semvec/pypi-packages huggingface dataset:
yt-queue 0.13.2
CLI to keep track of videos in Youtube playlists
Requires Python: >=3.10
Keywords: yt-dlp, YouTube

# yt-queue
CLI to keep track of videos in Youtube playlists

## cli

See `yt-queue -h` and `yt-queue <subcommand> -h` for details.

Examples:

```sh
# create a new file
yt-queue create example.ytq.json "https://www.youtube.com/playlist?list=PL0pg4HdU1lNMtRzycn3wbKyfQO5vQZja9"

# refresh a file, only if it wasnt recently updated
yt-queue refresh example.ytq.json --only-if-older=1day

# get the "new" items
yt-queue filter --no-status example.ytq.json

# read the values of a video from the file
yt-queue read-field example.ytq.json "C0DPdy98e4c" url
yt-queue read-field example.ytq.json "C0DPdy98e4c" title

# set the status
yt-queue set-status example.ytq.json "C0DPdy98e4c" some-text-status
yt-queue filter --status=some-text-status example.ytq.json

# more filter options
yt-queue filter --title "TEST VIDEO" example.ytq.json
yt-queue filter --min-duration 3 example.ytq.json
yt-queue filter --max-duration 20 example.ytq.json
```

### output

Most cli subcommands' output (`stdout`) is parsable. `stderr` is used for logging:

- `filter` returns the matching video ids, 1 per line
- `read-field` returns the value of the field for the given video id

Other subcommands output should not be parsed - they contain either progress or verbose logging (including
from `yt-dlp`)

## development

setup (or recreate) environment with `source dev/init.sh`

or manually:

```shell
python3 -m venv .env
source .env/bin/activate
pip install --editable .
pip install '.[dev]'
```

other dependencies: `shellcheck`

tests: `./check.sh`

test the built packages: `./dist-check.sh dist/...`
mvcrender 0.1.0
High-performance C extensions for image processing in Home Assistant - AutoCrop, Blend, Draw, and Rooms processing for Valetudo Map Parser
Requires Python: >=3.12
Keywords: image-processing, home-assistant, valetudo, map-parser, numpy, c-extension, autocrop, blend, draw, rooms, vacuum-map

# mvcrender

As per libraries like OpenCV are not available on Home Assistant's Python 3.12, this library provides
a fast C extension for manipulating images generated in Numpy.
This is a kind of collection of image processing utilities that can be used in Home Assistant.
The development focus is on improving the performance of the Valetudo Map Parsers Library.

## Functions Implemented
- **AutoCrop**: determines the bounding box of an image and crops it based on a background color.
- **Blend**: blend a color or image into another image based on a mask or alpha values.
- **Draw**: draw lines, circles, polygons, and polylines on an image.
- **Rooms**: process room masks, fill compressed pixels, extract points from masks.
- **Material**: render floor material patterns (wood planks, tiles) with ultra-fast C implementation.

## Supported platforms
- Python: 3.13+
- Wheels: manylinux and musllinux (Alpine), macOS (x86_64, arm64), Windows (x86_64)
- Platforms: Linux x86_64/aarch64, macOS x86_64/arm64, Windows x86_64

## Install

```bash
pip install mvcrender
```

## Quick start

```python
from mvcrender.autocrop import AutoCrop
from mvcrender.material import MaterialTileRenderer
from mvcrender.draw import line_u8, circle_u8
import numpy as np

# AutoCrop is a mixin you can use in your handler class
class Handler(AutoCrop):
    def __init__(self):
        AutoCrop.__init__(self, self)
        # set up any fields your handler expects (crop_area, etc.)

# Material rendering - ultra-fast floor patterns
tile = MaterialTileRenderer.get_tile("wood_horizontal", pixel_size=5)
image = np.zeros((500, 500, 4), dtype=np.uint8)
MaterialTileRenderer.apply_overlay_on_region(image, tile, 0, 500, 0, 500)

# Drawing primitives
line_u8(image, 0, 0, 100, 100, (255, 0, 0, 255), thickness=2)
circle_u8(image, 250, 250, 50, (0, 255, 0, 255), thickness=3)
```

## Why musllinux wheels?

Home Assistant often runs on Alpine-based Docker images. musllinux wheels ensure zero‑compile installs there.

## Development

- Build backend: setuptools (PEP 517)
- Source layout: src/
- C extension: src/mvcrender/autocrop/_native.c

### Local build (sdist + wheel)

```bash
python -m pip install -U build
python -m build
```

### Tests

A simple smoke example lives in tests/smoke.py. CI runs a minimal import test on the built wheels to keep the matrix fast and reliable.
mcp-gemini-search 0.3.1
MCP Server for web search via Gemini's grounding API
Requires Python: >=3.10
Keywords: ai, anthropic, claude, gemini, llm, mcp, search

# MCP Search Server

A Model Context Protocol (MCP) server that provides web search capabilities via Gemini's grounding API.

## Features

- 🔍 Real-time web search powered by Google Gemini
- ⚡ Built-in caching (5 min TTL)
- 🎯 Structured search results (Title + URL + Summary)
- 🌍 Multi-language support

## Installation

```bash
pip install mcp-search-server
```

## Configuration

Set environment variables:

```bash
export MCP_SEARCH_URL="http://localhost:8045/v1/messages"
export MCP_SEARCH_API_KEY="your-api-key"
export MCP_SEARCH_MODEL="gemini-3-flash"  # or gemini-3-pro-high
export MCP_SEARCH_CACHE_TTL="300"  # seconds
```

## Usage

### As MCP Server (with Zed, Claude Desktop, etc.)

```json
{
  "mcpServers": {
    "mcp-search": {
      "command": "python",
      "args": ["-m", "mcp_search"],
      "env": {
        "MCP_SEARCH_URL": "http://localhost:8045/v1/messages",
        "MCP_SEARCH_API_KEY": "your-api-key",
        "MCP_SEARCH_MODEL": "gemini-3-flash"
      }
    }
  }
}
```

### As Python Library

```python
import asyncio
from mcp_search import do_search

result = asyncio.run(do_search(
  query="latest AI news",
  model="gemini-3-flash"
))
print(result)
```

## Tool Parameters

| Parameter | Type | Required | Description |
|-----------|------|----------|-------------|
| `query` | string | ✅ | Search keywords |
| `model` | string | ❌ | Model ID (default: gemini-3-flash) |

## Requirements

- Python 3.10+
- [Antigravity Manager](https://github.com/lbjlaq/Antigravity-Manager) running locally

## License

MIT