semvec/pypi-packages huggingface dataset:
iflow-mcp_IHK-ONE-autoir-mcp 0.1.3
AutoIR MCP - Automated Incident Response tools for security analysis
Requires Python: >=3.11.0

 ```
该项目已经集成应急响应的相关的 MCP 工具接口，仅提供 AI 生成的提示词参考。具体根据个人需求更改 @mcp.tool() 提示词。

该项目为 AutoIR 的 FastMCP 版本，可以使用 Vscode / cursor 进行 AI 自动化应急响应，并未进行 Debug，如果有问题可以 Issues 或者联系作者。

配置 config 设置本地雷池地址，配置方法与 AutoIR_Remote 配置相同，详细跳转 [https://github.com/IHK-ONE/AutoIR_Remote](https://github.com/IHK-ONE/AutoIR_Remote)
```

# 功能列表
```plain
# 劫持排查
  1. 排查环境是否被劫持，以及劫持环境变量
  
# 恶意用户排查
  1. 排查 home 下用户
  2. 排查 /etc/passwd 下，拥有 shell 权限、root 权限、特殊权限的用户
  3. 排查 /etc/shadow 下，空口令用户（无密码登录用户）
  4. 排查 sudo 中权限异常用户
  5. 排查 拥有 authorized_keys 免密登录用户

# ProcAnalysis 恶意进程排查
  1. 排查 恶意挖矿脚本
  2. 排查 恶意启动，恶意命令执行的进程
  3. 排查 隐藏pid检
  4. 排查 被恶意替换命令名称的进程
  5. 排查 被恶意 mount 挂载的进程

# NetworkAnalysis 网络排查
  1. 分析网络对外连接
  2. 检测存在的网卡
  3. hosts 排查
  
# FileAnalysis 恶意文件检测
  1. /usr/bin 排查
  2. /tmp 排查
  3. webroot webshell
 
# BackdoorAnalysis 后门排查
  1. LD_PRELOAD后门检测
  2. LD_AOUT_PRELOAD后门检测
  3. LD_ELF_PRELOAD后门检测
  4. LD_LIBRARY_PATH后门检测
  5. ld.so.preload后门检测
  6. PROMPT_COMMAND后门检测
  7. cron后门检测
  8. alias后门
  9. ssh后门 ln -sf /usr/sbin/sshd /tmp/su; /tmp/su -oPort=5555;
  10. SSH Server wrapper 后门，替换/user/sbin/sshd 为脚本文件
  11. /etc/inetd.conf 后门
  12. /etc/xinetd.conf/后门
  13. setuid类后门
  14. /etc/fstab类后门（待写）
  13. 系统启动项后门检测

# LogAnalysis
  1. apache2 日志排查信息统计（并未制作 IIS Nginx Ruoyi 等服务日志审计，一般情况下 应急响应出题使用 Apache 居多）
  2. 登入成功和登入失败信息统计
  
# Rookit 排查
  1. 使用 rkhunter 实现
```

# MCP 导入
```plain
pip install uv
cd ./AutoIR_MCP
uv sync
```

初始化后，可以直接在 Vscode 与 Cursor 中让 AI 加载 MCP

![](https://cdn.nlark.com/yuque/0/2025/png/35229002/1760464295921-21ef314e-a9ca-49cc-b1ff-ee3b7e5798eb.png)

配置完后直接询问即可，尽量使用高参数模型，回答更加精准

测试1

![](https://cdn.nlark.com/yuque/0/2025/png/35229002/1760463837550-179de7da-0429-4a72-9886-8b222a97101f.png)

测试2

![](https://cdn.nlark.com/yuque/0/2025/png/35229002/1760604967548-d636825b-0f10-406f-9ea2-5b3ab86a0050.png)
imagededup-ui 0.1.0
UI for image deduplication
Requires Python: >=3.13

<div align="center">
  <h1>Imagededup UI</h1>
  <p align="center">
    <a href="https://github.com/S1M0N38/imagededup-ui/actions/workflows/code_quality.yml">
      <img alt="Run Tests badge" src="https://img.shields.io/github/actions/workflow/status/S1M0N38/imagededup-ui/code_quality.yml?style=for-the-badge&label=Tests"/>
    </a>
    <a href="https://www.python.org/downloads/">
      <img alt="Python 3.13+" src="https://img.shields.io/badge/python-3.13+-blue.svg?style=for-the-badge&logo=python&logoColor=white"/>
    </a>
    <a href="https://pypi.org/project/imagededup-ui/">
      <img alt="PyPI" src="https://img.shields.io/pypi/v/imagededup-ui?style=for-the-badge&logo=pypi&logoColor=white"/>
    </a>
  </p>
  <div><img src="https://github.com/user-attachments/assets/a3cd3845-0214-4e78-8bbf-67b0f8e1d9eb" alt="imagededup-ui screenshot"></div>
</div>

Assuming that you have [uv](https://docs.astral.sh/uv/) installed, simply run:

```
uvx imagededup-ui [path/to/images/directory]
```

Mark the images as duplicates by clicking on the image thumbnails. In the `path/to/images/directory` will be created a `.imagededup.txt` file with the duplicates with the path of images mark as duplicates.
aibox-data-lake 1.3.0
AiBox Data Lake Toolkit.
Requires Python: >=3.12

<h1 align="center">
  <br>
  <a href="https://aiboxlab.org/en/"><img src="https://aiboxlab.org/img/logo-aibox.png" alt="AiBox Lab" width="200"></a>
  <br>
  aibox-data-lake
  <br>
</h1>

<h4 align="center">AiBox Data Lake Toolkit.</h4>


[![Python](https://img.shields.io/pypi/pyversions/aibox-data-lake.svg)](https://badge.fury.io/py/aibox-data-lake)
[![PyPI](https://badge.fury.io/py/aibox-data-lake.svg)](https://badge.fury.io/py/aibox-data-lake)

# Quickstart

O AiBox Data Lake Toolkit é uma biblioteca enxuta que fornece acesso uniforme a Data Lakes em provedores de nuvem (e.g., GCP). Essa biblioteca foi desenvolvida para uso interno, mas grande parte do código-fonte e dos padrões adotados são comuns e podem ser aplicados a outros contextos.

A biblioteca pode ser instalada usando o seu gerenciador de pacotes preferido:

```sh
uv add aibox-data-lake
uv pip install aibox-data-lake
pip install aibox-data-lake
```

Depois de instalada, a biblioteca pode ser configurada através do `aibox-dl config`. A biblioteca fornece um registro simples de buckets, que associa a URL de um bucket (e.g., `gs://my-bucket`) a um nome (e.g., `bronze`). As credenciais da nuvem devem ser configuradas pelas bibliotecas cliente do provedor (e.g., `google-cloud-storage`, `boto3`). A CLI também oferece outros recursos, como listagem de objetos e leitura de metadados de datasets.

Também é possível utilizar a biblioteca com a URL do bucket diretamente, sem a necessidade de configuração extra.

A principal classe para acesso e manipulação programática do Data Lake é a [aibox.data_lake.Client](./src/aibox/data_lake/client.py). Essa classe fornece métodos para operações comuns no Data Lake, como leitura de arquivos específicos ou carregamento de datasets. Exemplo de uso:

```python
from aibox.data_lake import Client

# Load the configuration and authenticates
#   to the cloud providers.
client = Client()

# List all objects on a bucket
client.list_objects("<bucket-name>")

# Loads a structured data source (e.g.,
#   .parquet, .csv).
ds = client.get_tabular_dataset("<bucket-name>", "<dataset-prefix>")

# A structured data source can be easily
#   loaded to a DataFrame
ds.to_frame()
```
ipyannote 0.4.0
Interactive pyannote widgets
Requires Python: >=3.10

# ipyannote

## Installation

```sh
pip install ipyannote
```

or with [uv](https://github.com/astral-sh/uv):

```sh
uv add ipyannote
```

## Development

We recommend using [uv](https://github.com/astral-sh/uv) for development.
It will automatically manage virtual environments and dependencies for you.

```sh
uv run jupyter lab example.ipynb
```

Alternatively, create and manage your own virtual environment:

```sh
python -m venv .venv
source .venv/bin/activate
pip install -e ".[dev]"
jupyter lab example.ipynb
```

The widget front-end code bundles it's JavaScript dependencies. After setting up Python,
make sure to install these dependencies locally:

```sh
npm install
```

While developing, you can run the following in a separate terminal to automatically
rebuild JavaScript as you make changes:

```sh
npm run dev
```

Open `example.ipynb` in JupyterLab, VS Code, or your favorite editor
to start developing. Changes made in `js/` will be reflected
in the notebook.
kakitori 1.0.2
Audio transcription with speaker diarization using Gemini
Requires Python: >=3.11

# Kakitori

CLI tool for audio recording and transcription with speaker diarization using Google's Gemini Flash.

## Installation

```bash
uv tool install kakitori
```

Requires `mpv` and `pactl`/`ffmpeg` (for recording) installed on your system.

## Configuration

Add your Gemini API key to `~/.config/kakitori/.env`:

```bash
GEMINI_API_KEY=your-api-key-here
```

## Usage

### Record and transcribe

```bash
kakitori record
```

### Transcribe an existing file

```bash
kakitori process recording.mp3
```

### Options

```bash
kakitori process recording.mp3 -o transcript.txt   # save to file
kakitori process recording.mp3 --stdout             # output to stdout
kakitori process recording.mp3 --skip-speaker-id    # skip speaker identification
```

## Output

```
[00:15] John: Hello everyone, welcome to today's meeting.
[00:32] Jane: Thanks for having me. I have some updates to share.
```

## License

MIT