uncertainty-and-units – skill Marketing & SEO cho agent AI

Uncertainty and units

Scope

Use this skill whenever a calculation carries physical units or a reported number needs

an uncertainty. Concretely:

• converting between units, including conversions that need a physical context

(wavelength to photon energy, mass to amount of substance, energy to temperature);

• propagating uncertainty through a measurement model, with or without correlated inputs;
• building a GUM uncertainty budget from calibration certificates, specifications, and

repeatability data;

• choosing a coverage factor and deciding whether k = 2 is defensible;
• rounding and writing a result so a reader knows what the ± means;
• extracting parameter uncertainties from a curve fit without discarding correlations;
• reviewing existing analysis code for silent unit and uncertainty defects;
• checking that a dimensionally consistent answer is also physically possible — the

order of magnitude, the dimensionless group, and the regime it implies.

This skill covers the metrology and the two libraries that implement it. It does not

cover statistical inference, model selection, or study design — see statistical-analysis,

statistical-power, and experimental-design.

Current release and installation

Verified 2026-10-01:

• pint 0.26.1, released 2026-09-10; requires Python 3.12+.
• uncertainties 3.2.3, released 2025-04-21; requires Python 3.8+.
• NumPy 2.5.3 and SciPy 1.18.1; both require Python 3.12+.
• scipy.constants in SciPy 1.18.1 serves CODATA 2022. SciPy 1.11 and earlier

served CODATA 2018; the switch to CODATA 2022 occurred in SciPy 1.15.

uv venv --python 3.13
source .venv/bin/activate
uv pip install "pint==0.26.1" "uncertainties==3.2.3" "numpy==2.5.3" "scipy==1.18.1"

pint-pandas and pint-xarray add unit-aware columns and arrays and are separate

installs.

Non-negotiable workflow

1. Attach units at input and strip them only at output. Convert at function

boundaries with ureg.wraps or m_as("unit"), never mid-calculation.

2. Write the measurement model explicitly before computing anything, including

corrections whose estimated value is zero. A correction left out of the model leaves

its uncertainty out of the budget.

3. Give every input four things: an estimate, a standard uncertainty, the

distribution the uncertainty came from, and its degrees of freedom.

4. Convert Type B statements with the right divisor. A certificate's expanded

uncertainty divides by its stated k; rectangular limits divide by sqrt(3).

5. Identify correlations before combining. A shared calibration, instrument correction, or fit can induce correlation;

quantify shared components instead of assuming every pair is correlated.

6. Compute sensitivity coefficients, and read the budget from c_i * u(x_i) rather

than from the raw uncertainties.

7. Check the linearization. Compare propagated distributions with the GUM interval when in doubt.

Establish Monte Carlo numerical stability before a JCGM 101 clause 8 claim;

neither method validates the measurement model or the assigned input PDFs.

8. Justify coverage. A Student-t factor from effective degrees of freedom

is an approximation requiring suitable distributional assumptions.

9. Round the uncertainty first, then the value to the same decimal place.

10. State what the ± is — standard or expanded, with k, the coverage probability,

and the method.

11. Sanity-check the magnitude before reporting. A dimensionally consistent result can

still be impossible. Compare it against a known scale or a dimensionless group, and

confirm every assumption you relied on still holds in that regime.

The failures this skill exists to prevent

Each of the following runs without error and produces a plausible number.

A unit stripped at an unknown scale

length = (12.7 * ureg.mm).magnitude          # 12.7 -- of what?
length = (12.7 * ureg.mm).m_as("m")          # 0.0127 metres, stated

.magnitude returns whatever the quantity happened to be carrying. Name the unit at the

point of extraction, every time.

Offset temperature arithmetic

Q(20, "degC") + Q(5, "degC")     # OffsetUnitCalculusError -- correctly refused
Q(20, "degC") + Q(5, "delta_degC")   # 25 degree_Celsius
Q(25, "degC") - Q(20, "degC")        # 5 delta_degree_Celsius

Celsius and Fahrenheit are interval scales. An uncertainty on a temperature is always a

difference and belongs in a delta_ unit: converting 20 ± 0.5 degC to Fahrenheit

gives 68 degF ± 0.9 delta_degF, two different conversions on one line.

Logarithmic units that add by multiplying

Q(10, "dBm") + Q(10, "dBm")   # 0.0001 kilogram**2 * meter**4 / second**6

That is 10 mW × 10 mW, not 20 mW and not 13 dBm. Nothing raises. Convert to a linear

unit before any arithmetic.

A correlation destroyed by a round trip

x = ufloat(1.0, 0.1)
x - x                                     # 0.0+/-0
x - ufloat(x.nominal_value, x.std_dev)    # 0.00+/-0.14

Rebuilding a variable from its nominal value and standard deviation creates an

independent variable. So does any serialization that passes through a pair of floats.

Use correlated_values(values, covariance_matrix) to rebuild a correlated set.

A covariance matrix silently rescaled

popt, pcov = curve_fit(f, x, y, sigma=sigma)                        # default
popt, pcov = curve_fit(f, x, y, sigma=sigma, absolute_sigma=True)

The default rescales pcov by the reduced chi-square. Relative weights from sigma still affect the fit and covariance: equality with an unweighted fit holds for constant sigma, not generally for heteroscedastic inputs. On

one synthetic straight-line fit the two give [0.0364, 0.2154] and [0.0477, 0.2820] —

a 31% difference. Pass absolute_sigma=True whenever sigma holds real standard

uncertainties.

A linearization that was never checked

For y = x² with x = 1.0 ± 0.5, the GUM framework gives y = 1.0, u_c = 1.0, and a

95% interval of [-0.96, 2.96] — mostly negative, for a squared quantity. Monte Carlo

gives a mean of 1.25, u_c = 1.06, and a shortest 95% interval of [0, 3.32]. Nothing

in a linear-propagation library will tell you this happened.

Bundled local CLIs

All helpers run offline, reject URLs and symlinks, bound their inputs, write output

atomically with private permissions, and refuse to overwrite without --force.

python skills/uncertainty-and-units/scripts/propagate_uncertainty.py --help
python skills/uncertainty-and-units/scripts/uncertainty_budget.py --help
python skills/uncertainty-and-units/scripts/format_result.py --help
python skills/uncertainty-and-units/scripts/convert_units.py --help
python skills/uncertainty-and-units/scripts/audit_units.py --help
python skills/uncertainty-and-units/scripts/check_plausibility.py --help

propagate_uncertainty.py

Runs both propagation methods on the same numerical model and compares interval

endpoints. This fixed-trial diagnostic does not implement the adaptive stabilization

in JCGM 101 7.9/8.2 and never labels the GUM result fully validated.

python skills/uncertainty-and-units/scripts/propagate_uncertainty.py \
  --expression "m / (pi * (d / 2) ** 2 * h)" \
  --variable "m=250.0,0.05" \
  --variable "d=2.0,0.002,rectangular" \
  --variable "h=4.0,0.005,rectangular" \
  --measurand density --unit "g/cm3" --format markdown

Each --variable is name=value,standard_uncertainty[,distribution[,dof]], where the

distribution is normal, rectangular, triangular, arcsine, or exact and controls

Monte Carlo sampling only. Here mass is in g and lengths in cm: --unit is

a report label, not dimensional validation or conversion. JSON variable units are

also labels; convert inputs to a consistent numerical model first. Correlations go in as --correlation "a,b=0.9". A JSON

--spec file holds the same model for anything long-lived.

The joint normal sampler supports positive semidefinite correlation matrices, including

perfect correlations. Other joint distributions need a separate sampler. Correlated

finite-dof inputs are refused because independent-input Welch-Satterthwaite is invalid.

For independent finite-dof inputs the CLI's MC PDFs remain fixed: dof affects the

GUM factor only, not MC sampling. A small-sample t input needs a separate justified model.

The expression is parsed into an abstract syntax tree and reduced by an explicit walk

over + - * / ** and a fixed list of functions. It is never compiled or executed.

The report gives the estimate, u_c, sensitivity coefficients, the budget in percent,

effective degrees of freedom, k, U, both Monte Carlo coverage intervals, and the

endpoint-agreement diagnostic. Repeat/increase sampling and verify model/PDF

assumptions before deciding what to report.

uncertainty_budget.py

Combines components stated

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