semvec/pypi-packages huggingface dataset:
pylib-codefixer 0.1.0
AI-based code refactoring & linting assistant. Code quality AI tools. Perfect for AI agents and LLM applications.
License: MIT
Requires Python: >=3.8
Keywords: ai, code-quality, data-processing, data-science, machine-learning, ml, nlp, refactoring, utilities

# pycodefixer

Code refactoring

## Installation

```bash
pip install pycodefixer
```

## Usage

```python
from pycodefixer import refactor

# Usage example
```


## 🤖 AI Agent Friendly

This package is optimized for AI agents and code generation tools:
- **Clear function names** and signatures
- **Comprehensive docstrings** with examples
- **Type hints** for better IDE support
- **Common use cases** documented
- **Zero dependencies** for reliability

## License

MIT
pylib-docgen 0.1.0
Generate README/API docs using AI summarization. AI-powered documentation. Perfect for AI agents and LLM applications.
License: MIT
Requires Python: >=3.8
Keywords: ai, code-generation, data-processing, data-science, documentation, machine-learning, ml, nlp, utilities

# pydocgen

Documentation generation

## Installation

```bash
pip install pydocgen
```

## Usage

```python
from pydocgen import generate_docs

# Usage example
```


## 🤖 AI Agent Friendly

This package is optimized for AI agents and code generation tools:
- **Clear function names** and signatures
- **Comprehensive docstrings** with examples
- **Type hints** for better IDE support
- **Common use cases** documented
- **Zero dependencies** for reliability

## License

MIT
pylib-autogptkit 0.1.0
Build agent workflows with memory & tools. Autonomous AI agents toolkit. Perfect for AI agents and LLM applications.
License: MIT
Requires Python: >=3.8
Keywords: agent, ai, autonomous, data-processing, data-science, llm, machine-learning, ml, nlp, utilities

# pyautogptkit

Agent workflows

## Installation

```bash
pip install pyautogptkit
```

## Usage

```python
from pyautogptkit import Agent

# Usage example
```


## 🤖 AI Agent Friendly

This package is optimized for AI agents and code generation tools:
- **Clear function names** and signatures
- **Comprehensive docstrings** with examples
- **Type hints** for better IDE support
- **Common use cases** documented
- **Zero dependencies** for reliability

## License

MIT
pylib-textai 0.1.0
Sentiment analysis, embeddings, summarization wrappers. AI text processing. Perfect for AI agents and LLM applications.
License: MIT
Requires Python: >=3.8
Keywords: ai, data-processing, data-science, embeddings, machine-learning, ml, nlp, sentiment, utilities

# pytextai

Text AI utilities

## Installation

```bash
pip install pytextai
```

## Usage

```python
from pytextai import sentiment

# Usage example
```


## 🤖 AI Agent Friendly

This package is optimized for AI agents and code generation tools:
- **Clear function names** and signatures
- **Comprehensive docstrings** with examples
- **Type hints** for better IDE support
- **Common use cases** documented
- **Zero dependencies** for reliability

## License

MIT
pylib-aibox 0.1.0
Prompt templates & LLM orchestration helpers. Essential for AI agents and LLMs. Perfect for AI agents and LLM applications.
License: MIT
Requires Python: >=3.8
Keywords: agent, ai, data-processing, data-science, llm, machine-learning, ml, nlp, prompt-engineering, utilities

# pyaibox

AI orchestration

## Installation

```bash
pip install pyaibox
```

## Usage

```python
from pyaibox import PromptTemplate

# Usage example
```


## 🤖 AI Agent Friendly

This package is optimized for AI agents and code generation tools:
- **Clear function names** and signatures
- **Comprehensive docstrings** with examples
- **Type hints** for better IDE support
- **Common use cases** documented
- **Zero dependencies** for reliability

## License

MIT
iflow-mcp_mcp-server-calculator 0.2.0
A Model Context Protocol server for calculating
License: MIT
Requires Python: >=3.10
Keywords: calculator, llm, math, mcp

# Calculator MCP Server

A Model Context Protocol server for calculating. This server enables LLMs to use calculator for precise numerical calculations.

### Available Tools

- `calculate` - Calculates/evaluates the given expression.
  - `expression` (string, required): Expression to be calculated

## Installation

### Using uv (recommended)

When using [`uv`](https://docs.astral.sh/uv/) no specific installation is needed. We will
use [`uvx`](https://docs.astral.sh/uv/guides/tools/) to directly run *mcp-server-calculator*.

```bash
curl -LsSf https://astral.sh/uv/install.sh | sh
```

### Using PIP

Alternatively you can install `mcp-server-calculator` via pip:

```bash
pip install mcp-server-calculator
```

After installation, you can run it as a script using:

```bash
python -m mcp_server_calculator
```

## Configuration

### Using uv (recommended)

Add this to your MCP client settings:

```json
"mcpServers": {
  "calculator": {
    "command": "uvx",
    "args": ["mcp-server-calculator"]
  }
}
```

### Using PIP

Alternatively add this to your MCP client settings:

```json
"mcpServers": {
  "calculator": {
    "command": "python",
    "args": ["-m", "mcp_server_calculator"]
  }
}
```

## License

mcp-server-calculator is licensed under the MIT License. This means you are free to use, modify, and distribute the software, subject to the terms and conditions of the MIT License. For more details, please see the LICENSE file in the project repository.
pythagix 0.2.23
Pythagix is a lightweight Python library that provides a collection of mathematical utility functions for number theory
License: MIT
Requires Python: >=3.6
Keywords: math, prime, LCM, triangle numbers, gcd, statistics, utilities, high-digit math

# Pythagix


Pythagix is a **high-performance, battle-tested Python library** for number theory and statistics. 
It delivers **blazing-fast, reliable, and precise implementations** of essential mathematical utilities, including prime checking, GCD/LCM, ratio simplification, triangular numbers, and more. 
Designed for developers, students, and researchers alike, Pythagix makes handling **massive numbers, complex computations, and advanced math operations** simple, efficient, and hassle-free.

Source code: https://github.com/UltraQuantumScriptor/pythagix


## Features

Primes & Factorization: is_prime, nth_prime, prime_factorization, prime_factors

GCD / LCM / Ratios: gcd, lcm, simplify_ratio, is_equivalent

Statistics: mean, median, mode, variance, pvariance, std_dev, pstd_dev

Other utilities: triangle_number, compress_0, product, nCr, get_factors

Each function is fully tested and designed for high performance with arbitrarily large integers.


## Installation

```bash
pip install pythagix
```


## Usage

```python
from pythagix import gcd, is_prime, nth_prime

# Compute GCD of large numbers
print(gcd([12345678901234567890, 98765432109876543210]))

# Check if a number is prime
print(is_prime(101))  # True

# Get the 1000th prime number
print(nth_prime(1000))
```


## Testing

Pythagix uses pytest for automated testing. To run the test suite:

```bash
pytest
```

All core functionality is verified for correctness and performance across large inputs.


## Contributing

Contributions are welcome. To report bugs, suggest improvements, or submit code enhancements:

### Open an issue on GitHub

Submit a pull request

Refer to the repository’s CONTRIBUTING.md for detailed guidelines.


## License

Pythagix is licensed under the MIT License.
animepixels 2.0.1
Python wrapper for the AnimePixels API
License: MIT
Requires Python: >=3.8

# AnimePixels-API Python SDK

A simple NPM wrapper for the [AnimePixels API].
Fetch anime images & GIFs by category, ID, randomly, or search — in one line.
Works cross-platform (Node.js, React, Next.js, python etc.).

---

## 🚀 Installation

```bash
pip install animepixels



from animepixels import AnimePixels

# Initialize the API client
api = AnimePixels(base_url="https://animepixels-api.vercel.app/api/media")

# -------------------------------
#  Random Image
# -------------------------------
print("🖼️ Random Image:")
random_img = api.images.random()
print(random_img, "\n")

# -------------------------------
#  Random Image by Category
# -------------------------------
print("🎯 Random Naruto Image:")
naruto_img = api.images.random("naruto")
print(naruto_img, "\n")

# -------------------------------
#  Get Image by ID
# -------------------------------
print("🆔 Get Image by ID:")
image_by_id = api.images.by_id("123abc")  # Replace with valid ID
print(image_by_id, "\n")

# -------------------------------
#  Get Images by Category
# -------------------------------
print("📂 Images from 'onepiece' category:")
onepiece_imgs = api.images.by_category("onepiece", limit=3)
for img in onepiece_imgs:
    print(img["url"])
print()

# -------------------------------
#  Search Images
# -------------------------------
print("🔍 Search for 'itachi' images:")
search_results = api.images.search("itachi", limit=3)
for r in search_results:
    print(r["url"])
print()

# -------------------------------
#  Random GIF
# -------------------------------
print("🎞️ Random GIF:")
random_gif = api.gifs.random()
print(random_gif, "\n")

# -------------------------------
#  Random GIF by Category
# -------------------------------
print("🎬 Random 'naruto' GIF:")
naruto_gif = api.gifs.random("naruto")
print(naruto_gif, "\n")

# -------------------------------
#  Get GIF by ID
# -------------------------------
print("🆔 Get GIF by ID:")
gif_by_id = api.gifs.by_id("abc123")  # Replace with valid ID
print(gif_by_id, "\n")

# -------------------------------
#  Get GIFs by Category
# -------------------------------
print("📁 GIFs from 'attack-on-titan' category:")
aot_gifs = api.gifs.by_category("attack-on-titan", limit=3)
for gif in aot_gifs:
    print(gif["url"])
print()

# -------------------------------
#  Search GIFs
# -------------------------------
print("🔍 Search for 'luffy' GIFs:")
search_gifs = api.gifs.search("luffy", limit=3)
for g in search_gifs:
    print(g["url"])
print()

# -------------------------------
# Get All Categories
# -------------------------------
print("📚 Available Categories:")
categories = api.categories.all()
print(categories)
print("\n✅ Done! All features tested successfully.")