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Physics Solver

  • 18 installs
  • 869 repo stars
  • Updated June 8, 2026
  • beita6969/scienceclaw

physics-solver is a skill that solves physics problems and simulates physical systems across mechanics, electromagnetism, thermodynamics, and quantum mechanics using SymPy and SciPy.

About

This skill solves physics problems and simulates physical systems across classical mechanics, electromagnetism, thermodynamics, quantum mechanics, and optics. A developer or student uses it to derive equations, run numerical simulations, or do unit conversions. It provides SymPy for symbolic work, SciPy for numerics and physical constants, and a structured problem-solving framework.

  • Covers classical mechanics, electromagnetism, thermodynamics, quantum mechanics, and optics
  • Uses SymPy for symbolic derivations and SciPy for numerical simulation and constants
  • Includes a 7-step problem-solving framework with unit and limiting-case checks

Physics Solver by the numbers

  • 18 all-time installs (skills.sh)
  • Ranked #1,276 of 2,065 Data Science & ML skills by installs in the Skillselion catalog
  • Data as of Aug 2, 2026 (Skillselion catalog sync)
At a glance

physics-solver capabilities & compatibility

Capabilities
physics problem solving · physical simulation · symbolic derivation · unit conversion
Use cases
data analysis · research
Pricing
Free
From the docs

What physics-solver says it does

Physics problem solving including classical mechanics, electromagnetism, thermodynamics, quantum mechanics, optics, and computational physics.
SKILL.md
Use SymPy for symbolic derivations, SciPy for numerical
SKILL.md
For complex simulations, consider specialized tools (COMSOL, OpenFOAM)
SKILL.md
npx skills add https://github.com/beita6969/scienceclaw --skill physics-solver

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Listed on Skillselion
Installs18
repo stars869
Last updatedJune 8, 2026
Repositorybeita6969/scienceclaw

What it does

Solve and simulate physics problems across mechanics, EM, thermodynamics, and quantum mechanics using SymPy and SciPy.

Who is it for?

Students and researchers deriving equations, simulating physical systems, or converting units

Skip if: Large-scale multiphysics simulations, which the docs suggest specialized tools like COMSOL or OpenFOAM

When should I use this skill?

The user asks to solve a physics problem, simulate a physical system, derive an equation, or convert units

What you get

Symbolic derivations, numerical simulation results, or unit conversions with dimensional and limiting-case checks.

  • symbolic derivations
  • numerical simulation results
  • unit conversions

By the numbers

  • 7-step problem-solving framework
  • 6 physics domains covered

Files

SKILL.mdMarkdownGitHub ↗

Physics Solver

Physics computation and problem solving. Venv: source /Users/zhangmingda/clawd/.venv/bin/activate

Physical Constants

from scipy import constants as const
import numpy as np

# Key constants
c = const.c           # speed of light (m/s)
h = const.h           # Planck's constant (J·s)
hbar = const.hbar     # reduced Planck's constant
k_B = const.k         # Boltzmann constant (J/K)
e = const.e           # elementary charge (C)
m_e = const.m_e       # electron mass (kg)
m_p = const.m_p       # proton mass (kg)
G = const.G           # gravitational constant
N_A = const.N_A       # Avogadro's number
epsilon_0 = const.epsilon_0  # vacuum permittivity
mu_0 = const.mu_0     # vacuum permeability
sigma = const.sigma   # Stefan-Boltzmann constant

Classical Mechanics

from sympy import *

t = symbols('t')
m, g, k, L = symbols('m g k L', positive=True)

# Lagrangian mechanics
# Example: Simple pendulum
theta = Function('theta')(t)
T = Rational(1,2) * m * (L * diff(theta, t))**2  # kinetic energy
V = -m * g * L * cos(theta)                        # potential energy
Lag = T - V

# Euler-Lagrange equation
EL = diff(diff(Lag, diff(theta, t)), t) - diff(Lag, theta)
eq = simplify(EL)
print(f"Equation of motion: {eq} = 0")

# Numerical simulation (projectile, pendulum, etc.)
from scipy.integrate import solve_ivp

def pendulum(t, state, g=9.81, L=1.0):
    theta, omega = state
    return [omega, -g/L * np.sin(theta)]

sol = solve_ivp(pendulum, [0, 10], [np.pi/4, 0], max_step=0.01)

Electromagnetism

# Coulomb's law
def coulomb_force(q1, q2, r):
    """Force between two charges (N)"""
    return const.k * q1 * q2 / r**2  # k = 1/(4πε₀)

# Capacitor energy
def capacitor_energy(C, V):
    return 0.5 * C * V**2

# RC circuit
def rc_discharge(V0, R, C, t):
    tau = R * C
    return V0 * np.exp(-t / tau)

# Electromagnetic wave
def em_wavelength(frequency):
    return const.c / frequency

def photon_energy(wavelength):
    return const.h * const.c / wavelength

Quantum Mechanics

# Particle in a box energy levels
def particle_in_box(n, L, m=const.m_e):
    """Energy of nth level, box length L"""
    return (n**2 * const.h**2) / (8 * m * L**2)

# Hydrogen atom energy levels
def hydrogen_energy(n):
    """Energy in eV"""
    return -13.6 / n**2

# de Broglie wavelength
def de_broglie(p):
    return const.h / p

# Heisenberg uncertainty
# Δx · Δp ≥ ℏ/2

Thermodynamics & Statistical Mechanics

# Ideal gas
def ideal_gas_pressure(n, T, V):
    return n * const.R * T / V

# Carnot efficiency
def carnot_efficiency(T_hot, T_cold):
    return 1 - T_cold / T_hot

# Blackbody radiation (Planck's law)
def planck_spectral_radiance(wavelength, T):
    """W/(m²·sr·m)"""
    return (2 * const.h * const.c**2 / wavelength**5) / \
           (np.exp(const.h * const.c / (wavelength * const.k * T)) - 1)

# Maxwell-Boltzmann speed distribution
def mb_speed_dist(v, T, m):
    return 4 * np.pi * (m / (2 * np.pi * const.k * T))**1.5 * \
           v**2 * np.exp(-m * v**2 / (2 * const.k * T))

Unit Conversion

# scipy.constants has conversion factors
from scipy.constants import eV, atm, calorie, mile, inch

# Common conversions
def eV_to_J(energy_eV): return energy_eV * eV
def J_to_eV(energy_J): return energy_J / eV
def celsius_to_kelvin(T_C): return T_C + 273.15
def atm_to_Pa(P_atm): return P_atm * atm

Problem-Solving Framework

1. Identify the physical system and relevant principles 2. Draw a diagram (describe it textually) 3. List knowns and unknowns 4. Choose appropriate equations/laws 5. Solve symbolically first (SymPy), then substitute numbers 6. Check units, limiting cases, and order of magnitude 7. Interpret the result physically

Tips

  • Always carry units through calculations
  • Check dimensional consistency
  • Verify with limiting cases (e.g., v << c for classical limit)
  • Use SymPy for symbolic derivations, SciPy for numerical
  • For complex simulations, consider specialized tools (COMSOL, OpenFOAM)

Related skills

FAQ

What libraries does this skill use?

SymPy for symbolic derivations and SciPy (including scipy.constants) for numerical computation.

What is its solution framework?

A 7-step process: identify, diagram, list knowns, choose equations, solve symbolically then numerically, check, and interpret.

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