Results for “google-tasks”

浏览器自动化任务处理技能。仅在以下情况使用：1) 其他 skill/工具（搜索、API、数据接口等）都无法满足需求，需要通过真实浏览器 GUI 兜底执行；2) 任务必须在具体网站完成登录 / 授权 / 账号内动作（点赞 / 收藏 / 评论 / 发布 / 加购）；3) 命中白名单网站（淘宝/天猫、微博、小红书）的站内检索 / 互动 / 发布需求。当用户仅需要信息检索、文本生成、代码或数据处理时，不要使用本 skill。

Manage Google Workspace event subscriptions: create, list, update, delete, reactivate, and stream events via the gws CLI.

Operates Google Workspace services—Gmail, Calendar, Drive, Contacts, Sheets, and Docs—through a command-line interface with OAuth-based authentication.

Operates Google Workspace services—Gmail, Calendar, Drive, Contacts, Sheets, and Docs—through a command-line interface with OAuth-based authentication.

Manages Google People contacts and profiles via the gws CLI, including contact groups, other contacts, and directory people.

Read and write Google Forms through the gws command-line interface, including creating forms, batch updates, retrieving forms, and managing responses and watches.

Automate repetitive tasks and workflows using scripting, file watchers, scheduled jobs, CI triggers, and API polling to eliminate manual toil. Use when the user requests task automation or provides relevant inputs for this workflow.

Gmail, Calendar, Drive, Contacts, Sheets, and Docs integration for Hermes. Uses Hermes-managed OAuth2 setup, prefers the Google Workspace CLI (`gws`) when available for broader API coverage, and falls back to the Python client libraries otherwise.

Automate Google Maps tasks including geocoding, directions, place search, and distance calculations via Composio's Rube MCP toolkit.

Automate Google Classroom tasks including course management, assignments, student rosters, and announcements via Composio's toolkit and Rube MCP.

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.