semvec/pypi-packages huggingface dataset:
vsm-rootkit-detector 0.1.1
VSM Rootkit Detection Tool (Educational Linux Security Project)
License: MIT
Requires Python: >=3.8

# VSM Rootkit Detector

VSM Rootkit Detector is an educational Linux security tool that demonstrates
basic rootkit detection techniques using cross-view process analysis.

## Features
- Cross-view process detection (/proc, ps, psutil)
- Repeated scans to reduce race-condition false positives
- Root privilege enforcement
- Command-line interface

## Installation
```bash
pip install vsm-rootkit-detector
super-datascope 0.1.1
A universal dataset analysis tool with interactive visualization
License: MIT
Requires Python: >=3.10

# 📊 Super DataScope

通用数据集分析工具，支持 Parquet、SQLite、CSV 等格式。super 系列开源项目之一。

## 快速开始

### 安装

```bash
pip install super-datascope
```

### 使用

```bash
# 启动 Web UI
super-datascope

# 分析指定文件
super-datascope data.parquet

# 分析目录下所有数据文件（深度遍历）
super-datascope ./datasets/

# 分析 SQLite 数据库
super-datascope database.db
```

## 功能特性

- 📤 文件上传分析（开箱即用）
- 💻 命令行快速启动
- 📊 交互式数据可视化（8 种图表类型）
- 🔍 DuckDB SQL 查询
- 📋 JSON 数据深度分析（路径提取、通配符、正则）
- 💾 会话保存与恢复
- ☁️ MinIO 远程存储支持（可选）

## 数据接入方式

| 方式 | 描述 | 复杂度 |
|------|------|--------|
| 📤 文件上传 | Web UI 直接上传 parquet/csv/sqlite | 最简单 |
| 💻 命令行参数 | `super-datascope ./data/` | 简单 |
| ☁️ ObjStorage | 配置 MinIO 等对象存储浏览远程文件 | 高级 |

## 高级配置

### MinIO 远程存储

安装 MinIO 支持：

```bash
pip install super-datascope[minio]
```

创建配置文件 `~/.super-datascope/config.yaml`：

```yaml
storage:
  my_minio:
    type: minio
    connection:
      endpoint: localhost:9000
      access_key: minioadmin
      secret_key: minioadmin
      secure: false
    bucket_name: my-datasets
    bucket_root_path: data/
```

或通过环境变量配置：

```bash
export DATASCOPE_STORAGE_TYPE=minio
export DATASCOPE_STORAGE_ENDPOINT=localhost:9000
export DATASCOPE_STORAGE_ACCESS_KEY=minioadmin
export DATASCOPE_STORAGE_SECRET_KEY=minioadmin
export DATASCOPE_STORAGE_BUCKET=my-datasets
```

## 技术栈

- [Streamlit](https://streamlit.io/) — Web UI 框架
- [DuckDB](https://duckdb.org/) — 数据查询引擎
- [Pandas](https://pandas.pydata.org/) — 数据处理
- [Plotly](https://plotly.com/) — 交互式图表
- [smartlibs](https://github.com/huanghw1989/smartlibs) — 基础工具库

## License

MIT
pyprodrisk 2.0.3
Python interface to Prodrisk
License: MIT
Requires Python: >=3.10

# 2.0.3 (2026-02-06)

- Improve readability of changelog.
- Prepare for support of Python version 3.15 (one dependency package missing update to 3.15).
- Maintenance: remove unused code.

# 2.0.2 (2025-11-18)

- Bug fix regarding start time for step series input.
- Add changelog.

# 2.0.1 (2025-11-18)

- Update Python requirement to minimum version 3.10.
- Clean up README and links.

# 2.0.0 (2025-11-18)

- `new` Add support for Python version 3.13.
- Remove support for Python version 3.9.
- Added possibility of setting license file name.
- Bug fix in topology plot function.
- Fix time zone for step series.
- Prepare for support of Python version 3.14 (one dependency package missing update to 3.14).

# 1.4.0 (2025-05-05)

- `new` Step series functionality.
- `new` Function to plot the model topology.
- `new` Handling of time zone information.
- Add platform dependent default values for path variables.
- Implement formal name change to Prodrisk (previously ProdRisk).
- Set LTM_CALENDAR=TRUE in session constructor (this is basically a requirement by the Prodrisk core).

# 1.3.2 (2024-11-06)

- Add optional package requirements.

# 1.3.1 (2024-11-06)

- Fix missing package requirements.
- Prepare for support of Python version 3.13.

# 1.3.0 (2024-10-15)

- `new` Functions for dump and load of model data to/from yml/h5.
- `new` Add support for Python version 3.12.

# 1.2.0 (2023-10-03)

- `new` First complete version published.
dynatrace-slo-helper 0.0.0
Interactive CLI that guides users to build Dynatrace SLI/SLO JSON payloads
License: MIT
Requires Python: >=3.8

# Dynatrace SLO Helper

This tiny package provides a **command‑line wizard** that walks a user through the
creation of a Dynatrace Service Level Indicator (SLI) and Service Level
Objective (SLO).  The result is a **JSON payload** that can be pasted directly
into Dynatrace (Settings → Service level objectives → *Create SLO manually*)
or sent to the Dynatrace API.

## Features
- Pure‑Python, no third‑party dependencies.
- Interactive, plain‑English prompts – suitable for non‑technical users.
- Generates a ready‑to‑paste JSON payload **and** writes `slo_config.json`.
- Optional `--demo` flag prints a ready‑made example payload.

## Installation
```bash
pip install dynatrace-slo-helper
```

## Usage
```bash
# Interactive wizard (most common)
python -m dynatrace_slo_helper.cli
# or, after installation, the console script:

dynatrace-slo-helper

# Show a static example payload

dynatrace-slo-helper --demo
```

## What the wizard asks
1. Service name & optional description.
2. Which metric to track (latency, error‑rate, availability, or a custom selector).
3. Metric selector (Dynatrace metric expression).
4. Target value (e.g., `≤ 200 ms`).
5. Evaluation window (`7d`, `30d`, …).
6. Optional warning / critical thresholds.

After the last question the tool prints a nicely formatted JSON payload and saves it to `slo_config.json`.

## Publishing to PyPI (for maintainers)
```bash
# Build the distribution
python -m build
# Upload (you need an API token with upload rights)
python -m twine upload dist/*
```

The package is deliberately minimal – you can extend it later with a web front‑end, Docker image, or CI integration.

---
*Built with the help of RedByte (ChatGPT) and OpenClaw.*