gpd-derive-equation

Derive physics equations with explicit assumptions, notation, and verification steps.

Updated Mar 15, 2026
One-click install
npx skills add https://github.com/MichaelsEngineering/get-physics-done-test --skill gpd-derive-equation-michaelsengineering
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: gpd-derive-equation
Source: https://github.com/MichaelsEngineering/get-physics-done-test/tree/main/.agents/skills/gpd-derive-equation
Command: npx skills add https://github.com/MichaelsEngineering/get-physics-done-test --skill gpd-derive-equation-michaelsengineering

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill provides a structured, auditable workflow to derive physics equations with explicit assumptions, notation, and step-by-step verification, reducing errors in complex theoretical work.

Core Features & Use Cases

  • Step-by-step derivation with explicit assumptions and notation
  • Dimensional checks, symmetry verifications, and consistency checks at each stage
  • Automates documentation and cross-phase consistency for research workflows

Quick Start

Provide the starting Lagrangian and target equation to begin the end-to-end, rigorous derivation workflow.

Frequently Asked Questions about gpd-derive-equation

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

FAQPage Schema
How do I perform a rigorous physics derivation with step-by-step verification?

To perform a rigorous physics derivation, provide your starting Lagrangian and target equation to receive a stepwise workflow with explicit assumptions, notation, dimensional checks, and a final boxed result for reproducibility.

What is the best way to ensure dimensional consistency in physics derivations?

Ensuring dimensional consistency in physics derivations requires applying explicit dimensional checks and symmetry verifications at each stage of the workflow, cross-checking notation and assumptions before producing a final boxed result.

Can I use this workflow to derive equations from a specific Lagrangian?

Yes, you can derive equations from a specific Lagrangian by providing it as the starting input along with your target equation to initiate the end-to-end derivation workflow with stepwise verification.

Does the derivation workflow enforce explicit notation and assumptions for theoretical physics?

The derivation workflow enforces explicit notation and assumptions for theoretical physics by applying stepwise verification, dimensional checks, and cross-phase consistency checks to reduce errors in complex theoretical work.

What are the limitations of automated physics equation derivation and cross-checking?

Automated physics equation derivation focuses on theoretical work with explicit assumptions and notation, requiring a valid starting Lagrangian and target equation to execute its stepwise verification and cross-checks effectively.