization.
Fine-tune Gemma 4 and 3n models with audio, images, and text on Apple Silicon using PyTorch and Metal Performance Shaders.
Audit and improve SwiftUI runtime performance from code review and architecture. Use for requests to diagnose slow rendering, janky scrolling, high CPU/memory usage, excessive view updates, or layout thrash in SwiftUI apps, and to provide guidance for user-run Instruments profiling when code review alone is insufficient.
Comprehensive React 19 patterns expert covering Server Components, Actions, use() hook, useOptimistic, useFormStatus, useFormState, React Compiler, concurrent features, Suspense, and modern TypeScript development. Proactively use for any React development, component architecture, state management, performance optimization, or when implementing React 19's latest features.
Use when optimizing SQL queries, designing database schemas, or tuning database performance. Invoke for complex queries, window functions, CTEs, indexing strategies, query plan analysis.
React Three Fiber lighting - light types, shadows, Environment component, IBL. Use when adding lights, configuring shadows, setting up environment lighting, or optimizing lighting performance.
Mobile app testing strategy and execution for iOS and Android (native + cross-platform): choose automation frameworks, define device matrix, control flakes, validate performance/reliability/accessibility, and set CI + release gates. Use when you need a mobile QA plan, device lab/CI setup, or guidance on XCUITest/Espresso/Appium/Detox/Maestro/Flutter testing.
This skill should be used when the user asks to "create a 3D scene", "add a mesh", "implement OrbitControls", "load a GLTF model", "add bloom post-processing", "write a custom shader", "create particle effects", "optimize Three.js performance", "use WebGPU", "add shadows", "animate a model", or mentions Three.js, threejs, WebGL, WebGPU, GLTF, raycaster, shader material, PBR material, or post-processing effects. IMPORTANT: This skill is for VANILLA Three.js (imperative JavaScript). For React Three Fiber (@react-three/fiber, R3F, drei), check the `r3f-best-practices` skill, although three-js skills helps when working with R3F since R3F is a React renderer for Three.js. Provides complete Three.js reference for 3D web graphics including scene setup, geometry, materials, textures, lighting, cameras, loaders, animation, controls, interaction, shaders, post-processing, performance optimization, TSL/node materials, WebGPU, physics, and VR/XR integration.
React performance optimization guidelines. Use when writing, reviewing, or refactoring React components to ensure optimal rendering and bundle patterns. Triggers on tasks involving React components, hooks, memoization, or bundle optimization.
Build autonomous game-playing agents using AI and reinforcement learning. Covers game environments, agent decision-making, strategy development, and performance optimization. Use when creating game-playing bots, testing game AI, strategic decision-making systems, or game theory applications.