semvec/pypi-packages huggingface dataset:
mlbrecaps 0.1.0
Package that gathers information on given MLB games and allows downloading of video clips of plays.
Requires Python: >=3.12

# mlbrecaps

mlbrecaps is a Python library for querying and retrieving highlight videos and play information from Major League Baseball (MLB) games. It provides a simple interface to access game recaps, top plays, and player highlights programmatically.

## Features

- Query highlight videos for specific MLB games
- Retrieve top plays for a given day, month, or year
- Get player-specific highlight clips
- Easily integrate with your own Python scripts

## Installation

You can install mlbrecaps directly from PyPI using pip:

```bash
pip install mlbrecaps
```

### Install from Source

1. **Clone the repository:**

   ```bash
   git clone https://github.com/yourusername/mlbrecaps.git
   cd mlbrecaps
   ```

2. **Install dependencies with uv:**

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

   This will install the package in editable mode along with all required dependencies.

## Example Scripts

The `examples/` directory contains ready-to-run scripts:

- `examples/top_player_plays.py` — Get top plays for a player
- `examples/top_plays_of_month.py` — Get top plays for a month
- `examples/top_plays_of_year.py` — Get top plays for a year

Run an example with:

```bash
python examples/top_player_plays.py
```

## Contributing

Contributions are welcome! To contribute:

1. Fork the repository and create your branch.
2. Make your changes and add tests if applicable.
3. Ensure code style and formatting are consistent.
4. Submit a pull request with a clear description of your changes.

## License

This project is open source and available under the MIT License.
monkey-coder-core 1.1.1
Python orchestration core for Monkey Coder
Requires Python: >=3.8

# Monkey Coder Core

Python orchestration core for AI-powered code generation and analysis.

## Features

- Multi-agent orchestration with SuperClaude, monkey1, and Gary8D systems
- Support for multiple AI providers (OpenAI, Anthropic, Google, Qwen)
- Real-time monitoring and metrics collection
- Comprehensive error tracking with Sentry
- Production-ready FastAPI backend

## Installation

```bash
pip install monkey-coder-core
```

## Usage

```python
from monkey_coder.app.main import app

# Run with uvicorn
# uvicorn monkey_coder.app.main:app --host 0.0.0.0 --port 8000
```

## Model Support

The system only uses permitted models as defined in the MODEL_REGISTRY:

### OpenAI

- gpt-4.1
- gpt-4.1-mini

### Anthropic

- claude-4-opus
- claude-4-sonnet
- claude-3.7-sonnet
- claude-3.5-sonnet
- claude-3.5-haiku

### Google

- gemini-2.5-pro
- gemini-2.5-flash
- gemini-2.0-pro
- gemini-2.0-flash

### Qwen

- qwen-coder-3-32b
- qwen-coder-3-14b
- qwen-coder-3-7b
- qwen-coder-3-1.5b

### Grok

- grok-4
- grok-3

### Moonshot

- kimi-k2

## License

MIT License
monkey-coder-sdk 1.1.0
Python SDK for Monkey Coder API - AI-powered code generation and analysis
Requires Python: >=3.8
Keywords: ai, code generation, api, sdk, monkey coder, automation

# Monkey Coder SDK

Python SDK for interacting with the Monkey Coder API.

## Installation

```bash
pip install monkey-coder-sdk
```

## Usage

```python
from monkey_coder_sdk import MonkeyCoderClient

client = MonkeyCoderClient(api_key="your-api-key")

# Execute a coding task
result = client.execute(
    task_type="code_generation",
    prompt="Create a function to calculate fibonacci numbers",
    files=[]
)

print(result)
```

## Features

- Easy API integration
- Type hints and validation
- Async support
- Error handling

## License

MIT License