semvec/pypi-packages huggingface dataset:
zetcode-qita-jiaocheng-chuyi-fix1 2025.3.24.0
Zetcode 其它教程（初译）
Requires Python: >=3.6

# Zetcode 其它教程（初译）

## 下载

### Docker

```
docker pull apachecn0/zetcode-qita-jiaocheng-chuyi
docker run -tid -p <port>:80 apachecn0/zetcode-qita-jiaocheng-chuyi
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install zetcode-qita-jiaocheng-chuyi
zetcode-qita-jiaocheng-chuyi <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g zetcode-qita-jiaocheng-chuyi
zetcode-qita-jiaocheng-chuyi <port>
# 访问 http://localhost:{port} 查看文档
```
openai-tts-mcp-server 0.1.3
An MCP server integrating with OpenAI TTS (text-to-speech) API
Requires Python: >=3.12

# openai-tts-mcp-server MCP server

An MCP server integrating with OpenAI TTS (text-to-speech) API

## Components

### Resources

The server implements a simple note storage system with:
- Custom note:// URI scheme for accessing individual notes
- Each note resource has a name, description and text/plain mimetype

### Prompts

The server provides a single prompt:
- summarize-notes: Creates summaries of all stored notes
  - Optional "style" argument to control detail level (brief/detailed)
  - Generates prompt combining all current notes with style preference

### Tools

The server implements one tool:
- add-note: Adds a new note to the server
  - Takes "name" and "content" as required string arguments
  - Updates server state and notifies clients of resource changes

## Configuration

[TODO: Add configuration details specific to your implementation]

## Quickstart

### Install

#### Claude Desktop

On MacOS: `~/Library/Application\ Support/Claude/claude_desktop_config.json`
On Windows: `%APPDATA%/Claude/claude_desktop_config.json`

<details>
  <summary>Development/Unpublished Servers Configuration</summary>
  ```
  "mcpServers": {
    "openai-tts-mcp-server": {
      "command": "uv",
      "args": [
        "--directory",
        "/Users/tomek/workspace/openai-tts-mcp-server",
        "run",
        "openai-tts-mcp-server"
      ]
    }
  }
  ```
</details>

<details>
  <summary>Published Servers Configuration</summary>
  ```
  "mcpServers": {
    "openai-tts-mcp-server": {
      "command": "uvx",
      "args": [
        "openai-tts-mcp-server"
      ]
    }
  }
  ```
</details>

## Development

### Building and Publishing

To prepare the package for distribution:

1. Sync dependencies and update lockfile:
```bash
uv sync
```

2. Build package distributions:
```bash
uv build
```

This will create source and wheel distributions in the `dist/` directory.

3. Publish to PyPI:
```bash
uv publish
```

Note: You'll need to set PyPI credentials via environment variables or command flags:
- Token: `--token` or `UV_PUBLISH_TOKEN`
- Or username/password: `--username`/`UV_PUBLISH_USERNAME` and `--password`/`UV_PUBLISH_PASSWORD`

### Debugging

Since MCP servers run over stdio, debugging can be challenging. For the best debugging
experience, we strongly recommend using the [MCP Inspector](https://github.com/modelcontextprotocol/inspector).


You can launch the MCP Inspector via [`npm`](https://docs.npmjs.com/downloading-and-installing-node-js-and-npm) with this command:

```bash
npx @modelcontextprotocol/inspector uv --directory /Users/tomek/workspace/openai-tts-mcp-server run openai-tts-mcp-server
```


Upon launching, the Inspector will display a URL that you can access in your browser to begin debugging.
zetcode-python-jiaocheng-chuyi-fix1 2025.3.24.0
Zetcode Python 教程（初译）
Requires Python: >=3.6

# Zetcode Python 教程（初译）

## 下载

### Docker

```
docker pull apachecn0/zetcode-python-jiaocheng-chuyi
docker run -tid -p <port>:80 apachecn0/zetcode-python-jiaocheng-chuyi
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install zetcode-python-jiaocheng-chuyi
zetcode-python-jiaocheng-chuyi <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g zetcode-python-jiaocheng-chuyi
zetcode-python-jiaocheng-chuyi <port>
# 访问 http://localhost:{port} 查看文档
```
zetcode-php-jiaocheng-chuyi-fix1 2025.3.24.0
Zetcode PHP 教程（初译）
Requires Python: >=3.6

# Zetcode PHP 教程（初译）

## 下载

### Docker

```
docker pull apachecn0/zetcode-php-jiaocheng-chuyi
docker run -tid -p <port>:80 apachecn0/zetcode-php-jiaocheng-chuyi
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install zetcode-php-jiaocheng-chuyi
zetcode-php-jiaocheng-chuyi <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g zetcode-php-jiaocheng-chuyi
zetcode-php-jiaocheng-chuyi <port>
# 访问 http://localhost:{port} 查看文档
```
zetcode-kotlin-jiaocheng-chuyi-fix1 2025.3.24.0
Zetcode Kotlin 教程（初译）
Requires Python: >=3.6

# Zetcode Kotlin 教程（初译）

## 下载

### Docker

```
docker pull apachecn0/zetcode-kotlin-jiaocheng-chuyi
docker run -tid -p <port>:80 apachecn0/zetcode-kotlin-jiaocheng-chuyi
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install zetcode-kotlin-jiaocheng-chuyi
zetcode-kotlin-jiaocheng-chuyi <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g zetcode-kotlin-jiaocheng-chuyi
zetcode-kotlin-jiaocheng-chuyi <port>
# 访问 http://localhost:{port} 查看文档
```
zetcode-java-jiaocheng-chuyi-fix1 2025.3.24.0
Zetcode Java 教程（初译）
Requires Python: >=3.6

# Zetcode Java 教程（初译）

## 下载

### Docker

```
docker pull apachecn0/zetcode-java-jiaocheng-chuyi
docker run -tid -p <port>:80 apachecn0/zetcode-java-jiaocheng-chuyi
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install zetcode-java-jiaocheng-chuyi
zetcode-java-jiaocheng-chuyi <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g zetcode-java-jiaocheng-chuyi
zetcode-java-jiaocheng-chuyi <port>
# 访问 http://localhost:{port} 查看文档
```
mytorch123 0.3.1
Drop-in replacement for PyTorch that does distributed training and inference on a remote server
Requires Python: >=3.10

# myTorch

myTorch a wrapper around PyTorch. As such, it is a drop-in replacement for PyTorch. Calls to PyTorch can be replaced with myTorch with no code changes except for the import statement in the Python file. This allows you to code with PyTorch on your local machine, but do the training and/or inference on a remote server.
python-package-12175 0.0.1
A test python package for class assignment

# Test Package 12175

### Note: This package is a test package uploaded for class assignment.

```diff
- SAP: 590012175
```

_**This package contains 4 functions:**_

```python
def  add(x: int, y: int) -> int
```

```python
def  subtract(x: int, y: int) -> int
```

```python
def  multiply(x: int, y: int) -> int
```

```python
def  divide(x: float, y: float) -> int
```
yuanmeng-xiangmu-biji-wangzhuan-juejin-shicao-xiangmu-dianzishu-1911-fix1 2025.3.24.0
缘梦项目笔记——网赚掘金实操项目电子书 1911
Requires Python: >=3.6

# 缘梦项目笔记——网赚掘金实操项目电子书 1911

## 下载

### Docker

```
docker pull apachecn0/yuanmeng-xiangmu-biji-wangzhuan-juejin-shicao-xiangmu-dianzishu-1911
docker run -tid -p <port>:80 apachecn0/yuanmeng-xiangmu-biji-wangzhuan-juejin-shicao-xiangmu-dianzishu-1911
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install yuanmeng-xiangmu-biji-wangzhuan-juejin-shicao-xiangmu-dianzishu-1911
yuanmeng-xiangmu-biji-wangzhuan-juejin-shicao-xiangmu-dianzishu-1911 <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g yuanmeng-xiangmu-biji-wangzhuan-juejin-shicao-xiangmu-dianzishu-yijiuyiyi
yuanmeng-xiangmu-biji-wangzhuan-juejin-shicao-xiangmu-dianzishu-yijiuyiyi <port>
# 访问 http://localhost:{port} 查看文档
```
yuanmeng-xiangmu-biji-wangzhuan-juejin-shicao-xiangmu-dianzishu-1910-fix1 2025.3.24.0
缘梦项目笔记——网赚掘金实操项目电子书 1910
Requires Python: >=3.6

# 缘梦项目笔记——网赚掘金实操项目电子书 1910

## 下载

### Docker

```
docker pull apachecn0/yuanmeng-xiangmu-biji-wangzhuan-juejin-shicao-xiangmu-dianzishu-1910
docker run -tid -p <port>:80 apachecn0/yuanmeng-xiangmu-biji-wangzhuan-juejin-shicao-xiangmu-dianzishu-1910
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install yuanmeng-xiangmu-biji-wangzhuan-juejin-shicao-xiangmu-dianzishu-1910
yuanmeng-xiangmu-biji-wangzhuan-juejin-shicao-xiangmu-dianzishu-1910 <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g yuanmeng-xiangmu-biji-wangzhuan-juejin-shicao-xiangmu-dianzishu-yijiuyiling
yuanmeng-xiangmu-biji-wangzhuan-juejin-shicao-xiangmu-dianzishu-yijiuyiling <port>
# 访问 http://localhost:{port} 查看文档
```
yuanmeng-xiangmu-biji-wangzhuan-juejin-shicao-xiangmu-dianzishu-1909-fix1 2025.3.24.0
缘梦项目笔记——网赚掘金实操项目电子书 1909
Requires Python: >=3.6

# 缘梦项目笔记——网赚掘金实操项目电子书 1909

## 下载

### Docker

```
docker pull apachecn0/yuanmeng-xiangmu-biji-wangzhuan-juejin-shicao-xiangmu-dianzishu-1909
docker run -tid -p <port>:80 apachecn0/yuanmeng-xiangmu-biji-wangzhuan-juejin-shicao-xiangmu-dianzishu-1909
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install yuanmeng-xiangmu-biji-wangzhuan-juejin-shicao-xiangmu-dianzishu-1909
yuanmeng-xiangmu-biji-wangzhuan-juejin-shicao-xiangmu-dianzishu-1909 <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g yuanmeng-xiangmu-biji-wangzhuan-juejin-shicao-xiangmu-dianzishu-yijiulingjiu
yuanmeng-xiangmu-biji-wangzhuan-juejin-shicao-xiangmu-dianzishu-yijiulingjiu <port>
# 访问 http://localhost:{port} 查看文档
```
yuanmeng-xiangmu-biji-wangzhuan-juejin-shicao-xiangmu-dianzishu-1907-fix1 2025.3.24.0
缘梦项目笔记——网赚掘金实操项目电子书 1907
Requires Python: >=3.6

# 缘梦项目笔记——网赚掘金实操项目电子书 1907

## 下载

### Docker

```
docker pull apachecn0/yuanmeng-xiangmu-biji-wangzhuan-juejin-shicao-xiangmu-dianzishu-1907
docker run -tid -p <port>:80 apachecn0/yuanmeng-xiangmu-biji-wangzhuan-juejin-shicao-xiangmu-dianzishu-1907
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install yuanmeng-xiangmu-biji-wangzhuan-juejin-shicao-xiangmu-dianzishu-1907
yuanmeng-xiangmu-biji-wangzhuan-juejin-shicao-xiangmu-dianzishu-1907 <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g yuanmeng-xiangmu-biji-wangzhuan-juejin-shicao-xiangmu-dianzishu-yijiulingqi
yuanmeng-xiangmu-biji-wangzhuan-juejin-shicao-xiangmu-dianzishu-yijiulingqi <port>
# 访问 http://localhost:{port} 查看文档
```
lucaswangtool 0.0.1
一个演示用数学运算库
Requires Python: >=3.6

# lucaswangtool

This is a demonstration math operations library.


# 在项目根目录执行
python -m venv .venv  # 创建名为 .venv 的虚拟环境

# Linux/macOS
source .venv/bin/activate

pip install build

pip install -e .  # 会自动读取 setup.py/setup.cfg 的配置

# Windows
.\.venv\Scripts\activate

# 打包
rm -rf dist/ build/ 
python -m build
twine check dist/* 
# 上传到 PyPI（使用默认配置）
twine upload dist/*   

## Installation
```bash
pip install lucaswangtool
testronaut 1.1.1
A CLI tool that bridges code analytics, automated test generation, and smart CI/CD optimization—so your dev workflow scales with your codebase.
Requires Python: >=3.8

# Testronaut

**A CLI tool that bridges code analytics, automated test generation, and smart CI/CD optimization—so your dev workflow scales with your codebase.**

---

## Inspiration

As developers, we’re responsible for writing efficient, high-quality code — but that also means spending countless hours manually writing unit/integration tests and analyzing code performance. These tasks can take up **over 20% of a developer's day** in the industry.

**Testronaut** streamlines this process by automating the first step in the CI/CD pipeline, all from within your terminal — no need to leave your IDE.

---

## What It Does

Testronaut is a terminal-based CLI tool powered by Google Gemini, built to accelerate various parts of the software development lifecycle:

- **Test Case Generation**  
  Automatically generate unit tests and integration tests from source code.

- **Code Performance Analysis**  
  Analyze performance bottlenecks and get suggestions for optimization.

- **Code Refactoring**  
  Get clean, efficient refactoring suggestions to improve maintainability and readability.

- **CI/CD Pipeline Checks**  
  Quickly validate your CI/CD configuration files for common issues and misconfigurations.

---

## How We Built It

Testronaut combines the power of **Python**, **Node.js**, and **Google Gemini** to deliver a smooth developer experience:

- **Python** – Core CLI logic and orchestration
- **Node.js** – Enhances the terminal interface for better visuals
- **Google Gemini API** – Leverages LLMs for code analysis and generation
- **PyPI** – Easy installation and distribution

We designed the tool with a **modular architecture**, separating functionalities into test generation, performance analysis, refactoring, and CI/CD validation for future scalability.

---

## Accomplishments

- A clean and functional CLI experience
- Seamless integration with Google Gemini for LLM-powered suggestions
- A fully modular design — easy to extend and maintain
- Published on [PyPI](https://pypi.org/project/testronaut) for quick installation via `pip`
- Automates multiple parts of the SDLC from a single terminal command

---

## What’s Next

We plan to continue developing Testronaut by:

- Supporting more programming languages and CI/CD providers
- Fine-tuning LLM prompts for more accurate results
- Allowing developers to bring their own LLMs for increased control/security
- Adding a plugin system to support community-built modules
- Exploring IDE plugin integrations (VSCode, JetBrains, etc.)
- Adding deeper CI/CD inspection including security and performance validations

---

## Installation

```bash
pip install testronaut
```

Then simply run:

```bash
testronaut
```

---

## Repository

[GitHub: Dknx8888/grizzy7](https://github.com/Dknx8888/grizzy7)

---