Agent skill · research science · k-dense-ai
uncertainty-and-units
Track physical units and propagate measurement uncertainty in scientific calculations using pint and uncertainties. Use for unit conversion and dimensional checking, GUM uncertainty budgets, Type A and Type B evaluation, coverage factors and expanded uncertainty, Monte Carlo propagation, significant-figure and plus-minus reporting, error propagation through curve fits, CODATA constants, auditing Python code for stripped units or broken uncertainty propagation, and order-of-magnitude plausibility checks using dimensionless groups (Reynolds, Peclet, Damkohler, Knudsen, Biot, Womersley), characteristic scales such as diffusion time or Debye length, and observed magnitude ranges. Trigger on "is this number physically reasonable", "sanity check these units", "what regime is this flow in", or a result that looks off by orders of magnitude.
Why this skill is useful
Provides domain-specific knowledge on measurement uncertainty propagation and unit conversion that the AI wouldn't reliably generate on its own.
What it needs
Requires numpy, pint, scipy, uncertainties installed locally. About 11k tokens when loaded. Last updated 2026-08-03. 32,849 stars on the source repository.
What this skill does
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 2026-07-26: pint 0.25.3, released 2026-03-19; requires Python 3.11+. uncertainties 3.2.3, released 2025-04-21; requires Python 3.8+. NumPy 2.5.1 and SciPy 1.18.0; both require Python 3.12+. scipy.constants in SciPy 1.18.0 serves CODATA 2022. SciPy 1.11 and earlier served CODATA 2018, and several recommended values differ between them. 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 mas("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. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills k-dense-ai/uncertainty-and-units