semvec/pypi-packages huggingface dataset:
voyagermcp 0.2.0
A Voyager-inspired MCP server for local CLI skill library
Requires Python: >=3.10

# Voyager-MCP

A Voyager-inspired MCP server that enables coding agents to build and immediately use CLI tools without restarting sessions.

## Features

- **MCP as CLI wrapper**: simply allows agent to execute CLI commands, so tools are instantly available.
- **Memory**: Tools with `.desc` files get descriptions loaded automatically in MCP tool schema in the next session.
- **Secure Execution**: Uses subprocess with argument lists to prevent shell injection.
- **Configurable**: Tool description can be configured from `~/.voyager/prompt.txt`

## Installation

The script includes embedded dependencies and can be run directly with uv:

```bash
claude mcp add voyager uv run
```

## MIT LICENSE

## Citation
```
@article{wang2023voyager,
  title   = {Voyager: An Open-Ended Embodied Agent with Large Language Models},
  author  = {Guanzhi Wang and Yuqi Xie and Yunfan Jiang and Ajay Mandlekar and Chaowei Xiao and Yuke Zhu and Linxi Fan and Anima Anandkumar},
  year    = {2023},
  journal = {arXiv preprint arXiv: Arxiv-2305.16291}
}
```
rough2fine 0.2.1
通过给定的资料，获得细分场
Requires Python: >=3.10

# rough2fine

## 项目简介

本项目旨在通过给定的资料，自动生成出版级的剧本细分场，辅助编剧高效完成从粗分场到细分场的创作工作。项目基于MCP服务框架，提供结构化的创作提示词生成服务。

## 主要功能
- 解析剧本相关资料（如粗分场、前情、人物小传、故事梗概等）
- 自动生成细分场创作提示词
- 支持通过API调用服务
- 细分场输出严格遵循出版级格式

## 服务接口说明

### 1. `critical_thinking` 工具
- **功能**：根据输入的剧本资料，生成结构化的细分场创作提示词。
- **参数说明**：
  - `fine`：本场剧本粗分场内容，若为空则自动读取`fine.txt`
  - `person`：核心人物小传内容，若为空则自动读取`person.txt`
  - `previous_events`：本场前情内容，若为空则自动读取`previous_events.txt`
  - `screenplay_completed`：已完成剧本内容，若为空则自动读取`screenplay_completed.txt`
  - `story_beat`：本集情节点内容，若为空则自动读取`story_beat.txt`
  - `episode_summary`：本集故事梗概内容，若为空则自动读取`episode_summary.txt`
- **返回值**：
  - `response`：生成的结构化创作提示词（细分场内容）

## 使用方法

1. 安装依赖环境（建议使用 Python 3.8 及以上版本）：
   ```bash
   pip install poetry
   poetry install
   ```
2. 启动服务：
   ```bash
   python server.py
   ```
3. 通过MCP客户端或API调用`critical_thinking`工具，传入相关参数即可获得细分场创作提示词。

## 依赖环境
- Python >= 3.8
- poetry-core >= 2.0.0, <3.0.0

## 文件说明
- `server.py`：服务主程序，提供critical_thinking工具
- `prompt.md`：创作提示词模板及详细说明
- `pyproject.toml`：项目依赖与元数据

## 致谢
感谢所有为本项目提供灵感和支持的编剧、开发者与测试者。
simpledb-wrapper 0.1.0
A simple SQLite wrapper class based on Python sqlite3
Requires Python: >=3.6

# 📚 SimpleDB

`SimpleDB`는 Python `sqlite3`를 기반으로 하는 간단한 SQLite 래퍼 클래스입니다.  
기본적인 테이블 생성, 데이터 삽입, 조회, 갱신, 삭제 등을 쉽게 할 수 있습니다.

---

## 주요 기능

- **SQLite 경량 데이터베이스 처리**
- **테이블 자동 생성**
- **간편한 CRUD 메서드**
- **조건 기반 SELECT/UPDATE/DELETE**
- **단일 컬럼 값 쉽게 찾기 (`find`)**

---

## 사용 예시

```python
from simpledb_wrapper.simpledb import SimpleDB

# 설치
# pip install simpledb-wrapper


# DB 인스턴스 생성 및 연결
db = SimpleDB("example.db")
db.connect()

# 테이블 생성
db.table("users", {
    "id": "INTEGER PRIMARY KEY AUTOINCREMENT",
    "name": "TEXT",
    "age": "INTEGER"
})

# 데이터 삽입
db.insert("users", {"name": "Alice", "age": 25})

# 데이터 조회
results = db.select("users")
print(results)

# 조건부 조회
results = db.select("users", where="age > ?", where_params=(20,))
print(results)

# 데이터 수정
db.update("users", {"age": 30}, where="name = ?", where_params=("Alice",))

# 단일 값 찾기
age = db.find("users", "name", "Alice", "age")
print(f"Alice의 나이: {age}")

# 데이터 삭제
db.delete("users", where="name = ?", where_params=("Alice",))

# 연결 종료
db.close()
```

---

## 메서드 설명

### `connect()`
데이터베이스 파일에 연결합니다.

### `table(table_name, columns)`
테이블을 생성합니다.  
- `table_name`: 테이블 이름  
- `columns`: `{컬럼명: 타입}` 딕셔너리

### `insert(table, data)`
데이터를 삽입합니다.  
- `table`: 테이블 이름  
- `data`: `{컬럼명: 값}` 딕셔너리

### `select(...)`
데이터를 조회합니다.  
- `where`, `order_by`, `limit` 등 옵션 사용 가능

### `update(...)`
데이터를 수정합니다.

### `delete(...)`
데이터를 삭제합니다.

### `find(...)`
조건에 맞는 단일 값을 가져옵니다.

### `close()`
DB 연결을 종료합니다.
ocr_project 0.1.0
Solid figures calculator and OCR utilities.
Requires Python: >=3.7

# OCR Project & Solid Figures Calculator

This project provides two main features:

1. **OCR Utilities**: Extract words and tabular data from images, PDFs, and DOCX files, with optional CSV export.
2. **Solid Figures Calculator**: Calculate volume and surface area for common 3D shapes (cube, sphere, rectangular prism, cylinder).

## Project Structure

- `src/` — All solid figure classes (Cube, Sphere, RectangularPrism, Cylinder)
- `main.py` — Entry point for solid figures calculator
- `requirements.txt` — Python dependencies
- `pyproject.toml` — Build and packaging configuration

## Usage

### Solid Figures Calculator

Run:

python main.py
uv build
```
python main.py
```

This will print example calculations for all supported solid figures.

### OCR Utilities

See previous versions of `main.py` for OCR and CSV extraction logic, or ask for a dedicated script.

## How to Build (for packaging)

- Project uses a `src` layout for modern Python packaging.
- To build:

```
uv build
```

## Requirements

- Python 3.7+
- See `requirements.txt` for dependencies

---

For more features or help, just ask!
xvi-mcp 0.1.3
XVI MCP Server - A Model Context Protocol server for XVI vulnerability platform integration
Requires Python: >=3.8

# XVI MCP Server

A Model Context Protocol (MCP) server for XVI vulnerability platform integration.

## 简介

XVI MCP Server 是一个基于 Model Context Protocol (MCP) 的服务器，用于与 XVI 漏洞平台集成。它提供了一系列工具函数，可以通过 MCP 协议调用 XVI 平台的各种功能。

## 功能特性

- **漏洞查询**: 支持通过关键词、CVE编号、时间范围等条件查询漏洞信息
- **漏洞类型过滤**: 支持按漏洞类型进行筛选
- **风险等级筛选**: 支持按严重程度筛选漏洞
- **结果数量控制**: 可自定义返回结果数量
- **详细漏洞信息**: 返回包括漏洞描述、危害、修复建议等详细信息

## 安装

### 使用 uvx (推荐)

```bash
uvx xvi-mcp
```

### 使用 pip

```bash
pip install xvi-mcp
```

## 使用方法

### 作为 MCP 服务器运行

```bash
# 使用 uvx
uvx xvi-mcp

# 或使用 pip 安装后
xvi-mcp
```

### 在 MCP 客户端中配置

在你的 MCP 客户端配置文件中添加：

```json
{
  "mcpServers": {
    "xvi-mcp": {
      "command": "uvx",
      "args": ["xvi-mcp"]
    }
  }
}
```

## 可用工具函数

### get_vulns

查询漏洞信息的主要函数。

**参数：**
- `keyword` (str, 可选): 模糊查询关键词
- `cve` (str, 可选): CVE编号
- `publishTimeStart` (str, 可选): 起始时间，格式为 yyyy-MM-dd HH:mm:ss
- `publishTimeEnd` (str, 可选): 截止时间，格式为 yyyy-MM-dd HH:mm:ss
- `vulnType` (str, 可选): 漏洞类型
- `riskLevels` (list, 可选): 风险等级列表，可选值："serious", "high_risk", "medium_risk", "low_risk", "unknown"
- `resultSize` (int, 可选): 返回结果数量，范围 1-100，默认为 1

**返回值：**
返回包含漏洞详细信息的字符串，包括：
- 漏洞链接
- 漏洞ID
- 漏洞名称
- 风险等级
- CVE/CNVD/CNNVD编号
- CVSS3评分和字符串
- 漏洞描述
- 漏洞危害
- CWE类型
- 修复建议

## 配置要求

本服务器需要访问 XVI 平台的 API，使用了预配置的客户端凭据。如果需要使用自己的凭据，请修改源代码中的相关配置。

## 开发

### 本地开发安装

```bash
git clone https://github.com/WACHI/xvi-mcp.git
cd xvi-mcp
pip install -e .
```

### 运行测试

```bash
python -m pytest
```

### 构建包

```bash
python -m build
```

### 发布到 PyPI

```bash
twine upload dist/*
```

## 许可证

MIT License

## 贡献

欢迎提交 Issue 和 Pull Request！

## 安全注意事项

- 本工具包含硬编码的 API 凭据，仅用于演示目的
- 在生产环境中使用时，请确保使用环境变量或安全的配置管理方式
- 请遵守 XVI 平台的使用条款和 API 限制

## 联系方式

- 作者: WACHI
- 邮箱: wachi@example.com
- 项目地址: https://github.com/WACHI/xvi-mcp
radiatreepp 0.1.0
Radial dendrogram visualization for hierarchical clustering with feature importance overlays

# 📊 radiatreepp

**RadialTree++** is a Python package for generating **radial dendrograms** from hierarchical clustering output, with rich visual overlays for **SHAP-style feature importances**, **semantic rings**, and **custom annotations**.

---

## 🚀 Features

- 📐 **Radial dendrograms** for hierarchical clustering (via SciPy)
- 🎨 **Gradient edge coloring** (e.g., by average depth or SHAP value)
- 🧠 **Semantic ring overlays** to display feature groups or categories
- 🔤 **Flexible label layout** (radial or horizontal)
- 🔘 **Node highlighting options** (e.g., only inner merges, or top N)
- 🧩 Easy to integrate with any feature importance method (XGBoost, TabNet, etc.)

---

## 📦 Installation

```bash
pip install radiatreepp
```

Or clone locally and install in editable mode:

```bash
git clone git@github.com:es15326/radialtreepp.git
cd radiatreepp
pip install -e .
```

---

## 🧪 Demo Examples

To generate the plots from the included synthetic dataset:

```bash
python -m radiatreepp.examples.xgboost_demo log_feature_importance_synthetic.csv
python -m radiatreepp.examples.tabnet_demo log_feature_importance_synthetic.csv
```

Each command generates:

- `out_figs/radial_dendrogram_<model>.png`
- `out_figs/radial_dendrogram_<model>_legend.png`

---

## 🔧 Customization

You can fully control the look and behavior via the `RadialTreeConfig` class:

```python
from radiatreepp import RadialTreeConfig

config = RadialTreeConfig(
    fontsize=8,
    radial_labels=False,
    label_radius=1.2,
    node_display_mode='inner',         # all, inner, none
    node_label_display_mode='top_3',   # all, inner, top_3, none
    node_size=5,
    node_label_fontsize=7,
    gradient_colors=["black", "blue"], # colormap for edge gradient
    colorlabels={"Category": color_array},  # optional outer rings
)
```

---

## 📁 File Structure

```
radiatreepp/
├── core.py          # Main plotting logic
├── config.py        # Dataclass for RadialTreeConfig
├── utils.py         # Helper for dendrogram computation
├── __init__.py

examples/
├── xgboost_demo.py
├── tabnet_demo.py

log_feature_importance_synthetic.csv   # Safe-to-publish example CSV
```

---

## 📘 Input CSV Format

Your input file should have at least:

- `Feature`: Feature names
- `XGBoost`, `TabNet`, ...: Importance values
- `Category`: For outer ring color labels

Example:

```csv
Feature,Category,XGBoost,TabNet
Feature_0,Category_1,0.134,0.112
Feature_1,Category_2,0.984,0.803
...
```

---

## 🧠 Applications

- Interpreting SHAP values from models like XGBoost, TabNet, LightGBM
- Explaining hierarchical clusters with group semantics
- Publishing visualizations for papers, dashboards, and reports

---

## ✍️ Author

**Elham Soltani Kazemi**  
University of Missouri  
[GitHub Profile](https://github.com/YOUR_USERNAME)

---

## 📜 License

MIT License — free for personal, academic, and commercial use.

---

## ❤️ Acknowledgments

Inspired by SHAP visualization, SciPy’s dendrograms, and feature importance research in explainable AI.