semvec/pypi-packages huggingface dataset:
anime-quotes 0.1.8
Random anime quotes generator to motivate your burn-out soul
License: MIT
Requires Python: >=3.7
Keywords: quotes, anime, motivation, fun

# anime-quotes

A lightweight and easy-to-use command-line tool to display random anime quotes.

## About

**anime-quotes** is a Python package that allows users to retrieve random quotes from anime characters via the command line.  
It supports filtering by character name and fetches quote data from a public GitHub repository. The quotes are cached locally for offline access after the first run.

## Installation

Install from PyPI:

```bash
pip install anime-quotes
```

## Usage

Displays a random quote:

```bash
anime-quote
```

Displays a quote from the character "Goku":

```bash
anime-quote goku
```

> Note: An internet connection is required on the first run to download quote data.

## Terms of Use

1. This package retrieves quote data from an open GitHub repository.
2. Offline use is supported after initial data retrieval.
3. Do not redistribute the content without appropriate credit.
4. This package is not affiliated with any anime studios or official licensors.

<!-- ## Data Source

Quotes are fetched in real-time from the following GitHub repository:

[miya3333](https://github.com/miya3333/anime-quotes-data)

Contributions to the quote database are welcome.

## License

This project is licensed under the MIT License. -->

## Contributing

To contribute new quotes or improvements, please submit a pull request to the following repository:

[miya3333](https://github.com/miya3333/anime-quotes-data)
wiki-mcp-server 0.0.6
내 MCP 서버
License: MIT
Requires Python: >=3.10

# Wiki-MCP 서버

Wiki 서비스를 위한 MCP(Model Context Protocol) 서버입니다.

## 설치 및 설정

### 사전 요구사항
- Python 3.10 이상
- [uv](https://github.com/astral-sh/uv) 설치
- [fastmcp](https://github.com/jlowin/fastmcp) 설치

### 설치 방법

1. 저장소를 클론합니다:
```
git clone <repository-url>
cd wiki-mcp
```

2. uv를 사용하여 가상 환경을 생성하고 의존성을 설치합니다:
```
uv venv
uv pip install -r requirements.txt
```

개발 의존성도 설치하려면:
```
uv pip install -e ".[dev]"
```

## 환경 변수 설정

`.env` 파일을 루트 디렉토리에 생성하고 필요한 환경 변수를 설정합니다:

```
API_KEY=your_api_key_here
DEBUG=True
```

## MCP 서버 실행 방법

### 개발 모드에서 실행 (권장)
MCP Inspector를 사용하여 서버를 테스트하고 디버그할 수 있습니다:

```
fastmcp dev server.py
```

### Claude Desktop에 설치
서버가 준비되면 Claude Desktop에 설치하여 사용할 수 있습니다:

```
fastmcp install server.py
```

환경 변수 설정과 함께 설치:
```
fastmcp install server.py -e API_KEY=abc123 -e DEBUG=True
```

또는 .env 파일에서 환경 변수 로드:
```
fastmcp install server.py -f .env
```

### 직접 실행
고급 시나리오용:

```
fastmcp run server.py
# 또는
python server.py
```

## MCP 서버 구성하기

서버는 도구(tools), 리소스(resources), 프롬프트(prompts)로 구성됩니다:

### 도구 추가하기
```python
@mcp.tool()
def my_tool(param1: str, param2: int) -> str:
    """도구 설명"""
    return f"결과: {param1}, {param2}"
```

### 리소스 추가하기
```python
@mcp.resource("my-resource://{param}")
def my_resource(param: str) -> str:
    """리소스 설명"""
    return f"리소스 데이터: {param}"
```

### 프롬프트 추가하기
```python
@mcp.prompt()
def my_prompt(query: str) -> str:
    """프롬프트 설명"""
    return f"다음 정보를 처리해주세요: {query}"
```

## 개발

### 코드 포맷팅

```
black .
isort .
```

### 린팅

```
ruff check .
```

### 타입 체크

```
mypy .
```

## 테스트

```
pytest
```
dac 0.4.3
Tool to distribute data as code
License: MIT
Requires Python: <3.14,>=3.9

# `dac`: A CLI Helper Tool for Data as Code

[Data as Code](https://data-as-code.github.io/docs/) (DaC) is a paradigm of distributing versioned data as code.

`dac` is a tool that [supports the Producer](https://data-as-code.github.io/docs/#3-use-the-dac-cli-tool).

**IMPORTANT**: Currently the project is in the initial development phase, this is why releases are marked as `0.z.y`.
(following [semantic versioning 2.0.0](https://semver.org/): "Major version zero (0.y.z) is for initial development.
Anything MAY change at any time. The public API SHOULD NOT be considered stable.").
While in this phase, we will denote breaking changes with a minor increase.

## Quickstart

You can install `dac` as a regular python package

```sh
python -m pip install dac
```

Then use the integrated help to find out its functionalities
```sh
dac --help
```

## Setup development environment (for contributors only)

* Create a virtual environment and activate it

  ```shell
  python -m venv venv
  . venv/bin/activate
  ```

* Install the developer dependencies

  ```shell
  python -m pip install -U pip wheel setuptools
  python -m pip install -r requirements-dev.txt
  ```

* Enable the pre-commits

  ```shell
  pre-commit install
  ```

* To run all the tests

  ```shell
  pytest --run-slow
  ```

  (omit `--run-slow` to run only the fast unit tests)
jax-decision-making 0.0.2
Decision making with Gaussian processes in JAX.
License: MIT
Requires Python: <3.13,>=3.10
Keywords: decision-making, gaussian-processes, jax

# jax-decision-making

[![PyPI - Version](https://img.shields.io/pypi/v/jax-decision-making.svg)](https://pypi.org/project/jax-decision-making)
[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/jax-decision-making.svg)](https://pypi.org/project/jax-decision-making)

`jax-decision-making` offers a collection of algorithms for sequential decision-making problems, including reinforcement learning, planning, and bandit algorithms. The implementations are built upon the JAX library, enabling automatic differentiation, vectorisation, and just-in-time (JIT) compilation to native code for high performance on CPUs, GPUs, and TPUs. This allows for efficient research, development, and deployment of decision-making agents.

-----

## Table of Contents

- [jax-decision-making](#jax-decision-making)
  - [Table of Contents](#table-of-contents)
  - [Installation](#installation)
  - [License](#license)

## Installation

```console
pip install jax-decision-making
```

## License

`jax-decision-making` is distributed under the terms of the [MIT](https://spdx.org/licenses/MIT.html) license.