pint-compute▌
parcadei/continuous-claude-v3 · updated Apr 8, 2026
Cognitive prosthetics for unit-aware computation. Use Pint for converting between units, performing unit arithmetic, checking dimensional compatibility, and simplifying compound units.
Unit Computation with Pint
Cognitive prosthetics for unit-aware computation. Use Pint for converting between units, performing unit arithmetic, checking dimensional compatibility, and simplifying compound units.
When to Use
- Converting between units (meters to feet, kg to pounds)
- Unit-aware arithmetic (velocity x time = distance)
- Dimensional analysis (is force = mass x acceleration?)
- Simplifying compound units to base or named units
- Parsing and analyzing quantities with units
Quick Reference
| I want to... | Command | Example |
|---|---|---|
| Convert units | convert |
convert "5 meters" --to feet |
| Unit math | calc |
calc "10 m/s * 5 s" |
| Check dimensions | check |
check newton --against "kg * m / s^2" |
| Parse quantity | parse |
parse "100 km/h" |
| Simplify units | simplify |
simplify "1 kg*m/s^2" |
Commands
parse
Parse a quantity string into magnitude, units, and dimensionality.
uv run python -m runtime.harness scripts/pint_compute.py \
parse "100 km/h"
uv run python -m runtime.harness scripts/pint_compute.py \
parse "9.8 m/s^2"
convert
Convert a quantity to different units.
uv run python -m runtime.harness scripts/pint_compute.py \
convert "5 meters" --to feet
uv run python -m runtime.harness scripts/pint_compute.py \
convert "100 km/h" --to mph
uv run python -m runtime.harness scripts/pint_compute.py \
convert "1 atmosphere" --to pascal
calc
Perform unit-aware arithmetic. Operators must be space-separated.
uv run python -m runtime.harness scripts/pint_compute.py \
calc "5 m * 3 s"
uv run python -m runtime.harness scripts/pint_compute.py \
calc "10 m / 2 s"
uv run python -m runtime.harness scripts/pint_compute.py \
calc "5 meters + 300 cm"
check
Check if two units have compatible dimensions.
uv run python -m runtime.harness scripts/pint_compute.py \
check newton --against "kg * m / s^2"
uv run python -m runtime.harness scripts/pint_compute.py \
check joule --against "kg * m^2 / s^2"
simplify
Simplify compound units to base or compact form.
uv run python -m runtime.harness scripts/pint_compute.py \
simplify "1 kg*m/s^2"
uv run python -m runtime.harness scripts/pint_compute.py \
simplify "1000 m"
Common Unit Domains
| Domain | Examples |
|---|---|
| Length | meter, foot, inch, mile, km, yard |
| Time | second, minute, hour, day, year |
| Mass | kg, gram, pound, ounce, ton |
| Velocity | m/s, km/h, mph, knot |
| Energy | joule, calorie, eV, kWh, BTU |
| Force | newton, pound_force, dyne |
| Temperature | kelvin, celsius, fahrenheit |
| Pressure | pascal, bar, atmosphere, psi |
| Power | watt, horsepower |
Output Format
All commands return JSON with relevant fields:
{
"result": "16.4042 foot",
"magnitude": 16.4042,
"units": "foot",
"dimensionality": "[length]",
"latex": "16.4042\\,\\mathrm{ft}"
}
Error Handling
Dimensionality errors are caught and reported:
# This will error - incompatible dimensions
uv run python -m runtime.harness scripts/pint_compute.py \
convert "5 meters" --to kg
# Error: Cannot convert '[length]' to '[mass]'
Related Skills
- /math-mode - Full math orchestration (SymPy + Z3)
- /sympy-compute - Symbolic computation
Ratings
4.5★★★★★10 reviews- ★★★★★Shikha Mishra· Oct 10, 2024
pint-compute is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Piyush G· Sep 9, 2024
Keeps context tight: pint-compute is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Chaitanya Patil· Aug 8, 2024
Registry listing for pint-compute matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Sakshi Patil· Jul 7, 2024
pint-compute reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Ganesh Mohane· Jun 6, 2024
I recommend pint-compute for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Oshnikdeep· May 5, 2024
Useful defaults in pint-compute — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Dhruvi Jain· Apr 4, 2024
pint-compute has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Rahul Santra· Mar 3, 2024
Solid pick for teams standardizing on skills: pint-compute is focused, and the summary matches what you get after install.
- ★★★★★Pratham Ware· Feb 2, 2024
We added pint-compute from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Yash Thakker· Jan 1, 2024
pint-compute fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.