implement-math

Translate mathematical formulas into explicit code with named estimators and order-sensitive tests.

4|Updated Feb 25, 2026
One-click install
npx skills add https://github.com/AMindToThink/claude-code-settings --skill implement-math
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: implement-math
Source: https://github.com/AMindToThink/claude-code-settings/tree/main/skills/implement-math
Command: npx skills add https://github.com/AMindToThink/claude-code-settings --skill implement-math

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps you translate mathematical formulas into code without silently changing the intended estimator, aggregation order, or normalization.

Core Features & Use Cases

  • Faithful Formula Translation: Expands compact notation into explicit code paths so sums, means, products, and expectations are implemented in the correct order.
  • Estimator Disambiguation: Separates similar quantities such as mean-of-ratios and ratio-of-means, or micro, macro, and weighted metrics, so the right one is named and used.
  • Validation and Review Support: Encourages property tests, docstrings, and line-by-line correspondence checks to catch numerical mismatches in metrics, losses, and algorithm implementations.
  • Use Case: A researcher implementing a paper’s per-byte metric can verify that the published formula matches the code instead of accidentally computing a different aggregation.

Quick Start

Use the implement-math skill to rewrite this formula into explicit code, name any competing estimators, and add a test that proves the intended aggregation order.

Frequently Asked Questions about implement-math

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

FAQPage Schema
How do I translate mathematical formulas into code without changing the aggregation order?

To translate mathematical formulas into code faithfully, expand compact notation into explicit code paths so sums, means, and products execute in the correct order. This prevents silently changing the intended estimator during metric implementation.

What is the difference between mean-of-ratios and ratio-of-means in metric implementation?

Mean-of-ratios and ratio-of-means are competing estimators distinguished by their aggregation order. Estimator disambiguation separates these similar quantities, ensuring the right one is explicitly named and used in your numerical code.

How do I verify my algorithm translation matches the published formula?

Verify algorithm translation by adding property tests and performing line-by-line correspondence checks between the code and the formula. This catches numerical mismatches in metrics, losses, and algorithm implementations.

Why does my numerical debugging show mismatches in weighted metrics?

Numerical debugging often reveals mismatches in weighted metrics because multiple aggregation paths are possible. Explicit operator expansion and distinct names for competing estimators clarify whether the intended normalization was actually computed.

When do I need explicit operator expansion for math-heavy code reviews?

You need explicit operator expansion for math-heavy code reviews whenever multiple aggregation paths are possible. It ensures sums, means, and expectations are implemented in the correct order, preventing silent estimator changes.