semvec/pypi-packages huggingface dataset:
opensarlab-lib 0.0.14
A Python library to support ASF OpenSARlab's Jupyter Notebook repository https://github.com/ASFOpenSARlab/opensarlab-notebooks
Requires Python: >=3.9

# The future home of opensarlab-lib

[![PyPI license](https://img.shields.io/pypi/l/opensarlab_lib.svg)](https://pypi.python.org/pypi/opensarlab_lib/)
[![PyPI pyversions](https://img.shields.io/pypi/pyversions/opensarlab_lib.svg)](https://pypi.python.org/pypi/opensarlab_lib/)
[![PyPI version](https://img.shields.io/pypi/v/opensarlab_lib.svg)](https://pypi.python.org/pypi/opensarlab_lib/)

[comment]: <> ([![Conda version]&#40;https://img.shields.io/conda/vn/conda-forge/opensarlab_lib&#41;]&#40;https://anaconda.org/conda-forge/opensarlab_lib&#41;)

[comment]: <> ([![Conda platforms]&#40;https://img.shields.io/conda/pn/conda-forge/opensarlab_lib&#41;]&#40;https://anaconda.org/conda-forge/opensarlab_lib&#41;)

A python library to support [ASF OpenSARlab's Jupyter Notebook repository](https://github.com/ASFOpenSARlab/opensarlab-notebooks)

## Install

In order to easily manage dependencies, we recommend using dedicated project environments with
[Anaconda/Miniconda](https://docs.conda.io/projects/conda/en/latest/user-guide/install/index.html). or
[Python virtual environments](https://docs.python.org/3/tutorial/venv.html). 

The opensarlab_lib can be installed with 

[comment]: <> (into a conda environment with)

[comment]: <> (```)

[comment]: <> (conda install -c conda-forge opensarlab_lib)

[comment]: <> (```)

[comment]: <> (or into a virtual environment with)

```
python -m pip install opensarlab_lib
```

## Contact Us

Found a bug? Want to request a feature?
[open an issue](https://github.com/ASFOpenSARlab/opensarlab-lib/issues/new)
health-mcp-server 0.1.2
MCP server for accessing Azure Cloud Health information.
Requires Python: >=3.8

# Health MCP Server

This project provides an MCP server for accessing Azure Cloud Health information using custom health tools.

## Prerequisites

- Python 3.8 or higher
- pip (Python package manager)
- Azure credentials set up for `DefaultAzureCredential` (e.g., via `az login` or environment variables)
- The following Python packages:
  - `requests`
  - `azure-identity`
  - `mcp` (ensure this is installed or available in your environment)


## Installation (from PyPI)

To install the package from PyPI:

```pwsh
pip install health-mcp-server
```

This will install all required dependencies.

## Running the MCP Server (after installation)

After installing, you can run the MCP server with:

```pwsh
python -m health_tools.HealthToolsMcpServer
```

Or, if you have set up a console script entry point, simply:

```pwsh
health-mcp-server
```

**Note:** Ensure your Azure credentials are set up (e.g., run `az login` or set environment variables for a service principal) before starting the server.

## Installation (from source)

1. Clone this repository.
2. Install dependencies:

   ```pwsh
   pip install requests azure-identity
   ```

   If `mcp` is not available on PyPI, ensure it is installed or accessible in your environment.

## Running the MCP Server

From the root directory, run:

```pwsh
mcp run src/HealthToolsMcpServer.py
```

The server will start and register the health tools.

### Use VS Code
Add the MCP server to VS code.

## Usage

The server exposes two main tools:

- **GetEntityHealthPy**: Get the health of an entity.
- **GetHealthModelPy**: Get the health model.

Refer to the code in `src/HealthToolsMcpServer.py` for details on tool parameters.

### Prompts
Typical prompts:

"Give me the health of the health model `{health_model_name}` in subscription `{subscription_id}` and resource group `{resource_group}`".

Once you have setup the context - health model, subscription id and resource group - you can query freely. For example:

* "Summarize the health for my health model"
* "Do you see any patterns in the health model"
* "Give me more details on the health of `{entity_name}`"

## Logging

Logs are written to `c:\temp\McpServerPy.txt`.

## Troubleshooting

- Ensure your Azure credentials are correctly configured.
- Check the log file for error details if something goes wrong.
scanux 1.1.30
System security and performance scanner
Requires Python: >=3.8

# Scanux

A system security and performance scanner that helps monitor and analyze system health, security, and performance metrics.

## System Requirements

Before installing scanux, ensure you have the following system dependencies installed:

- nmap (Network Mapper)
  - Ubuntu/Debian: `sudo apt-get install nmap`
  - CentOS/RHEL: `sudo yum install nmap`
  - macOS: `brew install nmap`

## Features

- System metrics monitoring (CPU, memory, disk usage)
- Security scanning
- Performance analysis
- Network monitoring
- Multiple output formats (text, JSON, YAML)

## Installation

1. Install system dependencies (see above)
2. Install scanux:
   ```bash
   pip install scanux
   ```

## Usage

```bash
# Run a full system scan
scanux

# Show only issues
scanux --issues-only

# Output in JSON format
scanux --json

# Output in YAML format
scanux --yaml

# Run specific modules
scanux --modules system security
```

## Requirements

- Python 3.8 or higher
- psutil
- python-nmap
- netifaces
- rich
- pyyaml

## License

MIT
llm-cache-plugin 0.1
Check whether you've already run this prompt before calling the LLM
Requires Python: >=3.9

# llm-cache

[![PyPI](https://img.shields.io/pypi/v/llm-cache-plugin.svg)](https://pypi.org/project/llm-cache-plugin/)
[![Changelog](https://img.shields.io/github/v/release/kevinschaul/llm-cache-plugin?include_prereleases&label=changelog)](https://github.com/kevinschaul/llm-cache-plugin/releases)
[![Tests](https://github.com/kevinschaul/llm-cache-plugin/actions/workflows/test.yml/badge.svg)](https://github.com/kevinschaul/llm-cache-plugin/actions/workflows/test.yml)
[![License](https://img.shields.io/badge/license-Apache%202.0-blue.svg)](https://github.com/kevinschaul/llm-cache-plugin/blob/main/LICENSE)

Check whether you've already run this prompt before calling the LLM

## Installation

Install this plugin in the same environment as [LLM](https://llm.datasette.io/).

```bash
llm install llm-cache-plugin
```

## Usage

This plugin add a `--cache` flag to `llm prompt`.

If `--cache` is specified, the plugin will search your llm database for a previously-saved response for this prompt-system-model combination. If it is found, it returns that text, saving you time and money. If not, it passes off to the regular `llm prompt` command.

```bash
llm 'How many rs are in strawberry? Think very hard.'
> There are 2 'R's in strawberry
```

Took 3.01s user 1.45s system 104% cpu 4.280 total

```bash
llm --cache 'How many rs are in strawberry? Think very hard.'
> There are 2 'R's in strawberry
```

Took 2.83s user 1.50s system 152% cpu 2.836 total

## Development

To set up this plugin locally, first checkout the code. Then create a new virtual environment:

```bash
cd llm-cache
python -m venv venv
source venv/bin/activate
```

Now install the dependencies and test dependencies:

```bash
llm install -e '.[test]'
```

To run the tests:

```bash
pytest
```