semvec/pypi-packages huggingface dataset:
shadowbot 0.1.0
A Python package for scientific management of RPA processes.
Requires Python: >=3.8

# RPA Assistant

RPA Assistant Client Configuration Tool, integrated with Streamlinet Server, provides a complete set of RPA management service applications.

## Installation

You can install this package using pip:
```cmd
pip install shadow_bot
```

## Usage

```python
import xbot
from .import package
from shadow_bot import SBA, service_init

try:
    sba = SBA(xbot, package)
    service_init(sba, init=True)
except Exception as e:
    pytest.fail(f"shadow_bot raised an exception: {e}")
```
audidata 0.0.5
Audio dataloaders
Requires Python: >=3.5

# Audidata

Audidata is a toolkit that allows users to easily load audio datasets. Audidata supports music, audio, and speech datasets. Audidata also provides samplers, tokenizers, and transforms. Users can also create their own datasets, samplers, and transforms based on Audidata.

## 0. Installation

### Method 1.

```bash
pip install audidata
````

### Method 1. Download source code

```bash
git clone https://github.com/AudioFans/audidata
cd audidata
export PYTHONPATH=`pwd`  # Export environment path so that users can import audidata anywhere in the terminal.
```

## 1. Example

Users must download the dataset manually. The datsets should be structured described in dataset files. Here is an example of loading GTZAN dataset.

```python
from audidata.datasets import GTZAN

root = "/datasets/gtzan"
dataset = GTZAN(root=root, split="train", test_fold=0, sr=16000)
print(dataset[0])
```

Output:

<pre>
{'audio_path': '/datasets/gtzan/genres/blues/blues.00010.au', 
'audio': array([[ 0.11234417,  0.13617763,  0.10609552, ..., -0.06634186, -0.07007345, -0.07359146]], dtype=float32), 
'target': array([1., 0., 0., 0., 0., 0., 0., 0., 0., 0.], dtype=float32), 
'label': 'blues'}
</pre>

For more examples please see [audidata/examples](https://github.com/AudioFans/audidata/tree/main/examples). For example, users can run the following example script to concatenate multiple datasets:

```python
python examples/test_concat_datasets.py
```

Output:

<pre>
0 dict_keys(['dataset_name', 'audio_path', 'bass', 'drums', 'other', 'vocals', 'accompaniment', 'mixture'])
1 dict_keys(['dataset_name', 'audio_path', 'audio', 'target', 'label'])
2 dict_keys(['dataset_name', 'audio_path', 'bass', 'drums', 'other', 'vocals', 'accompaniment', 'mixture'])
3 dict_keys(['dataset_name', 'audio_path', 'audio', 'target', 'label'])
</pre>

## Repo structure
<pre>
audidata
├── audidata
│   ├── collate
│   │   ├── base.py
│   │   └── ...
│   ├── datasets
│   │   ├── gtzan.py
│   │   └── ...
│   ├── io
│   │   ├── audio.py
│   │   └── ...
│   ├── samplers
│   │   ├── multi_datasets.py
│   │   └── ...
│   ├── tokenizers
│   │   ├── base.py
│   │   └── ...
│   ├── transforms
│   │   ├── midi.py
│   │   └── ...
│   └── utils.py
├── examples
│   ├── test_concat_datasets.py
│   └── ...
├── LICENSE
├── README.md
└── pyproject.toml

</pre>

## License

MIT
pycommentcleaner 0.1.2
A simple and fast Python utility to remove comments from Python scripts.
Requires Python: >=3.6
Keywords: python, comments, cleaner, utility, development

# PyCommentCleaner

A simple and fast Python utility to remove comments from Python scripts.

## Features

- Removes inline and standalone comments from Python files
- Preserves comments inside strings and docstrings
- Provides both command-line interface and Python API
- Maintains original formatting and whitespace
- Handles complex Python syntax elements correctly

## Installation

```bash
pip install pycommentcleaner
```

## Usage

### Command Line Interface

```bash
# Clean a single file
pycommentcleaner path/to/your_script.py

# Clean multiple files
pycommentcleaner path/to/file1.py path/to/file2.py

# Specify an output directory
pycommentcleaner path/to/file.py --output-dir path/to/output

# Increase verbosity
pycommentcleaner path/to/file.py -v     # Warning level
pycommentcleaner path/to/file.py -vv    # Info level
pycommentcleaner path/to/file.py -vvv   # Debug level

# Show help
pycommentcleaner --help
```

### Python API

```python
# Clean a file
from pycommentcleaner import clean_file

success, message = clean_file("path/to/your_script.py")
print(message)

# Clean code directly
from pycommentcleaner import clean_code

code = """
def hello():
    # This is a comment
    print("Hello, world!")  # This is another comment
    
    # This comment will be removed
    string_with_hash = "This # is not a comment"
"""

cleaned_code = clean_code(code)
print(cleaned_code)
```

## Examples

Before:
```python
# This is a comment at the beginning of the file
def hello():
    # This is a function that prints a greeting
    print("Hello, world!")  # This prints a greeting
    
    string_with_hash = "This # is not a comment"
    """This is a docstring
    # This is not a comment because it's in a docstring
    """
```

After:
```python
 
def hello():
     
    print("Hello, world!")   
    
    string_with_hash = "This # is not a comment"
    """This is a docstring
    # This is not a comment because it's in a docstring
    """
```

## Development

### Setup

1. Clone the repository:
   ```bash
   git clone https://github.com/viekayy/pycommentcleaner.git
   cd example
   ```

2. Create a virtual environment:
   ```bash
   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate
   ```

3. Install development dependencies:
   ```bash
   pip install -e ".[dev]"
   ```

### Running Tests

```bash
pytest
```

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.

## Author

- **Viadishwar** - [GitHub](https://github.com/viekayy)
arxiv-paper-mcp 0.1.2
arXiv 논문 데이터를 Claude AI와 연동하는 MCP 서버
Requires Python: >=3.11
Keywords: arxiv, claude, ai, research, papers

# 🧠 arXiv Research Assistant MCP Server

This project is an MCP (Model Context Protocol) server built to interact with the vast arXiv.org paper database.

It allows clients like **Claude AI** to search, explore, and compare arXiv papers efficiently — all through a custom-built, local server. It’s built with **Python** and the **FastMCP** framework, and uses **uv** for lightweight package management.

---
S
## ✨ Features

- **🔍 Keyword-based Paper Search**  
  Search arXiv papers by keywords, with options to sort by relevance or most recent.

- **📚 Latest Papers by Category**  
  Specify an arXiv category code (e.g., `cs.AI`, `math.AP`) to fetch the most recent papers in that field.

- **📄 Paper Details Lookup**  
  Fetch detailed metadata using a paper's arXiv ID: title, authors, abstract, categories, DOI, PDF link, and more.

- **🧑‍🔬 Author-based Paper Search**  
  Retrieve a list of papers published by a specific author.

- **📊 Trend Analysis (Experimental)**  
  Get an overview of trending keywords or topics based on recent papers in a category (currently uses mock data).

- **📝 Summarization Prompt Generator**  
  Dynamically generate prompts that help LLMs summarize a selected paper more effectively.

- **🆚 Comparison Prompt Generator**  
  Provide two paper IDs to generate a structured prompt for comparing their content.

---

## 🛠️ Tech Stack

- Python 3.11+
- [FastMCP](https://github.com/modelcontextprotocol/fastmcp)
- uv (for dependency & environment management)
- requests (for API communication)
- xml.etree.ElementTree (for parsing XML responses)

---

## 🚀 Getting Started

### 1. Installation from PyPI
```bash
pip install arxiv-paper-mcp
# or with uv
uv pip install arxiv-paper-mcp
```
### 🔧 2. Clone the repository (for development)
```bash
git clone https://github.com/daheepk/arxiv-mcp-server.git
cd arxiv-mcp-server
```
### 🔧 3. Install Dependencies (for development)

Use `uv` to install all dependencies in editable mode:

```bash
uv pip install -e .
```

## ⚙️ How to Run

### ▶️ Run the server (locally)

```bash
uv run mcp dev arxiv_mcp/server.py
```

## 🔌 Use with Claude

To use this MCP server with Claude, add the following JSON configuration to Claude's MCP settings:

```json
{
  "arXivPaper": {
    "command": "uv",
    "args": [
      "run",
      "--with",
      "arxiv-paper-mcp>=0.1.0",
      "arxiv-mcp"
    ]
  }
}
```

## Project Structure
```
arxiv-mcp-server/
├── arxiv_mcp/              # Main package
│   ├── __init__.py
│   ├── app.py              # FastMCP app setup
│   ├── server.py           # Server entry point
│   ├── utils.py            # arXiv API communication logic
│   ├── resources/          # MCP resources (categories, authors, etc.)
│   ├── tools/              # MCP tools (search, detail lookup, trends)
│   └── prompts/            # Prompt templates (summarize, compare)
├── pyproject.toml          # Project config & dependencies
└── README.md               # This file
```
ykyos-mcp 0.1.3
MCP Server for URL Processing and Image Extraction
Requires Python: >=3.8

# YkyosMCP

一个用于处理URL和提取图片的模型上下文协议(MCP)服务器。

## 功能

此MCP服务器提供三个主要工具：

1. **fetch_markdown**: 使用Firecrawl API获取URL内容并返回其原始内容
2. **extract_images**: 从markdown内容中提取所有图片URL
3. **download_images**: 下载图片URL列表到指定目录

## 安装

```bash
pip install ykyos-mcp
```

## Usage with Claude Desktop

Add the following configuration to your Claude Desktop settings:

```json
{
  "mcpServers": {
    "YkeyMCP": {
      "isActive": true,
      "command": "uvx",
      "args": [
        "ykyos-mcp"
      ],
      "env": {
        "DOWNLOAD_BASE_PATH": "/path/to/download/directory",
        "FIRECRAWL_API_KEY": "your_firecrawl_api_key"
      }
    }
  }
}
```

Replace `/path/to/download/directory` with the directory where you want to save downloaded images.

## Development

To set up for development:

```bash
# Clone the repository
git clone https://github.com/your-username/ykyos-mcp.git
cd ykyos-mcp

# Install in development mode
pip install -e .
```

## License

MIT License