semvec/pypi-packages huggingface dataset:
vg_management 0.1.0.post3
The Python script & library for managing web service stack
Requires Python: >=3.10

# VG_MANAGEMENT - The Python script & library for managing web service stack


VG_MANAGEMENT is a python library and also collection of scripts. It helps automate the basic tasks when you're working with web service stack or provide a python library to integrate with other system and flow. Currently, the library only works with the LAMP stack.

<!--This is one of the tool we built to help you manage your web service stack. You can find more information at the [documentation site](DOCUMENTATION_LINK)-->

VG_MANAGEMENT is maintained and published by [VNGuru](https://vnguru.com/).

---

## Notices

**Python >=3.10 is required for this project**.

---

## Getting Started

Assuming you have a supported version of Python installed, we recommended using python virtual environment:

```bash
python -m venv .venv
source .venv/bin/activate
```

Then, you can install vg_management from PyPI with:

```bash
python -m pip install vg_management
```

## Using VG_MANAGEMENT
After installing vg_management package, you can use it as a script or import it as a library in your python code.

### As a Script
You can run the script directly from the command line, here is the list of commands:

```bash
vg_create_vhost --help
vg_remove_vhost --help
```

### As a Library
You can import the library in your python code:

```python
from vg_management.vg_lib.raw.vhost.config import WebConfiguration
from vg_management.vg_exec.create_vhost import *
import sys
# Use the library functions
config = WebConfiguration()
parsed_config = parse_command_line(config)
new_user(parsed_config, messages_file=sys.stdout))
```
ethical-gardeners 0.0.1
A RL environment for learning ethically-aligned behaviours
Requires Python: >=3.9
Keywords: Gym, Gymnasium, Machine Ethics, Multi-Agent System, OpenAI Gym, Reinforcement Learning

# EthicalGardeners

**EthicalGardeners** is a [PettingZoo](https://pettingzoo.farama.org/)
multi-agent environment for simulating gardeners tending to a grid-world
garden, including ethical considerations.

The goal is to make agents learn an ethically-aligned behaviour that includes
and respects these considerations.


## How to

### Run

To launch the simulation with default settings, use the following command in a shell, with the current
working directory being the project root:

```sh
python ethicalgardeners/main.py --config-name config
```

### Run tests

Tests must be placed in the `tests/` folder; files must follow the `test_*.py`
naming convention to be detected by unittest.

To run the tests, use the following command in a shell, with the current
working directory being the project root:

```sh
python -m unittest tests/test_*.py
```

### Build the docs

Documentation can be found in the `docs/` folder, and is built using Sphinx.
A quick command to build the documentation in the HTML format is:

```sh
cd docs/
make html
```

Then, open the `build/html/index.html` file in your favorite browser to read 
the rendered docs.

Documentation has its own requirements, which can be installed with

```sh
pip install -r docs/requirements.txt
```

See the [documentation](https://ethicsai.github.io/ethical-gardeners/main/index.html) for more details.
acparser 1.0.0
Ansible collection parser for developers.

A tool to parse an ansible collection.

## Setup

```
python3 -m pip install acparser
```

## Usage

```
acparser --tarfile ~/Downloads/ansible-posix-2.0.0.tar.gz
```

## Usge as a Python module

```Python
import acparser
result = acparser.process_collection("ansible", "posix", "2.0.0", "/home/adas/Downloads/ansible-posix-2.0.0.tar.gz")
```

## License

GNU General Public License v3.0 or later

## Copyright notice

Copyright (C) 2025  Anwesha Das
pybotchi-cli 0.1.0
PyBotchi implementation for executing cli commands
Requires Python: <4.0,>=3.13.0

# PyBotchi Files

Dedicated agents for cli commands execution.

## Feel free to override/extend it's functionality to cater your requirements.

# Example Usage

- [Execute Bash](https://github.com/amadolid/pybotchi/blob/master/agents/pybotchi-cli/example_usage.py)

## `python3 example_usage.py "list files"`

```
Logging: Listing files in the current directory
total 248
drwxr-xr-x 4 root root   4096 Sep  8 14:06 .
drwxr-xr-x 4 root root   4096 Sep  8 12:40 ..
-rw-r--r-- 1 root root    202 Sep  8 13:41 .flake8
drwxr-xr-x 3 root root   4096 Sep  8 13:56 .mypy_cache
-rw-r--r-- 1 root root    297 Sep  8 14:07 README.md
-rw-r--r-- 1 root root   2243 Sep  8 14:06 example_usage.py
-rw-r--r-- 1 root root     28 Sep  8 14:06 mypy.ini
-rw-r--r-- 1 root root 211899 Sep  8 12:43 poetry.lock
drwxr-xr-x 3 root root   4096 Sep  8 13:51 pybotchi_cli
-rw-r--r-- 1 root root    811 Sep  8 12:42 pyproject.toml
Logging: Listing complete
```

## `python3 example_usage.py "remove README.md"`

```
Here's the script I will run:
---
echo "[INFO] Attempting to remove README.md file..."
if [ -f "README.md" ]; then
  rm README.md
  echo "[SUCCESS] README.md has been deleted."
else
  echo "[ERROR] README.md file does not exist."
fi
---

If you want to proceed, please reply with "I know what I am doing" exactly:
```

- > I know what I am doing

```
[INFO] Attempting to remove README.md file...
[SUCCESS] README.md has been deleted.
```

- > anything else

```
Cancelled!
```
crawler-byte 0.0.2
A python package that crawls a directory recursively and returns the file names and file stats as well as execute a custom function passed as an argument.
Requires Python: >=3.8

# 🚀 CrawlerByte

**Crawler Byte** is a Python package for recursively crawling directories, collecting file stats, and executing custom actions on each file.  
It’s perfect for building file search tools, batch processors, or custom directory explorers.

---

## 📦 Installation

```bash
$ pip install crawlerbyte
```




## Usage
crawl() takes three arguments
=> path (str), max_depth (int), function (callable)

``` python
from crawlerbyte.crawler import crawl
import os

# Set your target directory
base_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'data')

def main():
    # Crawl the directory and run a custom action on each file
    result = crawl(base_dir, max_depth=1000, action=lambda f: f)
    print("Crawled Data:", result)

if __name__ == "__main__":
    main()
```