asymptotic-notation-calculator

Automated derivation and simplification of Big-O, Big-Omega, and Big-Theta expressions for algorithm analysis

509 stars

Best use case

asymptotic-notation-calculator is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

Automated derivation and simplification of Big-O, Big-Omega, and Big-Theta expressions for algorithm analysis

Teams using asymptotic-notation-calculator should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

You only need a quick one-off answer and do not need a reusable workflow.
You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/asymptotic-notation-calculator/SKILL.md --create-dirs "https://raw.githubusercontent.com/a5c-ai/babysitter/main/library/specializations/domains/science/computer-science/skills/asymptotic-notation-calculator/SKILL.md"

Manual Installation

Download SKILL.md from GitHub
Place it in .claude/skills/asymptotic-notation-calculator/SKILL.md inside your project
Restart your AI agent — it will auto-discover the skill

How asymptotic-notation-calculator Compares

Feature / Agent	asymptotic-notation-calculator	Standard Approach
Platform Support	Not specified	Limited / Varies
Context Awareness	High	Baseline
Installation Complexity	Unknown	N/A

Frequently Asked Questions

What does this skill do?

Automated derivation and simplification of Big-O, Big-Omega, and Big-Theta expressions for algorithm analysis

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

SKILL.md Source

# Asymptotic Notation Calculator

## Purpose

Provides expert guidance on analyzing, simplifying, and comparing asymptotic complexity expressions for algorithm analysis.

## Capabilities

- Parse and simplify asymptotic expressions
- Compare complexity classes
- Identify dominant terms
- Handle logarithmic, polynomial, and exponential factors
- Generate LaTeX-formatted notation
- Prove asymptotic relationships

## Usage Guidelines

1. **Expression Analysis**: Parse complexity expressions into canonical forms
2. **Simplification**: Apply dominance rules to simplify nested expressions
3. **Comparison**: Determine ordering between complexity classes
4. **Documentation**: Generate properly formatted asymptotic notation
5. **Verification**: Prove Big-O, Big-Omega, and Big-Theta relationships

## Tools/Libraries

- SymPy
- Symbolic computation
- LaTeX rendering

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