semvec/pypi-packages huggingface dataset:
kwdevtheme 0.1.0
The official Sphinx theme for the kwdev.org project.
Requires Python: >=3.9

# kwdevtheme - kwdev.org Official Theme

`kwdevtheme` is a Sphinx theme developed for the [kwdev.org](https://kwdev.org)
project to use on official webpages, blogs, and documentation

**To Install**

```bash
$ python3 -m pip install kwdevtheme
```

**To Use**

```
# conf.py
html_theme = "kwdevtheme-blue"
```

``kwdevtheme`` includes 9 variants:

- ``kwdevtheme-blue``
- ``kwdevtheme-gray``
- ``kwdevtheme-green``
- ``kwdevtheme-orange``
- ``kwdevtheme-pink``
- ``kwdevtheme-purple``
- ``kwdevtheme-red``
- ``kwdevtheme-teal``
- ``kwdevtheme-yellow``

Please visit (https://kwdevtheme.kwdev.org) for more information.
nonebot-plugin-nodejsphira 0.1.7
Phira Server Manager plugin for NoneBot2
Requires Python: >=3.10

<div align="center">
  <a href="https://v2.nonebot.dev/store"><img src="https://raw.githubusercontent.com/A-kirami/nonebot-plugin-template/resources/nbp_logo.png" width="180" height="180" alt="NoneBotPluginLogo"></a>
  <br>
  <p><img src="https://raw.githubusercontent.com/A-kirami/nonebot-plugin-template/resources/NoneBotPlugin.svg" width="240" alt="NoneBotPluginText"></p>
</div>

<div align="center">

# nonebot-plugin-nodejsphira

✨ **Phira 游戏服务器管理与监控插件** ✨

适用于 NoneBot2 的 Phira Multiplayer (Node.js 版) 后端管理插件。提供实时的房间查询、网页截图监控、服务器协议级连通性检测以及完整的管理员远程控制功能。

## 📖 功能介绍

- **多维度房间查询**：支持纯文本列表展示及高清网页长截图（PC/移动端布局）。
- **连通性检测**：模拟 Phira 客户端进行 TCP 协议级握手测试，获取真实延迟。
- **状态监控**：一键获取 Phira 服务器各节点的运行状态截图。
- **动态鉴权管理**：集成 AES-256-CBC 动态加密算法，安全对接管理接口。
- **全方位行政指令**：支持广播、踢人、强制开始、切换锁定、修改人数上限、关闭房间等。

## 💿 安装方法

推荐使用 `nb-cli` 进行安装：

```bash
nb plugin install nonebot-plugin-nodejsphira
```

并在机器人 `.env` 文件中配置必要项。

> **注意**：网页截图功能依赖 `nonebot-plugin-htmlrender`。安装后请务必执行：
> ```bash
> playwright install chromium
> ```

## ⚙️ 插件配置项

在机器人的 `.env` 文件中添加以下配置：

| 配置项 | 类型 | 默认值 | 说明 |
| :--- | :--- | :--- | :--- |
| `PHIRA_API_URL` | str | `https://phira.chuzoux.top` | Phira WEB服务器基础地址 |
| `PHIRA_STATUS_PAGE_URL` | str | `https://status.dmocken.top/status/phira` | Phira 状态页截图地址 |
| `PHIRA_CHECK_EMAIL` | str | 无 | Phira 检测账号邮箱 (用于 /status) |
| `PHIRA_CHECK_PASSWORD` | str | 无 | Phira 检测账号密码 (用于 /status) |
| `PHIRA_CHECK_SERVER` | str | `mp.phira.cn:12346` | 默认检测的 TCP 服务器地址 |
| `PHIRA_SERVER_NAME` | str | 无 | Phira 服务器名称展示 (用于 /status) |
| `PHIRA_ADMIN_SECRET` | str | 无 | **必填**。需在 [phira-mp-nodejsver](https://github.com/chuzouX/phira-mp-nodejsver) 项目中设置 |
| `SUPERUSERS` | list | `[]` | 机器人管理员列表 (OpenID) |

## 🎮 触发规则

### 基础指令 (所有用户)
- `/room` : 获取服务器房间概览列表 (纯文本)。
- `/proom` : 以移动端尺寸获取房间列表的网页长截图。
- `/status` : 执行 Phira 服务器协议级握手检测 (TCP，返回延迟)。
- `/pstatus` : 获取 Phira 服务器节点运行状态图 (截图)。
- `/ping` : 查看机器人在线状态及当前用户权限。
- `/help` : 显示帮助菜单。

### 管理指令 (仅 SUPERUSERS)
- `/players` : 列出全服所有活跃玩家。
- `/broadcast "内容" [#ID]` : 全服或指定房间广播系统消息。
- `/kick {UID}` : 强制踢出指定 ID 的玩家。
- `/fstart {RID}`, `/lock {RID}`, `/maxp {RID} {人数}`, `/close {RID}` : 房间行政管理。

## 🛠️ 其它用法

### 协议检测原理
`/status` 指令会模拟登录 `phira.5wyxi.com` 获取 Token，随后与目标服务器建立 TCP 连接并发送 `0x01` 握手包。如果收到正确响应，则判定为“在线”并计算往返延迟。

## 📝 许可证
MIT
forestwalker 0.1
A library for walking JSON-like trees, parsing and validating them.
Requires Python: >=3.11

# The Forest Walker

Python modules for parsing JSON, TOML, YAML, and similar formats
produce simple tree structures composed of numbers, strings, Booleans,
arrays, and dictionaries. These structures are then parsed for the second
time to produce more Pythonic data structures. This usually involves
validation of some kind.

We firmly believe that parsing and validation should not be separate
steps: structure of data should have a single source of truth (doing 
otherwise opens door to security issues). Also, existing formal descriptions
of syntax (e.g., JSON schema) are too weak for many purposes and unwieldy
for others.

Enters the ``forestwalker``. A Python module that makes it easy to traverse
trees, parse values while checking types, and report invalid data with
their precise location in the tree.

## License

The Forest Walker was written by Martin Mareš <mj@ucw.cz>.
It can be freely used and distributed under the MIT License.
schema-drift-cli 0.1.0
Detect schema drift in JSON APIs
Requires Python: >=3.9

# Schema Drift Detector - CLI

Schema Drift Detector is a CLI that: 
- Fetches a JSON payload from an HTTP API endpoint (GET for v1).
- Loads a “baseline” schema from a local file (JSON).
- Compares structure: added/removed/changed fields, type changes, optional vs required.
- Outputs the diff in three formats: JSON, Markdown, and a pretty terminal table.

## Language & Stack

I used Python and Click for the CLI as they make argument parsing, subcommands, and help text clean and maintainable.

## Output Examples

### JSON:
Simple: 
```bash 
print(json.dumps(diff, indent=2))
```
Good for machines and CI logs.

### Markdown:
```text
### Added fields

| Path              | Type   |
|-------------------|--------|
| `user.middle_name`| string |
```

### Pretty Terminal Table:
```text
ADDED | user.middle_name | - | string
```

## Installation

Use the package manager [pip](https://pypi.org/project/reorder-editable/) to install reorder-editable.

```bash
pip install -e .
```

## Usage

```bash
python -m schema_drift_cli --help
```

### Commands

- `version` - Shows the CLI version

## Contributing

Pull requests are welcome. For major changes, please open an issue first
to discuss what you would like to change.

Please make sure to update tests as appropriate.

## License

[MIT](https://choosealicense.com/licenses/mit/)
envorce 1.0.0
A tiny package for enforcing the existence of required environment variables.
Requires Python: >=3.11

# Envorce

Mini package for verifying environment variables at runtime to catch misconfigurations as early as possible.

# Configuration

There are a number of different ways in which you can specify the environment variables which need to be "envorced". They can be used individually or simultaneously.

## Via environment variables

Set the environment variable `ENVORCE` as a comma-separated list of required environment variable.

```bash
ENVORCE=DB_USERNAME, DB_PASSWORD, API_URL
```

Then, in your top-level script simply call

```python
from envorce import envorce

envorce()
```

## Via function arguments

Specify required variables by passing them into the top-level function call.

```python
from envorce import envorce

envorce("AWS_ACCESS_KEY_ID", "AWS_SECRET_ACCESS_KEY")
```

## Via configuration file

Add a file `envorce.toml` into your project repository and set the key `envorce` as a list of required environment variables.

```toml
# envorce.toml
envorce = ["DATABASE_HOST", "DATABASE_USERNAME", "DATABASE_PASSWORD"]
```

Then, in your top-level script simply call

```python
from envorce import envorce

envorce()
```

The program will continue to search parent directories for the file `envorce.toml` until reaching the home directory or the iteration limit.
mcpskills-cli 0.1.2
Generate Cursor Agent Skills from MCP server tools
Requires Python: >=3.10

# mcpskills-cli

Generate Agent Skills from MCP server tools. Connects via Streamable HTTP, discovers tools, and outputs a skill with schema docs and a call script in the language of your choice.

## Why bake MCP into skills?

Skills are easy to understand, static, edit, and most coding and non-coding agents are adopting them. Traditional MCPs load all tools into context and pollute the agent; token penalty is also costly because of loading every tool. This is why we transform MCP into skills: skills reduce token consumption because the agent does not load all skills into context—it only loads them when necessary.

## But why do we even need MCP?

MCP is a standardized protocol for AI agents to communicate. Because of this, any system can easily adopt it and create the necessary tools specific to their needs.

## Install

```bash
pip install mcpskills-cli
# or for development:
pip install -e .
```

## Usage

```bash
mcpskills-cli --url <MCP_SERVER_URL> --token <TOKEN> [--name <NAME>] [--output <DIR>] [--script <LANG>] [--multi-skills]
```

| Flag | Default | Description |
|---|---|---|
| `--url` | (required) | MCP server endpoint |
| `--token` | (required) | Bearer token |
| `--name` | from URL | Server name (skill dir + credentials key) |
| `--output` | `~/.cursor/skills` | Skills output directory |
| `--script` | `bash` | Call script language: `bash`, `python`, `node`, `go`, `rust` |
| `--multi-skills` | `false` | Generate a separate skill for each tool |

### Examples

```bash
# Generate single skill with all tools (default)
mcpskills-cli --url http://localhost:8027/mcp/abc123 --token mytoken --name my-db

# Generate separate skill for each tool
mcpskills-cli --url http://localhost:8027/mcp/abc123 --token mytoken --name my-db --multi-skills

# Generate with Python call script
mcpskills-cli --url http://localhost:8027/mcp/abc123 --token mytoken --name my-db --script python

# Generate with Node.js call script
mcpskills-cli --url http://localhost:8027/mcp/abc123 --token mytoken --name my-db --script node
```

## Credentials

Stored in `~/.mcps/credentials` (INI format, `chmod 600`). One section per server, updated automatically.

```ini
[my-db]
url = http://localhost:8027/mcp/abc123/
token = mytoken
```

Rotate tokens by editing the file directly; no need to regenerate skills.

## Generated Output

### Default Mode (Single Skill)

```
~/.cursor/skills/<server-name>/
  SKILL.md              # Documents all tools with parameters
  scripts/
    call.<ext>          # Calls any tool: ./call.<ext> <tool_name> '{"key":"val"}'
```

### Multi-Skills Mode (--multi-skills)

```
~/.cursor/skills/<server-name>-<tool-name-1>/
  SKILL.md              # Documents single tool
  scripts/
    call.<ext>          # Calls tool: ./call.<ext> <tool_name> '{"key":"val"}'

~/.cursor/skills/<server-name>-<tool-name-2>/
  SKILL.md
  scripts/
    call.<ext>
```

## Requirements

- Python >= 3.10
- `fastmcp` >= 2.3
- `jinja2` >= 3.1
lean-mcp 0.1.1
MCP server and client for Lean 4 + Mathlib formal verification
Requires Python: >=3.10
Keywords: formal-verification, lean4, mathlib, mcp, theorem-proving

# lean-mcp

MCP server and client for **Lean 4 + Mathlib** formal theorem verification.

Exposes the Lean 4 compiler as an MCP tool (`verify_lean_theorem`) via stdio transport, allowing any MCP-compatible AI client (OpenCode, Claude Desktop, Cursor, etc.) to verify mathematical proofs.

## Prerequisites

- **Lean 4** installed via [Elan](https://github.com/leanprover/elan)
- A **Lean 4 project** with Mathlib dependency (with `lake build` completed)
- **Python 3.10+**

## Installation

```bash
pip install lean-mcp
```

Or install from source:

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

## Quick Start

### Server

Run the MCP server (stdio transport):

```bash
LEAN_PROJECT_PATH=/path/to/your/lean4/project lean-mcp-server
```

### Client (Programmatic)

```python
import asyncio
from lean_mcp.client import LeanMCPClient

async def main():
    async with LeanMCPClient() as client:
        await client.connect(lean_project_path="/path/to/lean4/project")

        result = await client.verify(
            "theorem modus_ponens (P Q : Prop) (hp : P) (hpq : P -> Q) : Q := hpq hp"
        )
        print(result)  # [QED] Compilacao bem-sucedida. Nenhum erro. Prova completa.

asyncio.run(main())
```

### Client Configuration (Claude Desktop, OpenCode, etc.)

Add to your MCP client configuration:

```json
{
  "mcpServers": {
    "lean-mcp": {
      "command": "lean-mcp-server",
      "env": {
        "LEAN_PROJECT_PATH": "/absolute/path/to/lean4/project"
      }
    }
  }
}
```

## Tool: `verify_lean_theorem`

| Parameter | Type | Required | Description |
|-----------|------|----------|-------------|
| `lean_code` | string | yes | Lean 4 code to compile. If no `import` is present, `import Mathlib` is prepended automatically. |
| `timeout` | int | no | Compilation timeout in seconds (default: 120). |

### Return Values

| Prefix | Meaning |
|--------|---------|
| `[QED]` | Proof compiles successfully (returncode 0, no output). |
| `[FEEDBACK DO COMPILADOR]` | Compilation error — includes compiler output for debugging. |
| `[ERRO_SISTEMA]` | Infrastructure failure (timeout, `lake` not found, etc.). |

## Architecture

```
MCP Client (any)
    |
    | stdio (JSON-RPC)
    v
lean_mcp/server.py      Tool: verify_lean_theorem
    |
    | subprocess (lake env lean)
    v
Lean 4 + Mathlib         Formal verifier
```

## Environment Variables

| Variable | Required | Description |
|----------|----------|-------------|
| `LEAN_PROJECT_PATH` | yes | Absolute path to the Lean 4 project directory containing `lakefile.toml`. |

## License

MIT
Topsis-Naman-102317144 1.0.3
A Python package to perform TOPSIS analysis for multi-criteria decision making
Requires Python: >=3.6

# TOPSIS Implementation in Python

A simple command-line tool and Python package for performing TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) analysis on multi-criteria decision-making problems.

## What is TOPSIS?

TOPSIS is a method used to find the best alternative from a set of options based on multiple criteria. It works by:
1. Normalizing the decision matrix
2. Applying weights to criteria
3. Finding ideal best and worst solutions
4. Calculating distances from these ideal points
5. Ranking alternatives based on relative closeness to the ideal best

## Installation

You can install this package using pip:

```bash
pip install Topsis-Naman-102317144
```

## Usage

### Command Line

After installation, you can use the `topsis` command directly from terminal:

```bash
topsis <InputDataFile> <Weights> <Impacts> <ResultFileName>
```

**Example:**
```bash
topsis data.csv "1,1,1,1,1" "+,+,-,+,+" result.csv
```

### Python Package

You can also import and use it in your Python code:

```python
from Topsis_Naman_102317144 import run_topsis

# Run TOPSIS analysis
run_topsis('data.csv', '1,1,1,1,1', '+,+,-,+,+', 'result.csv')
```

## Input File Format

The input file should be a CSV or Excel file with:
- First column: Name/ID of alternatives
- Remaining columns: Criteria values (numeric only)

**Example (data.csv):**
```
Fund Name,P1,P2,P3,P4,P5
M1,0.84,0.71,6.7,42.1,12.59
M2,0.91,0.83,7.0,31.7,10.11
M3,0.79,0.62,4.8,46.7,13.23
```

## Parameters

- **Weights**: Comma-separated numbers representing importance of each criterion
  - Example: "1,1,1,2" means 4 criteria with last one having double weight
  
- **Impacts**: Comma-separated + or - signs
  - `+` means higher value is better (benefit criteria)
  - `-` means lower value is better (cost criteria)
  - Example: "+,+,-,+,+" means 3rd criterion is cost, others are benefits

## Output

The program generates a CSV file with:
- All original columns
- **Topsis Score**: Calculated score for each alternative
- **Rank**: Ranking based on TOPSIS score (1 is best)

## Error Handling

The program checks for:
- Correct number of parameters
- File existence
- Minimum 3 columns in input
- Numeric values in criteria columns
- Matching count of weights, impacts, and criteria
- Valid impact values (+ or -)

## Example

```bash
# Install the package
pip install Topsis-Naman-102317144

# Run TOPSIS
topsis input.csv "1,2,1,1" "+,-,+,+" output.csv
```

## Requirements

- Python 3.6+
- pandas
- numpy

## License

MIT License - feel free to use this for your projects!

## Author

Naman Singh (Roll: 102317144)

## Version

1.0.3