Skip to content

kalarislabs/research-agent-skills

v1.1.1MIT

281 Agent Skills for researchers: scientific and research paper writing, journal formats, literature review, citations, data science, ML research and domain science.

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.

Version
1.1
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.
Read SKILL.md at the source

Pinned to revision df088027ff23, so it is the text this page describes rather than whatever the author pushed since.

Pre-approved tools experimental

Experimental field. Support varies between clients, so this list is what the author declared, not what your client will enforce.

  • Read
  • Write
  • Edit
  • Bash

Files

Every link opens the file at its source, pinned to the revision this page describes.