semvec/pypi-packages huggingface dataset:
cartel 1.1a1
Как ему не страшно говорить про кортель
Requires Python: >=3.6

Библиотека cartel: как ей пользоваться

cartel - это шуточная библиотека при помощи которой можно написать скрипт который будет выводить текстовый мем из тиктока "Как ему не страшно говорить про кортель"
Пример использования:

from cartel import cartel

text = carteltext()
print(text)

Пользуйтесь на здоровье! (Интересно для чего ее вообще будут использовать...)
refine-user-story-mcp 0.4.0
A tool for analyzing user stories using the INVEST criteria
Requires Python: >=3.8

# User Story INVEST Analyzer

A tool for analyzing user stories using the INVEST criteria and providing improvement recommendations.

## Installation

```bash
pip install refine-user-story-mcp
```

## Setup

1. Create a `.env` file with your Groq API credentials:

```
GROQ_API_KEY=your_groq_api_key_here
GROQ_MODEL=mixtral-8x7b-32768
```

2. Import and use the tool in your Python code:

```python
from refine_user_story_mcp import invest_analyze

# Sample user story
user_story = {
    "UserStory": {
        "Title": "User Registration",
        "Description": "As a website visitor, I want to register an account so that I can access member features.",
        "AcceptanceCriteria": [
            "User can enter email and password",
            "System validates email format",
            "User receives confirmation email"
        ],
        "AdditionalInformation": "Registration should be quick and intuitive."
    }
}

# Analyze the user story
result = invest_analyze(user_story=user_story, format_output=True)
print(result["content"][0]["text"])
```

## Features

- Analyzes user stories based on the INVEST criteria (Independent, Negotiable, Valuable, Estimable, Small, Testable)
- Scores each criterion on a scale of 1-5
- Provides detailed explanations and recommendations
- Suggests improvements to the original user story
- Returns results in either formatted text or raw JSON

## Usage as MCP Tool

You can also use this as an MCP (Multi-Agent Conversational Protocol) tool:

```python
from mcp.client import MCPClient

client = MCPClient()
client.register_tool("refine_user_story_mcp")

result = client.call_tool("invest_analyze", {"user_story": user_story})
```

## License

MIT
nonebot-plugin-mcp 0.0.2
插件描述
Requires Python: <3.13,>=3.10

<div align="center">
    <a href="https://v2.nonebot.dev/store">
    <img src="https://raw.githubusercontent.com/fllesser/nonebot-plugin-template/refs/heads/resource/.docs/NoneBotPlugin.svg" width="310" alt="logo"></a>

## ✨ nonebot-plugin-mcp ✨

<a href="./LICENSE">
    <img src="https://img.shields.io/github/license/X-Zero-L/nonebot-plugin-mcp.svg" alt="license">
</a>
<a href="https://pypi.python.org/pypi/nonebot-plugin-mcp">
    <img src="https://img.shields.io/pypi/v/nonebot-plugin-mcp.svg" alt="pypi">
</a>
<img src="https://img.shields.io/badge/python-3.10+-blue.svg" alt="python">
<a href="https://github.com/astral-sh/ruff">
    <img src="https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/charliermarsh/ruff/main/assets/badge/v2.json" alt="ruff">
</a>
<a href="https://github.com/astral-sh/uv">
    <img src="https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/uv/main/assets/badge/v0.json" alt="uv">
</a>
</div>

> [!IMPORTANT]
> **收藏项目** ～⭐️

<img width="100%" src="https://starify.komoridevs.icu/api/starify?owner=X-Zero-L&repo=nonebot-plugin-mcp" alt="starify" />


## 📖 介绍

施工中，请勿使用

## 💿 安装

<details open>
<summary>使用 nb-cli 安装</summary>
在 nonebot2 项目的根目录下打开命令行, 输入以下指令即可安装

    nb plugin install nonebot-plugin-mcp --upgrade
使用 **pypi** 源安装

    nb plugin install nonebot-plugin-mcp --upgrade -i "https://pypi.org/simple"
使用**清华源**安装

    nb plugin install nonebot-plugin-mcp --upgrade -i "https://pypi.tuna.tsinghua.edu.cn/simple"


</details>

<details>
<summary>使用包管理器安装</summary>
在 nonebot2 项目的插件目录下, 打开命令行, 根据你使用的包管理器, 输入相应的安装命令

<details open>
<summary>uv</summary>

    uv add nonebot-plugin-mcp
安装仓库 master 分支

    uv add git+https://github.com/X-Zero-L/nonebot-plugin-mcp@master
</details>

<details>
<summary>pdm</summary>

    pdm add nonebot-plugin-mcp
安装仓库 master 分支

    pdm add git+https://github.com/X-Zero-L/nonebot-plugin-mcp@master
</details>
<details>
<summary>poetry</summary>

    poetry add nonebot-plugin-mcp
安装仓库 master 分支

    poetry add git+https://github.com/X-Zero-L/nonebot-plugin-mcp@master
</details>

打开 nonebot2 项目根目录下的 `pyproject.toml` 文件, 在 `[tool.nonebot]` 部分追加写入

    plugins = ["nonebot_plugin_mcp"]

</details>

## ⚙️ 配置

在 nonebot2 项目的`.env`文件中添加下表中的必填配置

| 配置项  | 必填  | 默认值 |   说明   |
| :-----: | :---: | :----: | :------: |
| 配置项1 |  是   |   无   | 配置说明 |
| 配置项2 |  否   |   无   | 配置说明 |

## 🎉 使用
### 指令表
| 指令  | 权限  | 需要@ | 范围  |   说明   |
| :---: | :---: | :---: | :---: | :------: |
| 指令1 | 主人  |  否   | 私聊  | 指令说明 |
| 指令2 | 群员  |  是   | 群聊  | 指令说明 |

### 🎨 效果图
如果有效果图的话
pyg4i 0.2.2
Более удобное использование g4f
Requires Python: >=3.6

# pyg4i

pyg4i — это мощная библиотека, предоставляющая удобный интерфейс для работы с библиотекой g4f. Она значительно упрощает взаимодействие с g4f и делает его более доступным для разработчиков. С помощью pyg4i вы можете бесплатно общаться с моделями GPT-4 и DeepSeek, что открывает новые возможности для создания интеллектуальных приложений.

## Возможности

* Бесплатное общение с моделью GPT-4. 
* Интеграция с DeepSeek для эффективного поиска информации. 
* Удобные функции для управления контекстом, позволяющие адаптировать взаимодействие с моделью под ваши нужды.

## Установка

Для установки pyg4i используйте пакетный менеджер pip:

```bash

pip install pyg4i
```

## Примеры использования

После успешной установки библиотеки, вы можете начать использовать её следующим образом:

```python
from pyg4i import g4i

# Получение ответа от модели

response = g4i.answer("Ваш запрос здесь")

print(response)
```

## Управление контекстом
### Очистка контекста

Чтобы очистить текущий контекст, используйте следующую функцию:

```python
g4i.clearContext()
```

### Отключение контекста

Если вам необходимо, чтобы модель не запоминала предыдущие сообщения, вы можете отключить контекст:

```python
g4i.contextDisable()
```

### Включение контекста

Для повторного включения контекста используйте следующую команду:

```python
g4i.contextActivate()
```
clientgram 0.0.1
Test module, not Unstable version 


<p align="center">
  <a href="https://badge.fury.io/py/socks">
    <img src="https://badge.fury.io/py/clientgram.svg" alt="PyPI version" />
  </a>
</p>

# Clientgram

**Clientgram** is a lightweight Python library designed to **manage Telegram bots** using the **Telegram API**. Unlike heavier libraries, Clientgram focuses on providing a simple and efficient way to handle bot interactions, allowing you to easily manage messages, and users, media sharing, with minimal overhead.

## Features:
- Lightweight and optimized for managing Telegram bots.
- Send and receive messages through the Telegram Bot API.
- Supports easy integration, customizable message responses.

## Simple Examples:
### Handle Incoming Messages
```python
from clientgram import Clientgram
# Create the bot client
bot = Clientgram('YOUR_BOT_TOKEN')
# Respond to incoming messages
@bot.on_message()
def handle_message(message):
    bot.send_message(message.chat.id, "This is a test!")

#Run the bot or client
bot.run()
```
```bash
pip install clientgram
```
__Simple__ not **easy**.
##Stay notified with latest new versions.
pygression 1.0.0
A package about chord progressions.

# pygression

## Overview
pygression is a Python package that adds the ability to represent chord progressions to your code.

## Installation
You can install pygression with pip:
```
python3 -m pip install pygression
```

## Basic Tutorial
The `Note` class is mainly used to get a chord progression in a specific key. It is instantiated with a `Letter` and an optional `Accidental`.
```python
>>> from pygression import Note, Letter, Accidental
>>> note = Note(Letter.A, accidental=Accidental.FLAT)
>>> note
Ab
```

The `Roman` class is needed to create chord progressions that include any kind of chord. It is instantiated with a degree and an optional `Accidental`.
```python
>>> from pygression import Roman
>>> roman = Roman(6, accidental=Accidental.FLAT)
>>> roman
bVI
```

The `RomanChord` class represents chords in Roman numeral analysis. It is instantiated with a `Roman` object and an optional `Quality`.
```python
>>> from pygression import RomanChord
>>> from pygression.quality import triad
>>> rchord = RomanChord(roman, quality=triad.Major())
>>> rchord
bVI
```

The `Progression` class represents chord progressions. Pass in a list of the degrees of the chords or `RomanChord`s in the progression with a mode and how the progression is viewed.
```python
>>> from pygression import Progression, Mode
>>> axis = Progression([1, 5, rchord, 4], mode=Mode.AEO, relative_to=Mode.ION)
>>> axis
[i, v, bVI, iv]
```

To get this progression in a specific key, use the `chords_in` method. This method takes in a `Note` object.
```python
>>> chords = axis.chords_in(note)
>>> chords
[Abm, Ebm, Fb, Dbm]
```

More advanced features can be found in the documentation: https://pygression.readthedocs.io/en/latest/index.html.
bigmodel-hub 1.0.0
BigModel Hub CLI Toolkit
Requires Python: <4,>=3.11
Keywords: Big Model, LLM, Hugging Face, ModelScope

# BigModel Hub

## Overview

This project enhances the functionality of the ModelScope CLI tool, optimizing the experience of downloading new models from Hugging Face Hub and ModelScope Hub, as well as querying, updating, and removing downloaded models.

## Model Storage

By default, BigModel Hub leverages the caching mechanisms of Hugging Face Hub or ModelScope Hub to store models, ensuring maximum compatibility and storage efficiency.

To modify the default cache directory location, refer to the documentation of Hugging Face Hub or ModelScope Hub and set the environment variables `HF_HUB_CACHE` or `MODELSCOPE_CACHE` accordingly.

If you prefer not to rely on the caching mechanisms of Hugging Face Hub or ModelScope Hub, manually specify a local model storage directory and adhere to the following structure:

```
|- <Local Model Storage Directory>
   |- <Organization ID>
      |- <Model ID>
      |- ...
   |- ...
```

Example of a local model storage directory structure:

```
|- <Local Model Storage Directory>
   |- Qwen
      |- Qwen2.5-7B-Instruct
         |- config.json
         |- ...
```

## Installation

```bash
pip install bigmodel-hub
```

Test the availability of the BigModel Hub CLI:

```bash
bmhub --help
```

If accessing Hugging Face is hard, set the `HF_ENDPOINT` environment variable:

```bash
HF_ENDPOINT=https://hf-mirror.com bmhub --help
```

## CLI Features

[CLI Documentation](CLI.md)

### List Models

List downloaded models, filtered by model ID glob patterns, and view storage usage details.

By default, searches within Hugging Face Hub or ModelScope Hub cache directories. Use `--local-dir` to specify a custom local model storage directory.

```bash
bmhub list --help
```

[CLI Documentation](CLI.md#bmhub-list)

### Download Models

Download a model by the model ID. If the model already exists, it will be updated.

By default, models are downloaded to Hugging Face Hub or ModelScope Hub cache directories. Use `--local-dir` to specify a custom local storage directory

```bash
bmhub download --help
```

[CLI Documentation](CLI.md#bmhub-download)

### Update Models

Update downloaded models, filtered by model ID glob patterns.

By default, updates models in Hugging Face Hub or ModelScope Hub cache directories. Use `--local-dir` to specify a custom local storage directory.

```bash
bmhub update --help
```

[CLI Documentation](CLI.md#bmhub-update)

### Remove Models

Delete downloaded models, filtered by model ID glob patterns.

By default, removes models from Hugging Face Hub or ModelScope Hub cache directories. Use `--local-dir` to specify a custom local storage directory.

```bash
bmhub remove --help
```

[CLI Documentation](CLI.md#bmhub-remove)
nba-scores 0.1.7
A CLI interface to get current NBA scores and standings.
Requires Python: >=3.8

# NBA

NBA is a simple Python package that provides you with a quick and easy
command-line interface to current NBA scores and standings.

See the [Installation](#installation) & [Usage](#usage) sections below
for more information.

# Table of Contents

- [Installation](#installation)
- [Usage](#usage)
- [Contributing](#contributing)

# Installation

[Back to top](#table-of-contents)

## PyPi

```shell
pipx install nba-scores
```

## Manual

To run the script locally, run the following commands:

```shell
git clone https://github.com/ccleberg/nba
cd nba
pipx install .
```

![Installation](https://github.com/ccleberg/nba/blob/main/screenshots/installation.png?raw=true)

# Usage

[Back to top](#table-of-contents)

All commands can be passed to the program with the following template:
`nba <ARGUMENT>`

| Argument      | Shortcut | Description                       |
|---------------|----------|-----------------------------------|
| `--scores`    | `-sc`    | Show today's scoreboard           |
| `--standings` | `-st`    | Show current conference standings |

Scores:
![Scores](https://github.com/ccleberg/nba/blob/main/screenshots/scores.png?raw=true)

Standings:
![Standings](https://github.com/ccleberg/nba/blob/main/screenshots/standings.png?raw=true)

# Contributing

[Back to top](#table-of-contents)

Any and all contributions are welcome. Feel free to fork the project,
add features, and submit a pull request.
ingot-ppm 1.1.6
A faster and more comprehensive Python package manager
Requires Python: >=3.7
Keywords: dependency management, package manager, python

# PPM - A faster and more comprehensive Python package manager

一个更快速、更全面的Python包管理器，默认使用清华PyPI镜像源。

**版本**: 1.1
**作者**: IngotStudio

## 特点

- 使用异步IO实现快速下载
- 默认使用清华PyPI镜像源
- 美观的进度条显示
- 简单直观的命令行接口
- 自动更新功能

## 安装

```bash
pip install ppm
```

### 配置镜像源

PPM默认使用清华PyPI镜像源，无需额外配置。如需手动配置，可使用以下命令：

```bash
ppm config set mirror https://pypi.tuna.tsinghua.edu.cn/simple
```

## 使用方法

### 安装包

```bash
# 安装最新版本
ppm add package_name

# 安装指定版本
ppm add package_name -v=1.0.0

# 安装开发依赖
ppm add package_name --dev

# 从requirements.txt安装
ppm add -r requirements.txt
```

### 包管理

```bash
# 列出已安装的包
ppm list

# 查看包信息（版本、作者、描述等）
ppm info package_name

# 卸载包
ppm remove package_name
```

### 更新

```bash
# 更新PPM和所有过期的包
ppm update
```

## 特色功能

- 自动检测并更新过期的包
- 智能版本管理
- 详细的包信息展示
- 安全的包卸载机制

## 开发

1. 克隆仓库
2. 安装依赖: `pip install -e .`
3. 运行测试: `pytest`

## 许可证

MIT