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), characte
npx skills add K-Dense-AI/scientific-agent-skills --skill uncertainty-and-units --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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`. ## Cu
- Scope
- Current release and installation
- Non-negotiable workflow
- The failures this skill exists to prevent
- A unit stripped at an unknown scale
- Offset temperature arithmetic
- Logarithmic units that add by multiplying
- A correlation destroyed by a round trip
- A covariance matrix silently rescaled
- A linearization that was never checked
- Bundled local CLIs
- propagateuncertainty.py
- uncertaintybudget.py
- formatresult.py
uv venv --python 3.13 source .venv/bin/activate uv pip install "pint==0.25.3" "uncertainties==3.2.3" "numpy==2.5.1" "scipy==1.18.0" 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 python skills/uncertainty-and-units/scripts/propagate_uncertainty.py \
What does the uncertainty-and-units skill do?
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), characte
How do I install it?
Run `npx skills add K-Dense-AI/scientific-agent-skills --skill uncertainty-and-units --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From K-Dense-AI/scientific-agent-skills, a repository with 32,619 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.