What is the benefit of progressive loading in this skill?

Progressive loading saves context tokens by only loading specific mathematical sub-skills when they are actually required for the task at hand.

Does this skill activate automatically?

Yes, Claude Code will automatically trigger this discovery skill whenever it detects work involving math, proofs, or complex algorithms.

Which mathematical fields are covered by this skill?

The skill provides access to 11 specialized areas including abstract algebra, category theory, differential equations, optimization, and topology.

How do I see the full list of available math sub-skills?

You can view the complete category details and descriptions by running 'cat ~/.claude/skills/math/INDEX.md' in your terminal.

Advanced Math & Algorithms Discovery

Name: Advanced Math & Algorithms Discovery
Author: rand

byrand

•

Data Science & ML

Automatically discovers and activates specialized mathematics and algorithm skills for complex computational and theoretical development tasks.

About

This skill serves as a high-efficiency gateway for Claude Code, enabling the AI to automatically recognize and load specialized mathematical frameworks ranging from linear algebra to topology. By implementing a progressive loading system, it maintains context efficiency while providing deep expertise in eleven distinct sub-fields including category theory, optimization, and numerical methods, ensuring Claude applies the most relevant theorems and implementation patterns to your technical workflows.

Key Features

Progressive loading architecture to optimize context window usage
Automatic activation upon detection of mathematical or algorithmic tasks
42 GitHub stars
Comprehensive indexing of proofs, theorems, and implementation patterns
Direct integration with bash-based skill discovery commands
Gateway access to 11 specialized sub-skills including Topology and Number Theory

Use Cases

Implementing advanced linear algebra and optimization routines in data science projects
Formalizing mathematical proofs and exploring theoretical category theory foundations
Developing and optimizing complex numerical algorithms and differential equation solvers

About

Key Features

Progressive loading architecture to optimize context window usage
Automatic activation upon detection of mathematical or algorithmic tasks
42 GitHub stars
Comprehensive indexing of proofs, theorems, and implementation patterns
Direct integration with bash-based skill discovery commands
Gateway access to 11 specialized sub-skills including Topology and Number Theory

Use Cases

Implementing advanced linear algebra and optimization routines in data science projects
Formalizing mathematical proofs and exploring theoretical category theory foundations
Developing and optimizing complex numerical algorithms and differential equation solvers