A specialized library of mathematical utilities for number theory, digit analysis, and optimized large integer multiplication using the Karatsuba algorithm.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install maths
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install maths using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Pywayne Maths?
Pywayne Maths provides a suite of essential mathematical functions designed to give AI agents deep numerical reasoning capabilities. By integrating these Openclaw Skills, developers can enable their agents to perform complex factorization, analyze digit distributions across large ranges, and execute high-performance arithmetic. The library is particularly useful for scenarios where standard built-in functions lack the optimization needed for competitive programming or cryptographic-level calculations.
The core of the library is built around three pillars: thorough factor analysis, statistical digit counting, and algorithmic optimization. These features ensure that whether your agent is simplifying fractions, checking for primality, or multiplying integers with hundreds of digits, it can do so efficiently using the specialized tools found within Openclaw Skills.
Pywayne Maths Use Cases
- Identifying all factors and divisors for number theory and primality testing.
- Performing digit frequency analysis for data forensics or statistical research.
- Optimizing large-scale integer multiplication in cryptographic applications.
- Solving competitive programming problems that require efficient divide-and-conquer algorithms.
- Automating fraction simplification and greatest common divisor calculations.
How Pywayne Maths Works
- The AI agent identifies a numerical task, such as finding the factors of a large integer, and invokes the appropriate function from Openclaw Skills.
- For factorization, the system calculates all unique divisors and returns them as a sorted list.
- For digit frequency, the skill iterates through the range [1, n] and applies optimized logic to count the occurrences of target digit k.
- When multiplying large numbers, the skill switches to the Karatsuba algorithm, recursively breaking down the numbers into smaller parts to reduce time complexity to O(n^1.585).
- The results are passed back to the agent in a structured format, allowing for immediate logical branching or further computation.
Pywayne Maths Setup
To start using these mathematical utilities, install the pywayne package via pip. This allows you to integrate the library directly into your Openclaw Skills workflow.
pip install pywayne
Once installed, you can import the tools into your script:
from pywayne.maths import get_all_factors, digitCount, karatsuba_multiplication
Pywayne Maths Data Schema & Taxonomy
The skill returns structured data designed for easy consumption by AI agents within the Openclaw Skills ecosystem:
| Function | Input Parameters | Output Format | Details |
|---|---|---|---|
| get_all_factors | n: int | list | Returns a sorted list of all unique divisors. |
| digitCount | n: int, k: int | int | Returns the frequency of digit k in range [1, n]. |
| karatsuba_multiplication | x: int, y: int | int | Returns the product of x and y using divide-and-conquer logic. |
Pywayne Maths Advanced Features
- Karatsuba Multiplication: A divide-and-conquer implementation that is significantly faster than O(n^2) naive multiplication for very large integers.
- Robust Digit Analysis: Specialized logic for digitCount to handle the edge cases of trailing zeros when k=0.
- Primality Logic: The factor output structure is optimized for O(1) checking of prime conditions after computation.
- Multi-Agent Support: Designed to be used as a stateless utility that can be shared across multiple Openclaw Skills instances for parallel processing.
SKILL.md
Loading
Related Openclaw Skills
Magnetometer Calibration (Pywayne)
A high-performance sensor calibration toolkit for computing magnetometer soft-iron matrices and hard-iron offsets.
Pywayne Lark Custom Bot
A comprehensive Python wrapper for the Feishu/Lark Custom Bot API that facilitates automated messaging, image uploads, and interactive cards via webhooks.
Pywayne Lark Bot
A comprehensive Python wrapper for the Feishu/Lark Bot API, supporting rich messaging, file management, and real-time event listening.
Pywayne Lark Bot Listener
An asynchronous WebSocket listener for Feishu and Lark that simplifies real-time message handling and automated bot responses.
Pywayne Visualization Pangolin Utils
A high-performance Python wrapper for the Pangolin library designed for real-time 3D visualization of robotics and spatial data.
Pywayne Plot
A professional-grade visualization toolkit for high-precision time-frequency analysis and enhanced spectrogram generation.
Featured*
