Calculator

Skill skill-commons/skill-commons/skills/calculator

Git-first hub for open, reusable research Agent Skills

Install

npx -y skills add skill-commons/skill-commons --skill calculator

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

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What its author says it does

Copied from the file, not written here

Exact symbolic + numeric math with sympy/mpmath — derive formulas, evaluate constants, propagate errors, convert units. Use for ANY multi-step arithmetic or algebra instead of mental math.

The file declares its own license as MIT. That is the author’s claim about this one file, and it is not the same thing as the license GitHub reports for the repository, which is listed with the other numbers below.

SKILL.md

2.9 KB, as published. Nobody here has run it

Calculator — sympy/mpmath for honest math

When to Use

EVERY time a task involves a formula, a derivation, a unit conversion, or any arithmetic beyond a single trivial operation. The rule from your SOUL: never hand-evaluate formulas — mental algebra and mental arithmetic both fabricate precision. Derive symbolically, evaluate numerically, and quote exactly what the code printed.

Create an isolated workspace environment instead of assuming that an agent runtime preinstalls the libraries:

python3.12 -m venv .venv
.venv/bin/python -m pip install \
  sympy==1.14.0 mpmath==1.3.0 astropy==8.0.1

Run the examples with .venv/bin/python. The published lock was exercised with CPython 3.12 on macOS ARM64; do not modify a system Python implicitly.

Core recipe

import sympy as sp

# 1. Declare symbols and the formula SYMBOLICALLY
M, t = sp.symbols("M t", positive=True)
t_ms = 10 * (M) ** sp.Rational(-5, 2)   # main-sequence lifetime in Gyr, M in Msun

# 2. Manipulate symbolically if needed (solve, diff, integrate, simplify)
M_of_t = sp.solve(sp.Eq(t, t_ms), M)[0]

# 3. Substitute numbers LAST, evaluate with evalf — quote THIS output
print(t_ms.subs(M, 1.1).evalf())        # -> 7.879856... Gyr (NOT a guessed 3.6!)
print(M_of_t.subs(t, 4.0).evalf())      # mass whose t_MS = 4 Gyr

Error propagation (do this instead of hand-waving uncertainties)

import sympy as sp
x, y, sx, sy = sp.symbols("x y sigma_x sigma_y", positive=True)
f = x * y**2
sigma_f = sp.sqrt((sp.diff(f, x) * sx) ** 2 + (sp.diff(f, y) * sy) ** 2)
print(sp.simplify(sigma_f / f))          # relative error, exact
print(sigma_f.subs({x: 3.2, y: 1.7, sx: 0.1, sy: 0.05}).evalf())

Units & constants — astropy, not memory

from astropy import units as u, constants as c
E = (c.G * u.Msun**2 / u.Rsun).to(u.erg)       # gravitational self-energy scale
v = (500 * u.km / u.s).to(u.pc / u.Myr)        # unit conversion
print(E, v)

Never recall constants from memory when astropy.constants has them.

High precision / special functions — mpmath

import mpmath as mp
mp.mp.dps = 50                       # 50 significant digits
print(mp.quad(lambda x: mp.exp(-x**2), [0, mp.inf]))   # sqrt(pi)/2 to 50 digits

Honesty contract

The number you report MUST be a number your code printed in this session.
If symbolic and numeric paths disagree, or a result surprises you, show both and say so — do not silently pick the one that matches expectations.
Keep the derivation script in the task's folder (e.g. analysis/derive_age.py) so the user can rerun it.