semvec/pypi-packages huggingface dataset:
deepstrike-ai 2.2.1
AI-assisted cybersecurity research & authorized penetration-testing framework
Requires Python: >=3.8
Keywords: cybersecurity, security-testing, penetration-testing, ai, research, privacy

# DEEPSTRIKE AI

<p align="center">
  <img src="assets/logo.png" width="160" />
</p>

<p align="center">
  <strong>AI‑Assisted Cybersecurity Research & Authorized Penetration‑Testing Framework</strong><br/>
  Educational • Research • Contract‑Based • Legal‑Only Use
</p>

<p align="center">
  <img src="https://img.shields.io/pypi/v/deepstrike-ai?style=flat-square" />
  <img src="https://img.shields.io/pypi/pyversions/deepstrike-ai?style=flat-square" />
  <img src="https://img.shields.io/pypi/license/deepstrike-ai?style=flat-square" />
</p>

---

## Overview

**DEEPSTRIKE AI** is a modular, AI‑assisted framework designed for **cybersecurity education, defensive research, and authorized penetration testing**. It provides a structured command‑line interface, extensible modules, and optional AI assistance to support **ethical and consent‑based security assessments**.

> ⚠️ **Important Notice**  
> DEEPSTRIKE AI must be used **only** on systems you own or where you have **explicit written authorization**. Any unauthorized use is strictly prohibited.

---

## Key Features

- Interactive command‑line interface with guided workflows
- Multi‑provider AI support (configurable)
- Modular and extensible architecture
- Optional privacy‑aware routing for research
- Cross‑platform support (Linux, macOS, Termux)
- Designed for offline‑friendly operation

---

## Installation

Install from PyPI:

```bash
pip install deepstrike-ai
```

Launch the interactive interface:

```bash
deepstrike menu
```

---

## Basic Usage

```bash
deepstrike --help
```

```bash
deepstrike menu
```

The interactive menu provides access to authorized research workflows and utilities.

---

## Ethics & Legal Use

- Use only on assets you own or are authorized to test
- Follow responsible disclosure practices
- Comply with local and international cybersecurity laws
- Disable modules outside your permitted scope

---

## Support

- 🐞 **Issue Tracker**: GitHub Issues
- 📖 **Documentation**: Project repository
- 📧 **Contact**: hackura@keemail.com

---

## License

MIT License — Commercial use permitted.

---

## Disclaimer

This software is provided for **educational and authorized security research purposes only**. The authors and maintainers assume **no liability** for misuse.

---

**DEEPSTRIKE AI** — Professional tooling for ethical cybersecurity research
olas 0.4.0
Library with wave tools like ESTELA
Requires Python: <3.15,>=3.9
Keywords: waves, xarray, ESTELA

# olas

[![pypi package version](https://img.shields.io/pypi/v/olas.svg)](https://pypi.python.org/pypi/olas)
[![conda-forge version](https://img.shields.io/conda/vn/conda-forge/olas.svg)](https://anaconda.org/conda-forge/olas)
[![python supported shield](https://img.shields.io/pypi/pyversions/olas.svg)](https://pypi.python.org/pypi/olas)

Library with wave tools. At the moment it only includes a prototype of ESTELA.

Documentation: <https://jorgeperezg.github.io/olas>

## Installation

Installation from [PyPI](https://pypi.org/project/olas/) with pip (or similar tools) is straightforward:
```
pip install olas
```

It can also be installed with conda from [conda-forge](https://anaconda.org/conda-forge/olas) ([feedstock](https://github.com/conda-forge/olas-feedstock)):
```
conda install -c conda-forge olas
```

## Basic usage
Calculate and plot ESTELA maps for a location near Santander using the sample netcdf files.

NOTE: a longer period should to be considered to get meaningful results.

Using the command line interface:
```sh
# estela -h to check additional options
estela "./tests/sample_files/test20180101T??.nc" 44 -4 --outdir "."
```

Or python functions:
```python
from olas.estela import calc, plot
estelas = calc("./tests/sample_files/test20180101T??.nc", 44, -4)
plot(estelas, outdir=".")  # for plotting base maps
plot(estelas, gainloss=True, outdir=".")  # for plotting GL maps
```
myodbc-fr8train 1.1.0
Refactoring of typical ODBC handling for myodbcSQL18
Keywords: myodbc, 18, fr8train

# myodbc
### By fr8train-sv

A refactored facade for ODBC connections.

```bash
  pip install myodbc-fr8train
```

Upgrade this specific library by running:

```bash
  pip install --upgrade myodbc-fr8train
```

## Important Commands

The following list of commands are important for project maintenance.

## Building the Library

To build the library, ensure that the necessary build tools are installed in your environment. This can be done by installing `setuptools`, `build`, and `wheel`:

```bash
  pip install build twine setuptools wheel
```

`twine` can also be installed here while we're installing shit since we'll need it later. 

**REMEMBER**: INCREMENT YOUR VERSION NUMBER IN THE TOML BEFORE BUILDING.  

**REMEMBER**: REMOVE THE /DIST DIRECTORY BEFORE BUILDING FOR A CLEAN BUILD

Now, you can create the distribution files (source distribution and wheel) using the following command:

```bash
  python3 -m build
```

This will generate builds in the `dist/` directory.

## Deploying to PyPI

Ensure you have a valid PyPI account and credentials added to your `.pypirc` file or provide them during the publish process. Then, upload your package to PyPI with:

```bash
  python3 -m twine upload dist/*
```

Follow any prompts from `twine` to successfully upload your package. Once deployed, your package will be available on PyPI. 

# Usage

## Initialization

To start using the API SDK, you will need to initialize an API object:

```python
from myodbc.myodbc import MyODBC

my = MyODBC(override=True)
```

This will reach out into your project for a .env file for credentials to establish a connection to the Agilix API Gateway.
Structure your .env file like the example below and maintain it at the root of your project directory.

```dotenv
SQL_HOST=
SQL_PORT=
SQL_USER=
SQL_PASSWORD=
SQL_DATABASE=
```