semvec/pypi-packages huggingface dataset:
mcp-ai-jerry 2.0.13
MCP AI Jerry - 智能交互反馈 MCP 服务器，提供 Web UI 和桌面应用双重界面支持。
Requires Python: >=3.11
Keywords: ai, desktop-app, development, feedback, interactive, mcp, web-ui

# MCP AI Jerry

智能交互反馈 MCP 服务器，提供 Web UI 和桌面应用双重界面支持。

## 功能特点

- 🖥️ **双重界面**：桌面应用程序 + Web UI
- 📝 **智能工作流程**：提示词管理、自动定时提交、会话追踪
- 🎨 **现代化体验**：响应式设计、音效通知、多语言支持
- 🖼️ **图片支持**：拖拽上传、剪贴板粘贴
- 🔐 **授权系统**：月卡/年卡激活，设备绑定管理

## 快速开始

### 安装

```bash
pip install uv
```

### 配置 MCP

#### Cursor / Claude Desktop

在你的 AI 助手（如 Claude Desktop、Cursor 等）中添加 MCP 配置：

```json
{
  "mcpServers": {
    "mcp-ai-jerry": {
      "command": "uvx",
      "args": ["mcp-ai-jerry@latest"],
      "timeout": 600,
      "autoApprove": ["interactive_feedback"]
    }
  }
}
```

#### VS Code

在 VS Code 中，按 `Cmd+Shift+P`（Mac）或 `Ctrl+Shift+P`（Windows/Linux），输入 `MCP`???为 `100000000`，以获得更好的体验。

### 桌面应用模式（可选）

如需使用桌面应用模式而非浏览器，添加环境变量：

```json
{
  "servers": {
    "mcp-ai-jerry": {
      "command": "uvx",
      "args": ["mcp-ai-jerry"],
      "timeout": 600,
      "env": {
        "MCP_DESKTOP_MODE": "true"
      },
      "autoApprove": ["interactive_feedback"]
    }
  }
}
```

### 环境变量

| 变量 | 说明 | 默认值 |
|------|------|--------|
| MCP_DEBUG | 调试模式 | false |
| MCP_WEB_HOST | Web UI 主机绑定 | 127.0.0.1 |
| MCP_WEB_PORT | Web UI 端口 | 8765 |
| MCP_DESKTOP_MODE | 桌面应用程序模式 | false |
| MCP_LANGUAGE | 界面语言 (zh-CN/zh-TW/en) | 自动检测 |

## 激活授权

首次使用时需要激活授权。请访问我们的淘宝店铺购买激活码。

- 一个激活码支持 **2 台设备**
- 每 30 天可免费解绑 1 次

## 文档

- [使用指南](docs/使用指南.md) - 详细的安装和使用教程

## 许可证

MIT License
stile 0.0.1
Python Boilerplate contains all the boilerplate you need to create a Python package.
Requires Python: >=3.10

# TMA de-arraying for spatial transcriptomics

STile helps segment tissue microarray (TMA) cores from single-cell spatial transcriptomics data and prepare platform-specific outputs for downstream analysis.

## What it does

- Detects tissue cores by clustering cell centroids with HDBSCAN and refining core labels for Vizgen, Xenium, and CosMx outputs.

## Main entry points

- `stile identify_tissue_cores`: Load a `.h5ad`, cluster cells, assign cores, and save core plots.
- `stile prepare_data`: Convert Vizgen/Xenium/CosMx output directories into an `.h5ad` plus `cell_id`, `x_centroid`, `y_centroid` CSV for further processing.

## Quickstart

```bash
# Build AnnData and centroid CSV from a platform run directory
stile prepare_data xenium path/to/xenium/output path/to/save
stile run
```
GlobleXGPTAi 1.0.1
GlobleXGPTAi Model Python Client (Multi-Provider Support) developed by Himanshu
Requires Python: >=3.6

# GlobleXGPTAi Python Library 🚀

GlobleXGPTAi is an advanced AI model developed by **Himanshu**. This library allows you to easily integrate GlobleXGPTAi into your Python projects.

## Installation 🛠️

```bash
pip install GlobleXGPTAi
```

## Quick Start ⚡

```python
from globlexgptai import GlobleXGPTAiClient

# Initialize the client
client = GlobleXGPTAiClient(api_key="YOUR_API_KEY")

# Generate a response
response = client.generate("Hello GlobleXGPTAi, how are you?")
print(response)
```

## Features ✨

- **Professional Persona**: Built-in GlobleXGPTAi personality.
- **Easy Integration**: Simple class-based structure.
- **Powered by Himanshu**: Optimized for reliability and performance.

## License 📄

MIT License. Developed by Himanshu.
mopipe 0.2.0
MoPipe is a Python package for creating a motion capture data analysis pipeline.
Requires Python: >=3.9

# MoPipe

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

-----

**Table of Contents**

- [MoPipe](#mopipe)
  - [Installation](#installation)
  - [Documentation](#documentation)
  - [Contributing](#contributing)
  - [Development Environmnet](#development-environmnet)
  - [License](#license)

## Installation

```console
pip install mopipe
```

## Documentation

The official documentation is available at [https://au-imclab.github.io/mopipe/](https://au-imclab.github.io/mopipe/).

## Contributing

Contributions are welcome! Please refer to [CONTRIBUTING.md](https://github.com/au-imclab/mopipe/blob/main/CONTRIBUTING.md) for more information.

## Development Environmnet

Please refer to the [Development Environment section of CONTRIBUTING.md](https://github.com/au-imclab/mopipe/blob/main/CONTRIBUTING.md#development-environment)

## License

`mopipe` is distributed under the terms of the [MIT](https://spdx.org/licenses/MIT.html) [license](https://github.com/au-imclab/mopipe/blob/main/LICENSE).
gitthread 0.1.3
Ingest GitHub Issues and Pull Requests into LLM-friendly text dumps
Requires Python: >=3.8

# gitthread

Ingest GitHub Issues and Pull Requests into LLM-friendly text dumps.

**Live Demo:** [gitthread.streamlit.app](https://gitthread.streamlit.app)

![gitthread showcase](image.png)

gitthread is a companion tool to gitingest that focuses on the conversational context of GitHub Issues and Pull Requests.

## Credits

This project is inspired by and uses the library from [gitingest](https://github.com/cyclotruc/gitingest). Special thanks to the gitingest team for their amazing work on codebase ingestion.

## Features

- **Issue & PR Ingestion**: Extract title, body, and all comments from any public GitHub issue or PR.
- **Smart Linking**: Automatically detects and ingests linked issues (e.g., #123 or "fixes #123") in PR descriptions.
- **Repository Context**: Integrates with gitingest to provide a summary or full content of the repository.
- **Concurrent Processing**: Optimized fetching of both thread and repository data.
- **LLM-Friendly**: Outputs clean Markdown formatted for easy consumption by LLMs.
- **CLI & Web**: Use it in your terminal or via a polished Streamlit dark-themed interface.

## Installation

```bash
pip install gitthread
```

## Usage

### CLI

```bash
gitthread https://github.com/user/repo/issues/1
```

### Web Interface

```bash
gitthread-web
```

## Docker

For instructions on how to deploy `gitthread` using Docker or Docker Compose (including Dockge), please see [DOCKER.md](DOCKER.md).

## License

MIT