Results for “minikit”

Build onchain applications with React components and TypeScript utilities from Coinbase's OnchainKit, supporting wallet connection, token swaps, NFT minting, and payment processing.

Develop AI-powered applications using Genkit in Node.js/TypeScript. Use when the user asks about Genkit, AI agents, flows, or tools in JavaScript/TypeScript, or when encountering Genkit errors, validation issues, type errors, or API problems.

Build and modify Minecraft server plugins for Paper, Spigot, or Bukkit, covering plugin.yml setup, commands, listeners, schedulers, player state, team/arena systems, persistent progression, economy, configuration, and Adventure text.

Use mmx to generate text, images, video, speech, and music via the MiniMax AI platform. Use when the user wants to create media content, chat with MiniMax models, perform web search, or manage MiniMax API resources from the terminal.

Use when generating media via MiniMax API CLI scripts

Toolkit for creating animated GIFs optimized for Slack, with validators for size constraints and composable animation primitives. This skill applies when users request animated GIFs or emoji animations for Slack from descriptions like "make me a GIF for Slack of X doing Y".

Use mmx to generate text, images, video, speech, and music via the MiniMax AI platform. Use when the user wants to create media content, chat with MiniMax models, perform web search, or manage MiniMax API resources from the terminal.

JUnit 5 testing framework with Mockito integration. Covers unit tests, mocking, and Spring Boot test integration. USE WHEN: user mentions "junit", "java test", "mockito", asks about "@Test", "@Mock", "@InjectMocks", "unit test java", "spring boot test" DO NOT USE FOR: Integration tests with containers - use `testcontainers`; REST API tests - use `rest-assured`; E2E tests - use Selenium; JavaScript/TypeScript - use `vitest` or `jest`

Use mmx to generate text, images, video, speech, and music via the MiniMax AI platform. Use when the user wants to create media content, chat with MiniMax models, perform web search, or manage MiniMax API resources from the terminal.

Cross-platform Python library for quantum computing, quantum machine learning, and quantum chemistry. Enables building and training quantum circuits with automatic differentiation, seamless integration with PyTorch/JAX/TensorFlow, and device-independent execution across simulators and quantum hardware (IBM, Amazon Braket, Google, Rigetti, IonQ, etc.). Use when working with quantum circuits, variational quantum algorithms (VQE, QAOA), quantum neural networks, hybrid quantum-classical models, molecular simulations, quantum chemistry calculations, or any quantum computing tasks requiring gradient-based optimization, hardware-agnostic programming, or quantum machine learning workflows.