Cognitive prosthetics for unit-aware computation. Use Pint for converting between units, performing unit arithmetic, checking dimensional compatibility, and simplifying compound units.
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionpint-computeExecute the skills CLI command in your project's root directory to begin installation:
Fetches pint-compute from parcadei/continuous-claude-v3 and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate pint-compute. Access via /pint-compute in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
Submit your Claude Code skill and start earning
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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Cognitive prosthetics for unit-aware computation. Use Pint for converting between units, performing unit arithmetic, checking dimensional compatibility, and simplifying compound units.
| 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" |
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 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
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 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 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"
| 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 |
All commands return JSON with relevant fields:
{
"result": "16.4042 foot",
"magnitude": 16.4042,
"units": "foot",
"dimensionality": "[length]",
"latex": "16.4042\\,\\mathrm{ft}"
}
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]'
Make data-driven prioritization decisions faster
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
parcadei/continuous-claude-v3
mattpocock/skills
cursor/plugins
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
mattpocock/skills
Keeps context tight: pint-compute is the kind of skill you can hand to a new teammate without a long onboarding doc.
We added pint-compute from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Solid pick for teams standardizing on skills: pint-compute is focused, and the summary matches what you get after install.
pint-compute is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Registry listing for pint-compute matched our evaluation — installs cleanly and behaves as described in the markdown.
pint-compute reduced setup friction for our internal harness; good balance of opinion and flexibility.
Keeps context tight: pint-compute is the kind of skill you can hand to a new teammate without a long onboarding doc.
pint-compute is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Solid pick for teams standardizing on skills: pint-compute is focused, and the summary matches what you get after install.
We added pint-compute from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
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