# uncertainty-and-units

Keeps physical units attached through a calculation and carries measurement uncertainty with pint and uncertainties, then checks that the answer is physically plausible.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · 985 installs · ★ 54652
- Author: K-Dense-AI
- License: MIT · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/K-Dense-AI/scientific-agent-skills/tree/HEAD/skills/uncertainty-and-units/SKILL.md
- Updated: 2026-10-19
- HTML: https://affpapa.org/en/nevibecode/skills/uncertainty-and-units

## What it does

The skill keeps physical units attached from input to output and pushes measurement uncertainty through the whole calculation with `pint` and `uncertainties`. You get conversion, sensitivity coefficients, coverage factors, Monte Carlo propagation, and results written as value ± uncertainty. A separate static auditor reads Python for stripped units and broken correlations.

What it catches: `.magnitude` pulled off a quantity without naming the unit, adding `degC` to `degC`, decibels that multiply instead of add, and a covariance matrix rescaled by `curve_fit` when `absolute_sigma=True` is missing. It also asks whether a dimensionally correct answer is possible at all, using dimensionless groups and characteristic scales. Statistical inference and study design belong to other skills in the same set.

The numeric tools need Python 3.12+ plus `pint`, `uncertainties`, NumPy and SciPy, so set the environment up before asking for calculations; the auditor is standard-library only. Everything runs locally, no network access.

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add K-Dense-AI/scientific-agent-skills --skill uncertainty-and-units -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/uncertainty-and-units/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/uncertainty-and-units/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add K-Dense-AI/scientific-agent-skills --skill uncertainty-and-units -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/uncertainty-and-units/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add K-Dense-AI/scientific-agent-skills --skill uncertainty-and-units -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/uncertainty-and-units/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install K-Dense-AI/scientific-agent-skills uncertainty-and-units
```

2. **Manually: put the files in place**
- File: `.github/skills/uncertainty-and-units/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add K-Dense-AI/scientific-agent-skills --skill uncertainty-and-units -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/K-Dense-AI/scientific-agent-skills --path skills/uncertainty-and-units
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/uncertainty-and-units/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add K-Dense-AI/scientific-agent-skills --skill uncertainty-and-units -a opencode
```

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/K-Dense-AI/scientific-agent-skills/tree/HEAD/skills/uncertainty-and-units/SKILL.md

## When to use it
- convert a result to SI and check the dimensions
- build a GUM uncertainty budget from a calibration certificate
- choose a coverage factor and report expanded uncertainty
- get fit-parameter uncertainties out of `curve_fit` with correlations intact
- catch a number that is off by orders of magnitude

## Example prompts
- Convert this to SI, check the dimensions, and write the result with its uncertainty.
- Build a GUM uncertainty budget from this calibration certificate and tell me whether k = 2 is defensible.
- This result looks off by orders of magnitude — check the units and the dimensionless group.

## Source text · SKILL.md · MIT

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BEGIN THIRD-PARTY FILE b50505dfbe5d944d
```text
---
name: uncertainty-and-units
description: Tracks physical units and propagates 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.
license: MIT
compatibility: Requires Python 3.12+. The numeric CLIs need pint, uncertainties, NumPy, and SciPy; the static auditor is standard-library only. All bundled tooling runs locally with no network access.
allowed-tools: Read Write Edit Bash
metadata:
  version: "1.3"
  last-reviewed: "2026-10-01"
  skill-author: K-Dense Inc.
---

# 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
```
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---
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