math

Analyze mathematical pitfalls like integer overflow and floating-point inconsistencies in code.

5|2|Updated Feb 26, 2026

Search Tags:#software development #integer overflow #floating-point #mathematical analysis #modulo-wrap

Ready to run

npx skills add https://github.com/AxGord/claude-workflow --skill math-axgord

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: math
Source: https://github.com/AxGord/claude-workflow/tree/main/templates/skills/math
Command: npx skills add https://github.com/AxGord/claude-workflow --skill math-axgord

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill helps users identify and understand common mathematical issues, such as integer overflow, floating-point inconsistencies, and modulo-wrap problems, in their software development.

Core Features & Use Cases

Integer Overflow: Provides information on integer overflow boundaries and their impact on computations.
Floating-Point Parity: Identifies discrepancies between V8 Math.tan and CPython math.tan implementations.
Modulo-wrap Pitfalls: Explains the limitations of modulo-wrap operations with floating-point numbers.
Use Case: When developing a simulation that involves trigonometric calculations, this Skill can help prevent unexpected behavior due to floating-point inconsistencies between different runtimes.

Quick Start

Analyze the performance of integer overflow in the central binomial coefficient C(n, n/2) with the 'math' skill.

Frequently Asked Questions about math

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I identify integer overflow issues in software development?▼

Integer overflow issues in software development are identified by analyzing computational boundaries and their impact on calculations. This Skill analyzes integer overflow in operations like the central binomial coefficient C(n, n/2) to expose boundary limitations and prevent unexpected computational results.

Why do floating-point parity discrepancies occur between V8 and CPython?▼

Floating-point parity discrepancies between V8 and CPython occur due to implementation differences in mathematical functions like tangent. This Skill identifies discrepancies between V8 Math.tan and CPython math.tan implementations to help prevent unexpected behavior in trigonometric calculations across different runtimes.

What are the limitations of modulo-wrap operations with floating-point numbers?▼

Modulo-wrap operations with floating-point numbers face limitations that cause unpredictable wrapping behavior. This Skill explains these modulo-wrap pitfalls, detailing how floating-point precision impacts modular arithmetic and identifying inconsistencies in simulations and data processing tasks.

How do I analyze mathematical pitfalls in simulation code?▼

Mathematical pitfalls in simulation code are analyzed by evaluating precise calculations for integer overflow, floating-point inconsistencies, and modulo-wrap errors. This Skill applies to code relying on precise mathematical operations, detecting issues that cause unexpected behavior in simulations and data processing tasks.

When do I need mathematical analysis for floating-point inconsistencies?▼

Mathematical analysis for floating-point inconsistencies is needed when developing simulations involving trigonometric calculations across different runtimes. This Skill helps identify and understand these discrepancies, particularly when precise mathematical computations must yield consistent results.

Can I use this mathematical analysis for data processing tasks?▼

Yes, this mathematical analysis applies to data processing tasks that rely on precise mathematical calculations. It identifies common pitfalls such as integer overflow, floating-point parity issues, and modulo-wrap errors, ensuring computational consistency and preventing unexpected behavior in data processing workflows.