semvec/pypi-packages huggingface dataset:
invasync 3.0.0
Invaders synchronizer
Requires Python: >=3.9

# InvaSync

Synchronize your [flashed invaders](https://www.space-invaders.com/flashinvaders) to your [MySpaceInvaderMap](https://invaders.code-rhapsodie.com) easily!

## Installation

```sh
uv tool install invasync
```

## Usage

1. Create a `users.json` file:

```json
[
  {
    "name": "YOUR NAME",
    "flash_uid": "YOUR FLASH UID",
    "map_email": "YOUR INVADER MAP EMAIL",
    "map_password": "YOUR INVADER MAP PASSWORD",
    "map_token": "leave empty"
  },
  ...
]
```

2. Run InvaSync:

```sh
invasync -u users.json
```

3. Enjoy!

## Automatic updates

The following code will schedule a cron job running every 10 minutes:

```crontab
*/10 * * * * /home/USER/.local/bin/invasync -u /path/to/user.json
```
kubelingo 0.2.3
CKAD Studying Tool
Requires Python: >=3.8

# Kubelingo

[![CI Status](https://github.com/josephedward/kubelingo/actions/workflows/ci.yml/badge.svg)](https://github.com/josephedward/kubelingo/actions/workflows/ci.yml)


## CKAD Studying Tool




Kubelingo is a command-line interface (CLI) tool designed to help you study for the Certified Kubernetes Application Developer (CKAD) exam. It provides interactive questions and scenarios to test your knowledge of Kubernetes concepts and `kubectl` commands.

## Features

*   **Interactive Quizzes:** Test your knowledge with a variety of Kubernetes-related questions.
*   **Scenario-Based Learning:** Practice common `kubectl` operations in simulated environments.
*   **Comprehensive Coverage:** Questions cover key CKAD exam topics.
*   **User-Friendly Interface:** Simple and intuitive CLI for an efficient study experience.

## Installation

Kubelingo can be installed directly from PyPI using `pip`:

```bash
pip install kubelingo
```

## Usage

Once installed, you can run Kubelingo from your terminal:

```bash
kubelingo
```

## Managing Question Sources

You can manage `source` fields directly via the `kubelingo` CLI using the following options:

### Add sources from a consolidated file
```bash
kubelingo --add-sources --consolidated /path/to/consolidated_questions.yaml
```

### Check for missing sources
```bash
kubelingo --check-sources
```

### Interactively find and assign sources
```bash
kubelingo --interactive-sources
```

### Auto-approve first search result
```bash
kubelingo --interactive-sources --auto-approve
```

Follow the on-screen prompts to navigate through the questions and scenarios.

## Contributing

Contributions are welcome! If you have suggestions for new questions, features, or improvements, please feel free to open an issue or submit a pull request on the [GitHub repository](https://github.com/josephedward/kubelingo).

## License

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

[![asciicast](https://asciinema.org/a/HUZGlo91A8SfUPplgiPeQUDxJ.svg)](https://asciinema.org/a/HUZGlo91A8SfUPplgiPeQUDxJ)
dvara-llm-tool 1.0.0
A Minimal MCP server exposing LLM tools
Requires Python: >=3.8

# Dvara LLM Tool

A Minimal MCP server exposing LLM tools.

## Install

```bash
pip install build
pip install -e .
```

## CLI

```bash
dvara-llm-tool
```

## Using this distribution

- Install from source (editable):
  ```bash
  pip install -e .
  ```
- Or install from a built wheel:
  ```bash
  pip install dist/*.whl
  ```

### As a dependency in another project

Add to your project's requirements, then invoke the CLI installed by the package:
```text
dvara-llm-tool>=1.0.0
```
Configure your MCP client to launch `dvara-llm-tool` via stdio. No Python import is required for client usage.

### Environment

Set at least one provider key supported by LiteLLM (example shows OpenAI):
```bash
export OPENAI_API_KEY=sk-...               # required for model calls
# Optional Langfuse (for observability)
export LANGFUSE_HOST=https://cloud.langfuse.com
export LANGFUSE_PUBLIC_KEY=pk_...
export LANGFUSE_SECRET_KEY=sk_...
```

### Run the MCP server

```bash
dvara-llm-tool  # runs an MCP stdio server
```

Use with any MCP-compatible client by configuring a stdio tool pointing to `dvara-llm-tool`.

### Available tools (high level)

- `llm_completion(system_prompt, user_input, model?, customer_id?) -> str`
- `llm_vision(system_prompt, image, user_text?, model?, customer_id?) -> str`
- `llm_structured(system_prompt, user_input, schema, model?, customer_id?) -> JSON`
- `prompt_fetch(name, version?) -> Dict`

### Run tests

```bash
pytest -q
```