semvec/pypi-packages huggingface dataset:
soda-ahk-cursor 2025.8.10.0
Cursor helper
Requires Python: >=3.10

# ahk-cursor

<a href="https://pypi.org/project/soda-ahk-cursor/">
    <img alt="PyPI - Version" src="https://img.shields.io/pypi/v/soda-ahk-cursor">
</a>

move cursor randomly

install:
```
pip install -U soda-ahk-cursor
```

Run:
```
soda-ahk-cursor
```

# Reason

This is dedicated for playing game [Eustia][1].

See also [this issue][2].

[1]: https://en.wikipedia.org/wiki/Aiyoku_no_Eustia
[2]: https://www.reddit.com/user/HeronTimeN/comments/1hnpt0k/eustia_move_cursor/

# Thanks

Python standard library:
 - multiprocessing: <https://docs.python.org/3/library/multiprocessing.html>
 - I use [Queues](https://docs.python.org/3/library/multiprocessing.html#pipes-and-queues) to pass massages between processes.

without these great libraries this project won't exist:

- <https://fastapi.tiangolo.com/>
    - used for receving server shutdown message. 
- <https://requests.readthedocs.io/en/latest/>
    - used for sending test/shutdown messages to server.
- <https://www.uvicorn.org/#fastapi>
    - used to run fastapi.
- <https://pyautogui.readthedocs.io/en/latest/index.html>
    - Move cursors.
- <https://github.com/mhammond/pywin32>
    - Create shortcut in startup folder.


This project is developed using [UV](https://docs.astral.sh/uv/) and [ruff](https://docs.astral.sh/ruff/).

Also there are many great doc/blog writers:
 - Official windows API docs: <https://learn.microsoft.com/en-us/>.
 - api taken from <https://gist.github.com/topin89/f8156a64ee79ff034cdf74c238b0dfa7>.
 - idea also from <https://docs.rs/tasklist/latest/src/tasklist/lib.rs.html#77>.
 - also see <https://hellocode.co/blog/post/tracking-active-process-windows-rust/>.


## Other

running without console & taskbar icon is done using [autohotkey](https://www.autohotkey.com/).

Icons created using [Krita](https://krita.org/en/) and [em-keyboard](https://github.com/hugovk/em-keyboard).

developped using vscode and Jedi.

Hosted on GitHub.
twrapform 0.1.2
Terraform task management wrapper for Python
Requires Python: >=3.10

# twrapform

A Python library for running Terraform commands from Python with asynchronous workflow management.

## ✨ Features

- Run Terraform commands natively from Python
- Asynchronous execution using `asyncio`
- Immutable task definitions with unique IDs
- Grouped workflow orchestration
- Granular error handling with structured results

## 📦 Requirements

- Python 3.10+
- Terraform installed and available in your system's `PATH`

## 🔧 Installation

```bash
pip install twrapform
```

## 🚀 Usage Examples

### Run a single workflow with chained Terraform tasks
```python
import asyncio

from twrapform import Workflow
from twrapform.exception import TwrapformError
from twrapform.options import InitTaskOptions, PlanTaskOptions, ApplyTaskOptions, OutputTaskOptions


async def main():
    # Create an instance of Twrapform
    twrap = Workflow(work_dir="/terraform_rootpath")

    # Add Terraform tasks one by one
    twrap = twrap.add_task(InitTaskOptions())

    # Chain multiple tasks
    twrap = (
        twrap
        .add_task(PlanTaskOptions(var={"var1": 1}))
        .add_task(ApplyTaskOptions(var={"var1": 1}))
        .add_task(OutputTaskOptions(json=True))
    )

    # Execute all tasks
    results = await twrap.execute()

    try:
        # Raise errors if any task fails
        results.raise_on_error()
    except TwrapformError as e:
        print(f"Error occurred: {e.message}")

    # Output results for successful tasks
    for success_task in results.get_success_tasks():
        print(success_task.stdout)


if __name__ == "__main__":
    asyncio.run(main())
```

### Manage multiple workflows in a group using `WorkflowManager`
```python
from twrapform import Workflow, WorkflowManager
from twrapform.options import InitTaskOptions
import asyncio

async def main():
    # Define workflows with Terraform initialization tasks
    workflow1 = Workflow(work_dir="infra/project1").add_task(InitTaskOptions())
    workflow2 = Workflow(work_dir="infra/project2").add_task(InitTaskOptions())
    
    # Add workflows into a group and initialize the manager
    manager = WorkflowManager().add_workflows(workflow1, workflow2, group_id="init-group")
    
    # Run the grouped workflows asynchronously
    result = await manager.execute()
    
    # Display summaries for successfully completed tasks
    for group_result in result.group_results:
        for wf_result in group_result.workflow_results:
            for task in wf_result.get_success_tasks():
                print(task.summary())

                
if __name__ == "__main__":
    asyncio.run(main())
```

## ⚙️ Supported Terraform Commands
twrapform currently supports the following Terraform commands:
* `terraform init`
* `terraform plan`
* `terraform apply`
* `terraform output`
* `terraform workspace select`
trade-database-manager 0.0.12
A wrapper of kdb and sql for convenient trade data management.
Requires Python: >=3.10

<table border=1 cellpadding=10>
<tr>
<td style="color: red;">

#### \*\*\* IMPORTANT NOTICE \*\*\*

<p style="color: red">This package is <b>not</b> in a usable stage. It is only uploaded for convenience of developing and testing.</p>

</td></tr></table>



### Postgreslq Initialization

```sql
CREATE DATABASE trade_data;
CREATE USER tradedbadmin WITH PASSWORD 'trade_password';
GRANT ALL PRIVILEGES ON DATABASE trade_data TO tradedbadmin;
\connect trade_data;
GRANT ALL ON SCHEMA public TO tradedbadmin;
```

Add the following lines in the `pg_hba.conf` file to allow password authentication for the `tradedbadmin` role.

```
# trade database
host    all            tradedbadmin     0.0.0.0/0               scram-sha-256
host    all            tradedbadmin     ::/0                    scram-sha-256
```

### KDB+ Initialization

#### get license
Request a 64-bit license from [Kx Systems](https://kx.com/kdb-insights-personal-edition-license-download). Fill the form
then the system will send an email with the license file and a base64 key (Either can be used to license the product).

#### Server side (Linux system)
1. Put the executable `q` and license in desired folder, say `/opt/l64` directory.
2. Create a folder to store the data, say `/opt/data` directory.
3. Set the environment variable `QLIC` to the directory where the license file is stored. And `QHOME` to the data directory.
4. Run the `q` executable with the following command:
    ```bash
    $ q -p 5000 -s 1 -w 100 -t 1000 -T 1000 -U /opt/l64/trade.q
    ```
   - `-p 5000`: This sets the port number for the kdb+ process. In this case, the port number is 5000.

   - `-s 1`: This sets the number of secondary threads. In this case, it's set to 1.

   - `-w 100`: This sets the workspace heap size. In this case, it's set to 100 MB.

   - `-t 1000`: This sets the timer interval in milliseconds. In this case, it's set to 1000 milliseconds, or 1 second.

   - `-T 1000`: This sets the timeout in seconds for client queries. In this case, it's set to 1000 seconds.

   - `-U /opt/l64/trade.q`: This sets the access control list file. In this case, the file is located at `/opt/l64/trade.q`. This file contains a list of usernames and passwords for clients that are allowed to connect to the kdb+ process.

### MetaData Initialization

Allowed instruments types are given in "Instrument Types" section of [meta_enumerations.md](doc/meta_enumerations.md).

### Data Initialization for Instrument Type(s)
```python
from trade_database_manager.manager import MetadataSql

metadatalib = MetadataSql()
metadatalib.initialize(for_inst_types="CB")
```

This will try to create two tables, `instruments` and `instruments_cb` in the database if not yet exists. The `instruments` table will store the common information of all instruments, and the `instruments_cb` table will store the type-specific information of the instruments of type `CB`.

The table fields are listed in the [data_organization.md](doc/data_organization.md) file.
file-organizer-advanced 1.0.2
A CLI tool to automatically organize files into categorized folders
Requires Python: >=3.6
Keywords: file, organizer, cli, automation, python, folder, sort

# File Organizer

A Python script to automatically organize files in a specified folder into categorized subfolders based on file types.

## Features

- Automatically moves files into folders like **Documents**, **Images**, **Videos**, **Audio**, **Archives**, **Apps**, and **Others**.
- Supports organizing any folder by providing the path as a CLI argument or via interactive prompt.
- Non-destructive: existing folders and files not matched by categories remain unaffected.
- Easy to use and extend with more file type categories.
- Installable from PyPI for easy setup.

## Installation

Install the latest release from PyPI:

~~~bash
pip install file-organizer-advanced
~~~

## Usage

Run the CLI command to organize your files:
~~~bash
file-organizer-advanced
~~~

By default, it organizes your Downloads folder. To specify a different folder:

Example:

~~~bash
file-organizer-advanced /Users/username/Downloads
~~~

## Development and Testing (Optional)

If you want to contribute or test the package locally, follow these steps:

1. **Create and activate a Python virtual environment:**

~~~bash
python3 -m venv env
source env/bin/activate   # On Windows use `env\Scripts\activate`
~~~
2. **Install the package from PyPI or Locally:**
~~~bash
pip install --upgrade pip
pip install file-organizer-advanced
# Or for local development:
pip install -e .
~~~
3. **Run the CLI tool to organize a folder:**
~~~bash
file-organizer-advanced /path/to/folder
~~~
4.	**When done, deactivate the environment:**
~~~bash
deactivate
~~~

## How it works

- Scans the target folder.
- Detects file types based on extensions.
- Moves files into corresponding folders.
- Creates folders if they don’t exist.

## Notes

- Running the CLI multiple times will not affect already organized files.
- The CLI does not modify files inside existing subfolders.
- Files without known extensions are moved to the **Others** folder.

# Sample Output:
✅ Moved: example.pdf → Documents
✅ Moved: vacation.jpg → Images
✅ Moved: project.zip → Archives
✅ Moved: app.dmg → Apps

📂 Folder '/Users/username/Downloads' organized successfully!

# Screenshot
![Script Output Screenshot](File_Organizer_Screenshot.PNG)
tocsmith 0.1.0
Create PDF bookmarks automatically using heuristics, with CLI and async tkinter GUI
Requires Python: >=3.9

# TocSmith

为 PDF 添加目录书签的实用工具，支持命令行与简易 GUI。通过“手动粘贴目录文本 + 页码偏移”的方式生成 PDF 书签（大纲/Outline）。

- 运行环境：Python 3.9+
- 依赖：pypdf（写书签）
- 提供方式：CLI、Tk GUI、Python API

## 功能概览
- 手动粘贴目录文本（每行以书中页码结尾），自动解析标题、页码与层级（1..6）
- 支持页码偏移（实际页码 - 书籍页码），用于扫描件/前置页差异
- 编号前缀会被保留到标题中：如 `第1章`、`1.1` 将出现在最终书签标题里
- 支持行首星号标记：允许输入 `*1.1 Title` 或 `* 1.1 Title`，输出统一为 `*1.1 Title`
- 将条目以父子层级写入 PDF 书签
- 提供 CLI 与 GUI；亦可通过 Python API 使用

## 快速开始

### 安装与运行（uv 推荐）
本仓库使用 uv 管理与分发工具。

1) 通过uv安装命令行工具（推荐）：
```bash
uv tool install tocsmith
# 安装后可直接使用：
tocsmith --help
tocsmith-gui
```

2) 使用 pip 安装（备选）：
```bash
pip install tocsmith

# 现在可直接使用：
tocsmith --help
tocsmith-gui
```

3) 本地开发
```bash
git clone https://github.com/wesleyel/pdf-bookmark.git
cd pdf-bookmark
uv sync

uv tool install . --reinstall

tocsmith --help
tocsmith-gui
```

## 命令行使用（CLI）

```bash
tocsmith --help
```

### 通过 TOML 批量执行（自定义格式）
支持通过 TOML 配置批量执行多个任务。相对路径均以配置文件所在目录为基准；还可以通过 `defaults.input_prefix` 与 `defaults.output_prefix` 设定输入/输出根目录。

示例 `config.toml`：

```toml
[defaults]
# global page offset
page_offset = 0
# global minimum length
min_len = 3

# input folder
input_prefix = "input"
# output folder
output_prefix = "output"
# output file name append
output_suffix = ".bookmarked.pdf"

[[tasks]]
# input file name. relative to input_prefix
input_file = "book1.pdf"
toc = """
第一章 绪论 1
1.1 引言 3
1.2 数学分析的基本概念 5
"""
page_offset = 10
min_len = 2
```

运行：

```bash
tocsmith --config config.toml
```

说明：
- `defaults` 中的 `page_offset`、`min_len` 可被每个任务覆盖。
- `input_prefix` 用于解析任务中的 `input_file`；`output_prefix` 为输出目录根。
- 输出文件名为 `{stem}{output_suffix}`，其中 `stem` 来源于 `input_file`。
- 任务可直接内联 `toc` 文本；也兼容 `toc_file` 指定外部文件。

## 图形界面（GUI）
提供一个基于 Tk 的简易界面，便于在桌面环境下操作：
```bash
tocsmith-gui
# 或
uv run python -m tocsmith.gui
```
基本流程：
- 选择输入 PDF
- 可选：修改输出路径
- 在 “TOC text” 中粘贴目录文本；在 “Page Offset” 填写偏移（实际 - 书籍）
- 点击 “Parse TOC Text” 查看解析结果
- 点击 “Generate” 生成带书签的 PDF

提示：Linux 上若缺少 tkinter，可通过安装系统包启用（例如 Debian/Ubuntu：`sudo apt-get update && sudo apt-get install -y python3-tk`）。

## 开发与测试

- 代码检查与测试：
```bash
uv tool install .  # 安装命令，便于本地手动验证
uv run pytest -q
# 可选：
uv run ruff check
uv run mypy tocsmith
```

- 项目结构：
```
tocsmith/
  core.py   # 目录解析与书签生成核心逻辑
  cli.py    # 命令行入口
  gui.py    # Tk GUI 入口
  tests/    # 单元测试（pytest）
```

## 许可证

MIT
adbctool 0.1.5
apollo dbc tool
Requires Python: >=3.6

## Gen Vehicle Protocol Tool

`adbctool` is a convinent tool to let you quickly generate a nearly complete code for a new vehicle for [Apollo](https://github.com/ApolloAuto/apollo).

You only have to do is to have the dbc file (which is a communication protocol for the car, which is usually made by the vehicle integrated company), and write a less 10 lines config for generate an encode/decode.

## How to get vehicle's DBC file

- [opendbc](https://github.com/commaai/opendbc) is a project that opens the vehicle dbc protocol.
- [openvehicles](https://docs.openvehicles.com/en/latest/index.html) is an open car project that contains any vehicle information you want to know.
- [whl-vehicles](https://github.com/daohu527/vehicles) Apollo vehicle protocols based by opendbc.

## Quick start

#### Install

You can install adbctool by following cmd.

```shell
pip3 install adbctool
```

## Example

Generate c++ code based on dbc file.

- `vehicle dbc file `: vehicle's dbc file
- `vehicle type`: vehicle type
- `sender`: the sender of the message, the control protocol template will be used for sender

Generate code using a specific sender name
```shell
# adbctool -f <vehicle.dbc> -t <vehicle_type> --sender <sender_name>
adbctool -f test/acura_ilx_2016_nidec.dbc -t acura_ilx --sender ADAS
```

Or generate code using a list of sender IDs (in hexadecimal)
```shell
# adbctool -f <vehicle.dbc> -t <vehicle_type> --sender_list <sender_id_hex>
adbctool -f test/acura_ilx_2016_nidec.dbc -t acura_ilx --sender_list 0x400
```
metasim-core 0.1.0
Meta-Sim: A unified simulation framework for robotics
Requires Python: >=3.8
Keywords: robotics, simulation, reinforcement-learning, computer-vision

# Meta-Sim

A unified simulation framework for robotics that supports multiple physics simulators.

## Installation

### Basic Installation

```bash
pip install metasim-core
```

### Installation with Specific Simulators

Install with support for specific simulators:

```bash
# For MuJoCo support
pip install metasim-core[mujoco]

# For PyBullet support
pip install metasim-core[pybullet]

# For Sapien3 support
pip install metasim-core[sapien3]

# For Genesis support
pip install metasim-core[genesis]

# For all simulators (except Isaac)
pip install metasim-core[all]
```


## Quick Start

```python
import metasim
from metasim.sim import base

# Your simulation code here
```

## Supported Simulators

- **MuJoCo**: High-performance physics simulation
- **PyBullet**: Open-source physics simulation
- **Sapien**: Photorealistic rendering and physics
- **Genesis**: Next-generation simulation platform
- **Isaac Gym**: NVIDIA's physics simulation (requires separate installation)
- **Isaac Lab**: Advanced robotics simulation
- **Blender**: 3D creation and rendering

## License

MIT License
kittentts 0.1.3
Implementation of KittenTTS


# KittenTTS Example

This project demonstrates how to use the **KittenTTS** text-to-speech model to convert text into a `.wav` audio file.

## Features
- Convert any given text into natural-sounding speech.
- Choose a specific voice for the generated audio.
- Control the playback speed.

## Requirements
Install the required Python packages:
```bash
apt install espeak
pip install KittenTTS
```

## Usage

Below is a minimal working example:

```python
import soundfile as sf
from kittentts import KittenTTS  
# Initialize the model
model = KittenTTS()

# Generate speech
audio_data = model.generate(
    text="hi who are you",
    voice="expr-voice-2-m",
    speed=1.0
)

# Save output as a WAV file
sf.write("test.wav", audio_data, 24000)
```

## Parameters

* **text** *(str)*: The text you want to convert to speech.
* **voice** *(str)*: The voice preset to use (e.g., `"expr-voice-2-m"`).
* **speed** *(float)*: Playback speed multiplier (1.0 = normal speed).

## Reference
https://github.com/KittenML/KittenTTS

## Implementation with GUI
https://huggingface.co/spaces/shethjenil/KittenTTS
tree-sitter-orca 0.2.2
Parser for input file for the Orca quantum chemistry package
Requires Python: >=3.10
Keywords: incremental, parsing, tree-sitter, orca

# Tree-sitter ORCA

A [Tree-sitter](https://tree-sitter.github.io/tree-sitter/) grammar for [ORCA](https://www.faccts.de/orca/) quantum chemistry input files.

ORCA is a quantum chemistry package for electronic structure calculations.
This grammar parses ORCA input files (`.inp`) including simple command lines, input blocks, geometry specifications as well as complex workflows using compound scripts.
In addition, it provides queries to support syntax highlighting, proper indentation and code folding.

## Demo

<img width="690" height="729" alt="SCR-20250808-ufgv" src="https://github.com/user-attachments/assets/a4dbca4a-0545-4cb6-bdcb-a1a989b401ac" />

## Installation

### PyPI

```bash
pip install tree-sitter-orca
```

### Neovim with nvim-treesitter

#### Enable Parser

Add to your `init.lua`:

```lua
-- Define ORCA '*.inp' extension
vim.filetype.add({
	extension = {
		inp = "inp",
	},
})
-- Enable custom tree-sitter parser
local parser_config = require "nvim-treesitter.parsers".get_parser_configs()
parser_config.orca = {
  install_info = {
    url = "https://github.com/kszenes/tree-sitter-orca",
    files = { "src/parser.c" },
    branch = "main",
  },
  filetype = "inp",
}
```

Install the parser in Neovim using

```
:TSUpdate orca
```

You should now be able to inspect the abstract syntax tree from within Neovim using `:TSInspect`

#### Syntax Highlighting

Create syntax highlighting:

```bash
mkdir -p ~/.config/nvim/queries/orca
ln -s /path/to/tree-sitter-orca/queries/highlights.scm ~/.config/nvim/queries/orca/highlights.scm
```
facu-weather-flow 0.1.9
ETL para extraer, transformar y cargar datos del clima en S3
Requires Python: >=3.8

# Facu Weather Flow

**Facu Weather Flow** es una librería de Python modular que implementa un pipeline ETL para procesar datos del clima de cualquier ciudad que necesites.

**Facu Weather Flow** is a modular Python library that implements an ETL pipeline to process weather data for any city you need.

---

## 🚀 Características / Features

- Extracción de datos desde la API de OpenWeatherMap  
  Data extraction from the OpenWeatherMap API
- Transformación a DTOs estructurados  
  Transformation into structured DTOs
- Almacenamiento como archivos Parquet  
  Storage as Parquet files
- Carga directa en un bucket S3 en AWS  
  Direct upload to an AWS S3 bucket
- Configuración flexible mediante variables de entorno  
  Flexible configuration through environment variables
- Uso como librería o desde la línea de comandos (`weather-flow`)  
  Usable as a library or from the command line (`weather-flow`)

---

## 📦 Instalación / Installation

```bash
pip install facu-weather-flow
```

---

## 🧪 Uso desde CLI / CLI Usage

```bash
weather-flow
```

O como módulo / Or as a module:

```bash
python -m facu_weather_flow
```

---

## ⚙️ Variables de entorno / Environment Variables

La librería requiere las siguientes variables, definidas en un archivo `.env` o como variables del entorno:  
The library requires the following variables, defined in a `.env` file or as environment variables:

```env
OPENWEATHERMAP_API_KEY=tu_api_key_openweathermap / your_openweathermap_api_key
CITY_DATA=nombre_ciudad_o_archivo / city_name_or_file
AWS_ACCESS_KEY_ID=tu_access_key / your_access_key
AWS_SECRET_ACCESS_KEY=tu_secret_key / your_secret_key
AWS_DEFAULT_REGION=us-east-1
AWS_WEATHER_BUCKET_NAME=nombre_del_bucket_s3 / your_s3_bucket_name
WEATHER_OUTPUT_DIR=/ruta/de/salida/local / local_output_path
```

---

## 📚 Ejemplo de uso como módulo / Example module usage

```python
from facu_weather_flow.main import run_pipeline

run_pipeline()
```

---

## 🧊 Licencia / License

MIT © Facu Vega
database-mcp-server 1.0.2
Database MCP Server for SQL operations
Requires Python: >=3.13

# Database MCP Server

## 📋 Documentation Languages | 文档语言

[![English](https://img.shields.io/badge/English-Documentation-blue?style=for-the-badge)](./docs/README_en.md)
[![中文](https://img.shields.io/badge/中文-文档-red?style=for-the-badge)](./docs/README_zh.md)

---

## 🚀 Quick Start | 快速开始

A powerful database MCP (Model Context Protocol) server with multi-data source management and advanced SQL operations including table structure comparison and synchronization capabilities.

一个强大的数据库 MCP（模型上下文协议）服务器，支持多数据源管理和高级 SQL 操作，包括表结构对比和同步功能。

## 🎨 Key Features | 主要特性

- ✅ **Multi-Data Source Support** | **多数据源支持**
- ✅ **Table Structure Comparison** | **表结构对比**
- ✅ **SQL Generation** | **SQL 生成**
- ✅ **Data Export/Import** | **数据导出/导入**
- ✅ **Connection Pooling** | **连接池管理**

## 📚 Full Documentation | 完整文档

Please choose your preferred language for detailed documentation:

请选择您偶爱的语言查看详细文档：

### 🌍 Language Options | 语言选项

| Language | Documentation | Description |
|----------|---------------|-------------|
| 🇺🇸 English | [README_en.md](./docs/README_en.md) | Complete English documentation with installation, configuration, and usage examples |
| 🇨🇳 中文 | [README_zh.md](./docs/README_zh.md) | 完整的中文文档，包括安装、配置和使用示例 |

---

## ⚡ Quick Installation | 快速安装

```bash
# Install using pip | 使用 pip 安装
pip install database-mcp-server

# Or using uv | 或使用 uv
uvx database-mcp-server
```

## 📁 Project Structure | 项目结构

```
database-mcp-python/
├── README.md                        # This file | 本文件 (多语言导航)
├── docs/                            # Documentation | 文档目录
│   ├── README_en.md                 # English documentation | 英文文档
│   └── README_zh.md                 # Chinese documentation | 中文文档
├── src/                             # Source code | 源代码
│   ├── __init__.py                  # MCP service entry | MCP 服务入口
│   ├── factory/                     # Factory pattern | 工厂模式
│   ├── strategy/                    # Strategy pattern | 策略模式
│   ├── model/                       # Data models | 数据模型
│   └── tools/                       # Utility tools | 工具类
├── database-config.example.yaml     # Config example | 配置示例
└── pyproject.toml                   # Project config | 项目配置
```

## 🔗 Quick Links | 快速链接

- [📖 English Documentation](./docs/README_en.md) - Complete setup and usage guide
- [📖 中文文档](./docs/README_zh.md) - 完整安装和使用指南
- [💾 Installation Guide](./docs/README_en.md#installation) - How to install and configure
- [🔧 Configuration](./docs/README_en.md#configuration) - Setup your data sources
- [📚 Usage Examples](./docs/README_en.md#usage-examples) - Learn with examples
- [🔄 Table Comparison](./docs/README_en.md#advanced-table-structure-management) - Advanced features

---

### 📄 License | 许可证

MIT License

### 🤝 Contributing | 贡献

Issues and Pull Requests are welcome! | 欢迎提交 Issue 和 Pull Request！