semvec/pypi-packages huggingface dataset:
tsdtool 1.0.0
Script that downloads telegram stories.
Requires Python: >=3.9
Keywords: library, telegram, stories, download

# tsdtool

> _From the creators of [rcdtool](https://github.com/David256/rcdtool)..._

Script that downloads telegram stories.

## Usage

For help:

```bash
./tsdtool --help
```

This script needs the story link to download it.

```bash
tsdtool -c /path/to/config.ini --link "https://t.me/<username>/s/<id>" -O /tmp/path/to/directory
```

Then the script starts the downloading.

### Download all the current stories

You can pass the channel and download all the public stories:

```bash
tsdtool -c /path/to/config.ini -C "<username or channel ID>" -O /tmp/path/to/directory
```

### Download the pinned stories

These stories are saved as "posts":

```bash
tsdtool --pinned-stories -C "<username or channel ID>" -O /tmp/path/to/directory
```

### Download an specific story

You can pass the channel and the story ids separated by comma:

```bash
tsdtool -C "<username or channel ID>" --story-ids 23
```

or...

```bash
tsdtool -C "<username or channel ID>" --story-ids 23,24,40
```

## Dist

In this repository we release the source code (Python) and a binary option for GNU/Linux. You can build a binary for any other operating system using tool as [PyInstaller](https://pyinstaller.org/en/).

## Telegram session

You MUST have an API ID provided by Telegram at https://my.telegram.org/ (I think). This is as follows:

```
api_id: 32767
api_hash: ed855a59bbe4a3360dbf7a0538842142
```

Then rename `config.ini.sample` to `config.ini`, edit it and save wherever you want. If the file is in the same directory as `tsdtool` and its name is exactly "config.ini", then `tsdtool` will load it automatically.

The first time, **tsdtool** will ask you for your phone number, and will start a login process. When this is done, a `.session` file will be created. With this `.session` file, the tool could access to your Telegram account to read messages and download medias. The name of the .session file is set in `config.ini`.
iflow-mcp_cve-search-mcp 0.1.0
Add your description here
Requires Python: >=3.10

[![MseeP.ai Security Assessment Badge](https://mseep.net/pr/roadwy-cve-search-mcp-badge.png)](https://mseep.ai/app/roadwy-cve-search-mcp)

## CVE-Search MCP Server
----

![CVE-SEARCH_MCP](https://socialify.git.ci/roadwy/cve-search_mcp/image?name=1&theme=Light)

<a href="https://glama.ai/mcp/servers/@roadwy/cve-search_mcp">
  <img width="380" height="200" src="https://glama.ai/mcp/servers/@roadwy/cve-search_mcp/badge" alt="cve-search_mcp MCP server" />
</a>

A Model Context Protocol (MCP) server for querying the [CVE-Search](https://www.cve-search.org/api/) API. This server provides comprehensive access to CVE-Search, browse vendor and product、get CVE per CVE-ID、get the last updated CVEs.

## Requirements
- python 3.10+
- uv
- Cline、Roo Code etc

## Tools
- To get a JSON with all the vendors
- To get a JSON with all the products associated to a vendor
- To get a JSON with all the vulnerabilities per vendor and a specific product
- To get a JSON of a specific CVE ID
- To get a JSON of the last 30 CVEs including CAPEC, CWE and CPE expansions
- To get more information about the current databases in use and when it was updated

## Quick Start
1. Git clone this repository
```
git clone https://github.com/roadwy/cve-search_mcp.git
```
2. Install the dependencies
```
cd cve-search_mcp
uv sync
```
3.Add to your mcp client(vscode with cline/roo code) configuration file, modify the `"YOU_CVE_SEARCH_MCP_DIR_PATH"` as you self dir.
```
    "cve-search_mcp": {
      "command": "uv",
      "args": [
        "--directory",
        "YOU_CVE_SEARCH_MCP_DIR_PATH",
        "run",
        "main.py"
      ],
      "disabled": false,
      "autoApprove": []
    }
```

## Reference
https://github.com/cve-search/cve-search
agentruntime-cli 0.0.1
AgentRuntime CLI (arun)
Requires Python: >=3.10

# AgentRuntime CLI (arun)

## Install
```bash
pip install agentruntime-cli
```

## Commands

- arun init
  - Scaffold a minimal Python MCP agent in the current directory
  - Creates `app/` with `main.py`, `tools/`, and `config.yaml`

- arun proxy --target-url <url> [--overlay-file tools.yaml] [--host 127.0.0.1] [--port 8010]
  - Run a lightweight proxy agent that fronts an existing MCP server

- arun generate-openapi -i openapi.json -o app/tools/generated.py
  - Generate tool stubs from an OpenAPI document

## Quick start
```bash
pip install agentruntime-cli agentruntime-mcp
arun init
python -m app.main
```

## Config
- Edit `app/config.yaml` (env overrides: `HOST`, `PORT`, `MCP_AUTH_MODE`)
- Auth modes: `none`, `token` (Bearer or X-MCP-Token), `hmac`
selectorllm 0.0.2
Add your description here
Requires Python: >=3.12
Keywords: html, litellm, llm, selector, web-scraping

![Selector llm logo](https://raw.githubusercontent.com/youseiushida/selectorllm/refs/heads/main/images/logo-blue.png)
# selectorllm
Generate CSS/XPath selectors from natural language using LLMs.  Extract HTML data without writing selectors manually.

---

自然言語でHTMLセレクタを生成 - LLMによる自動抽出
## Usage
```python
from selectorllm.main import get_selector

html = """<html>...</html>"""
query = "Extract link texts."

selector = get_selector(
    query,
    html,
    model="openrouter/openai/gpt-5", # Any LiteLLM-compatible model name
    selector_type="css"               # "css" or "xpath"
)

print(selector)  # example: "body > div > a"
```
agentruntime-mcp 0.0.1
AgentRuntime MCP SDK (decorators, runtime, middleware, schemas, proxy, generators)
Requires Python: >=3.10

# AgentRuntime MCP SDK (Python)

Opinionated SDK for building MCP agents with FastMCP.

## Install
```bash
pip install agentruntime-mcp
```

## Minimal example
```python
from pydantic import BaseModel, Field
from agentruntime.mcp.decorators import tool
from agentruntime.mcp.runtime import run

class In(BaseModel):
    a: float = Field(...)
    b: float = Field(...)

class Out(BaseModel):
    result: float
    expression: str

@tool(name="add", input_model=In, output_model=Out)
def add(a: float, b: float) -> Out:
    return Out(result=a+b, expression=f"{a} + {b}")

if __name__ == "__main__":
    run("config.yaml")
```

## Config
- `config.yaml` controls server host/port, auth mode, and tracing.  
- Env overrides: `HOST`, `PORT`, `MCP_AUTH_MODE`.

Auth modes
- `none`: no auth
- `token`: `Authorization: Bearer <token>` or `X-MCP-Token`; dev fallback `?auth_token=` if `ALLOW_QUERY_TOKEN=true`
- `hmac`: headers `X-MCP-KeyId`, `X-MCP-Timestamp` (unix seconds), `X-MCP-Signature` (hex(HMAC-SHA256(secret, `${ts}\n${method}\n${path}`)))

## Proxy (library)
```python
from agentruntime.mcp.proxy import run_proxy
run_proxy(target_url="http://127.0.0.1:8000/mcp", overlay_file="tools.yaml", host="127.0.0.1", port=8010)
```
iflow-mcp_mcp-ynab 0.1.0
MCP server for YNAB API integration
Requires Python: >=3.12

# MCP YNAB Server

An MCP server implementation that provides access to YNAB (You Need A Budget) functionality through the Model Context Protocol.

## Features

- View account balances and transactions
- Create new transactions
- Access YNAB data through standardized MCP resources

## Installation

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

## Configuration

The server requires a YNAB API key to function. You can obtain one from your [YNAB Developer Settings](https://app.ynab.com/settings/developer).

The API key can be provided through:

1. Environment variable: `YNAB_API_KEY=your_api_key`
2. MCP secret management system
3. `.env` file in project root

## Usage

### Running the Server

```bash
# Development mode with hot reload and browser launch
task dev

# Production install for Claude Desktop, Goose, or any other MCP-supported environment
task install
```

### Available Resources

- `ynab://accounts` - List all YNAB accounts
- `ynab://transactions/{account_id}` - Get recent transactions for a specific account

### Available Tools

- `create_transaction` - Create a new transaction
- `get_account_balance` - Get the current balance of an account

## Example Usage

```python
# Create a new transaction
result = await create_transaction(
    account_id="your_account_id",
    amount=42.50,  # in dollars
    payee_name="Coffee Shop",
    category_name="Dining Out",
    memo="Morning coffee"
)

# Get account balance
balance = await get_account_balance("your_account_id")

# List accounts
accounts = await ctx.read_resource("ynab://accounts")

# Get recent transactions
transactions = await ctx.read_resource(f"ynab://transactions/{account_id}")
```

## Development

```bash
# Install dependencies (uses uv)
task deps

# Run all tests including integration tests (you will need a YNAB API key for this)
task test:all

# Generate coverage report
task coverage

# Format and lint code
task fmt  # Should add this to Taskfile
```

## Project Tasks

This project uses a Taskfile for common operations. Key commands:

```bash
task dev       # Start dev server with auto-reload
task test      # Run unit tests
task coverage  # Generate test coverage report
task install   # Install production build
task deps      # Synchronize dependencies
```

See [Taskfile.yml](Taskfile.yml) for all available tasks.
sphinxnotes-lilypond 2.5
Sphinx extension for embedding LilyPond scores
Requires Python: >=3.12
Keywords: sphinx, extension, documentation, sphinxnotes, music, lilypond

.. This file is generated from sphinx-notes/cookiecutter.
   You need to consider modifying the TEMPLATE or modifying THIS FILE.

====================
sphinxnotes-lilypond
====================

.. |docs| image:: https://img.shields.io/github/deployments/sphinx-notes/lilypond/github-pages
   :target: https://sphinx.silverrainz.me/lilypond
   :alt: Documentation Status
.. |license| image:: https://img.shields.io/github/license/sphinx-notes/lilypond
   :target: https://github.com/sphinx-notes/lilypond/blob/master/LICENSE
   :alt: Open Source License
.. |pypi| image:: https://img.shields.io/pypi/v/sphinxnotes-lilypond.svg
   :target: https://pypi.python.org/pypi/sphinxnotes-lilypond
   :alt: PyPI Package
.. |download| image:: https://img.shields.io/pypi/dm/sphinxnotes-lilypond
   :target: https://pypistats.org/packages/sphinxnotes-lilypond
   :alt: PyPI Package Downloads

|docs| |license| |pypi| |download|

Sphinx extension for embedding LilyPond scores.

.. INTRODUCTION START 
   (MUST written in standard reStructuredText, without Sphinx stuff)

.. INTRODUCTION END

Please refer to Documentation_ for more details.

.. _Documentation: https://sphinx.silverrainz.me/lilypond
scMMDI 0.0.1
A deep learning framework for single cell multimode data mosaic integration
Requires Python: >=3.10

# scMMDI_py
scMMDI: A deep learning framework for single cell multimode data mosaic integration
This is the Python implementation of the scMMDI algorithm. Note that this implementation supports GPU acceleration.

## 1. Installation
You can use the following command to install scMMDI:
```
pip install scMMDI
```

## 2. Usage
scMMDI accepts a muon object as input and populates the obsm and uns fields with embeddings and dictionaries, respectively.
The pre-processed PBMC dataset is available for download here. It is recommended that a GPU be used to run the model.
To initialize and run the scMMDI model, the following code should be used:

```
from scMMDI.model import scMMDI
import mudata as md
import scanpy as sc

# Load data into a Muon object.
mdata = md.read_h5mu("my_data.h5mu")

# Initialize and train the model.
model = scMMDI(latent_dim=30)
model.fit(mdata)

# Visualize the embedding with UMAP.
sc.pp.neighbors(mdata, use_rep="scMMDI_z")
sc.tl.umap(mdata)
sc.pl.umap(mdata)
```

'scMMDI' class also has other methods, you can use the 'help' or '?' command for more details explanations of the methods.