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Found 2,691 Skills
Manage Cloudflare infrastructure including DNS records, zones, SSL/TLS, caching, firewall rules, Workers, Pages, and analytics. Use when working with Cloudflare APIs, creating or modifying DNS records, managing domain security, purging cache, deploying Workers/Pages, or analyzing traffic. Created by After Dark Systems, LLC.
Design and implement beautiful, fluid Liquid Glass interfaces in Expo React Native apps. Covers four paths: (1) expo-glass-effect for UIKit-backed glass surfaces, (2) @expo/ui SwiftUI integration for native SwiftUI glass modifiers and advanced transitions, (3) Expo Router unstable native tabs for system Liquid Glass tab bars, and (4) @callstack/liquid-glass as a third-party alternative. Use when tasks mention "liquid glass", "glass effect", "frosted/translucent UI", "iOS 26 design", "native tabs", "expo-ui", "SwiftUI in Expo", or when shipping Apple-style glass with robust fallbacks, accessibility checks, HIG-aware design decisions (Foundations, Patterns, Components, Inputs), and cross-platform degradation.
Apply when writing, modifying, or reviewing code. Behavioral guidelines to reduce common LLM coding mistakes. Triggers on implementation tasks, code changes, refactoring, bug fixes, or feature development.
Generate concise, descriptive git commit messages following best practices. Use when creating git commits from staged changes, crafting commit messages, or reviewing commit message quality. Use when the user says /commit or asks to create a git commit.
Apple Core Bluetooth framework for BLE and Bluetooth Classic. Use for central/peripheral workflows, scanning, connecting, advertising, GATT services/characteristics, read/write/notify, L2CAP, background processing or state restoration, and error handling across Apple platforms.
Write correct and idiomatic Typst code for document typesetting. Use when creating or editing Typst (.typ) files, working with Typst markup, or answering questions about Typst syntax and features. Focuses on avoiding common syntax confusion (arrays vs content blocks, proper function definitions, state management).
A high-level interactive graphing library for Python. Ideal for web-based visualizations, 3D plots, and complex interactive dashboards. Built on plotly.js, it allows users to zoom, pan, and hover over data points in a browser-based environment. Use for interactive charts, web applications, Jupyter notebooks, 3D data visualization, geographic maps, financial charts, animations, time-series analysis, and building production-ready dashboards with Dash.
State-of-the-art Machine Learning for PyTorch, TensorFlow, and JAX. Provides thousands of pretrained models to perform tasks on different modalities such as text, vision, and audio. The industry standard for Large Language Models (LLMs) and foundation models in science.
The foundational library for creating static, animated, and interactive visualizations in Python. Highly customizable and the industry standard for publication-quality figures. Use for 2D plotting, scientific data visualization, heatmaps, contours, vector fields, multi-panel figures, LaTeX-formatted plots, custom visualization tools, and plotting from NumPy arrays or Pandas DataFrames.
Comprehensive guide for NumPy - the fundamental package for scientific computing in Python. Use for array operations, linear algebra, random number generation, Fourier transforms, mathematical functions, and high-performance numerical computing. Foundation for SciPy, pandas, scikit-learn, and all scientific Python.
A fast, extensible progress bar for Python and CLI. Instantly makes your loops show a smart progress meter with ETA, iterations per second, and customizable statistics. Minimal overhead. Use for monitoring long-running loops, simulations, data processing, ML training, file downloads, I/O operations, command-line tools, pandas operations, parallel tasks, and nested progress bars.
A Just-In-Time (JIT) compiler for Python that translates a subset of Python and NumPy code into fast machine code. Developed by Anaconda, Inc. Highly effective for accelerating loops, custom mathematical functions, and complex numerical algorithms. Use for @njit, @vectorize, prange, cuda.jit, numba.typed, JIT compilation, parallel loops, GPU acceleration with CUDA, Monte Carlo simulations, numerical algorithms, and high-performance Python computing.