semvec/pypi-packages huggingface dataset:
greedos 0.4.0
Advanced Cybersecurity Red Team Tool
Requires Python: >=3.7


   
  
   ~ GreeDoS a Cybersecurity Red Team tool based on DDoS ~
  
## Description

- **Advanced Cybersecurity Red Team Tool**
- **Test Network Resilience:** Simulate multi-vector DDoS attacks to evaluate how well network defenses hold up under stress as penetration testing purpose.
- **Validate Security Controls:** Assess the effectiveness of firewalls, intrusion prevention systems (IPS), and DDoS mitigation solutions in real-world scenarios.
- **Conduct Red Team Exercises:** Use the tool during authorized penetration tests to mimic attacker behavior and identify weaknesses in infrastructure.
- **Improve Incident Response:** Train SOC teams to detect, analyze, and respond to DDoS attacks by running controlled attack simulations.
- **Optimize Network Performance:** Identify bottlenecks and failure points under high traffic loads, enabling proactive network tuning.
- **Automated Testing:** Cybersecurity professionals can automate DDoS simulation tests within security testing suites, improving red teaming efficiency.
- **Rapid Deployment:** Network engineers can deploy the tool across environments for stress testing network resilience without complex setup.

  📫 How to reach me: **http://mrechofi.github.io/Tanjib_portfolio_website/** & **tanjibisham777@gmail.com & tanjibisham888@gmail.com**


# Installation Process via 'git' :
    git clone https://github.com/MrEchoFi/GreeDoS_V2.git

    cd GreeDoS_V2

    // install external libraries, 

    pip install requests scapy rich

    or,

    pip install -r requirements.txt

    // If you want to ensure everything is up-to-date, you can use:

     pip install --upgrade requests scapy rich

    // then run the tool,
   
    python3 GreeDoS_ii.py

Note: Install Npcap externally.

# Install Tcpreplay for high-speed packet replay:
   sudo apt install tcpreplay
   

# Recommendations:
   
        - you can run this tool in Ubuntu, kali linux, WSL, Debian & Arch, Parrot OS.
        - Use good level of router & ethernet cable.
        - If u want to run this tool aggressively for testing or lab based attacks with more time & thread then use high CPU, GPU and RAM based device.
pangea-azure-ai 0.2.0
A wrapper around the Azure AI Inference SDK
Requires Python: >=3.10

# Pangea + Azure AI Inference SDK

A wrapper around the Azure AI Inference SDK that wraps the chat completion API
with Pangea AI Guard. Supports Python v3.10 and greater.

## Installation

```bash
pip install -U pangea-azure-ai
```

## Usage

```python
import os

from azure.ai.inference.models import SystemMessage, UserMessage
from azure.core.credentials import AzureKeyCredential

from pangea_azure_ai.inference import PangeaChatCompletionsClient

endpoint = "https://[...].cognitiveservices.azure.com/openai/deployments/gpt-4o-mini"
model_name = "gpt-4o-mini"

client = PangeaChatCompletionsClient(
    endpoint=endpoint,
    credential=AzureKeyCredential(os.getenv("AZURE_API_KEY", "")),
    pangea_api_key=os.getenv("PANGEA_API_KEY", ""),
)

response = client.complete(
    messages=[
        SystemMessage(content="You are a helpful assistant."),
        UserMessage(content="I am going to Paris, what should I see?"),
    ],
    max_tokens=4096,
    temperature=1.0,
    top_p=1.0,
    model=model_name,
    stream=False,
)

print(response.choices[0].message.content)
```
psr-factory 5.0.0b52
PSR database management module.
Requires Python: >=3.9
Keywords: psr, factory, sddp

PSR Factory (version 4.0.40)
============================

Factory is a library that helps to manage SDDP cases. 
It contains functions that create, load, and save studies, and also functions that create,
access, and modify objects in a study.


Installation
------------

### System-wide installation

Open the command prompt and run the following command:

```bash
pip install psr_factory-4.0.40-py3-none-win_amd64.whl
```

Factory will be available to all Python scripts in your system after importing it:

```python
import psr.factory
```

### Local/project-specific usage

Copy the folder `psr` and its contents to your project folder or a specific folder (e.g., `C:\path\to\factory`). Then, in your Python script, add the following lines:

```python
import sys
sys.path.append(r"C:\path\to\factory")
import psr.factory
```


Usage sample
------------

```python
import psr.factory

study = psr.factory.load_study(r"C:\temp\my\study")
system_1 = study.find("System.*")[0]

battery = psr.factory.create("Battery")
battery.code = 1
battery.name = "Battery 1"
battery.set("InstalledCapacity", 10.0)
battery.set("RefSystem", system_1)
study.add(battery)

study.save(r"C:\temp\my\updated_study")
```


Full documentation
------------------

The full documentation and reference is available at [https://docs.psr-inc.com/factory/](https://docs.psr-inc.com/manual/factory/).

Releases
--------

New releases can be found in the release notes at [https://psrenergy-docs.github.io/factory/releases.html](https://psrenergy-docs.github.io/factory/releases.html).
redaction 0.1.6
Simple redaction script
Requires Python: >=3.12
Keywords: pii, redaction, presidio, privacy, text

# 🕵️ PII Redaction Tool

Wipes out personal info from text files using **[Microsoft Presidio](https://github.com/microsoft/presidio)**.

Built for devs who want something lightweight, fast, and Torch-free — a no-drama way to clean text.

## 🚀 Installation

```bash
pip install redaction
````

## 🧹 Usage

```bash
redact file.txt
redact file.txt -o output.txt
redact --help
```

* By default, the redacted version lands in the same directory as your input file.
* Use the `-o` flag to specify your own output file.

## 💡 Example

**Input:**

```
My name is John Doe and my email is john@example.com.
```

**Output:**

```
My name is <PERSON> and my email is <EMAIL_ADDRESS>.
```

## 🧠 Tech Notes

* Built on top of Microsoft Presidio's Analyzer + Anonymizer.
* No GPU, no Torch — just clean text ops.
* Designed for quick local use or integration in your data pipeline.

**GitHub:** [https://github.com/tdiprima/redaction](https://github.com/tdiprima/redaction)

---

## ⚠️ Heads up:
No PII tool is perfect — always sanity-check the output before using it in production.