semvec/pypi-packages huggingface dataset:
Bjob 1.0.0
工作性价比计算器 - MCP工具
Requires Python: >=3.8

# Bjob - 工作性价比计算器

一个基于MCP的工作性价比计算工具，帮助用户综合评估薪资、工时、通勤等多维度因素。

## 安装

```bash
pip install Bjob
```

## 使用

### 作为MCP工具使用

在Trae中配置：

```json
{
  "mcpServers": {
    "Bjob": {
      "command": "uvx",
      "args": ["Bjob"]
    }
  }
}
```

### 直接运行

```bash
python -m Bjob
```

## 功能

- 计算工作性价比综合评分
- 考虑PPP转换因子
- 包含学历、经验、城市等个人因素
- 提供改进建议

## 支持的参数

- 年薪总包
- 每周工作天数
- 居家办公天数
- 年假天数
- 通勤时间
- 学历、工作经验、城市等级等

## 示例

```python
from Bjob import calculate_job_worth

result = calculate_job_worth(
    annual_salary=300000,
    work_days_per_week=5,
    daily_work_hours=8,
    commute_hours=1.5,
    education="本科",
    city_level="二线城市"
)
print(result)
```
mlvlab 0.2.24
Visual AI Ecosystem with Reinforced Learning Simulations based on Gymnasium - MLVisual®
Requires Python: >=3.10
Keywords: reinforcement-learning, artificial-intelligence, machine-learning, gymnasium, simulation, visualization, ai-training, q-learning, ant-environment, educational, research, robotics, neural-networks, deep-learning, interactive-learning, real-time-visualization, gym-environments, ml-visualization, ai-simulation, learning-algorithms

# MLV-Lab: Visual AI Learning Ecosystem

[![License: MIT](https://img.shields.io/badge/License-MIT-blue.svg)](https://opensource.org/licenses/MIT)
[![Python Version](https://img.shields.io/badge/python-3.10%2B-blue)](https://www.python.org/)

**MLV-Lab** is a pedagogical ecosystem for exploring fundamental Artificial Intelligence concepts through visual and interactive experimentation.

Our philosophy is **"Show, don't tell"**: we move from abstract theory to concrete, visual practice, allowing enthusiasts and developers to train and observe intelligent agents right from the terminal.

---

## 🚀 Full Guide on GitHub

**This README is a lightweight version.**

For the most complete and up-to-date information, including:

* **Detailed installation instructions.**
* **CLI and API usage examples.**
* **Documentation for all available environments.**
* **Guidelines for contributing to the project.**

### 👉 Visit our GitHub repository: [https://github.com/hcosta/mlvlab](https://github.com/hcosta/mlvlab)