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pint-compute▌

parcadei/continuous-claude-v3 · updated Apr 8, 2026

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$npx skills add https://github.com/parcadei/continuous-claude-v3 --skill pint-compute
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summary

Cognitive prosthetics for unit-aware computation. Use Pint for converting between units, performing unit arithmetic, checking dimensional compatibility, and simplifying compound units.

skill.md

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
how to use pint-compute

How to use pint-compute on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • ›Cursor installed and configured on your development machine
  • ›Node.js version 16.0+ with npm package manager (verify with node --version)
  • ›Active project directory or workspace where you want to add pint-compute
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/parcadei/continuous-claude-v3 --skill pint-compute

The skills CLI fetches pint-compute from GitHub repository parcadei/continuous-claude-v3 and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/pint-compute

Reload or restart Cursor to activate pint-compute. Access the skill through slash commands (e.g., /pint-compute) or your agent's skill management interface.

⚠

Security & Verification Notice

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 development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

Additional Resources

›View source code on GitHub›Skills CLI documentation›Learn more about Cursor›What are agent skills?

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Use Cases▌

User Story & Requirements Generation

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

Competitive Analysis

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

Roadmap Prioritization

Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs

Example

Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale

✓

Make data-driven prioritization decisions faster

Stakeholder Communication

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

Implementation Guide▌

Prerequisites

  • ›Claude Desktop or compatible AI client
  • ›Access to product documentation and roadmap tools (Jira, Notion, etc.)
  • ›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
  • ›Stakeholder contact information and communication channels

Time Estimate

30-60 minutes to see productivity improvements

Installation Steps

  1. 1.Install product management skill
  2. 2.Start with user story generation for known feature
  3. 3.Progress to competitive analysis: research 2-3 competitors
  4. 4.Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5.Draft stakeholder communications and refine based on feedback
  6. 6.Build template library for recurring PM tasks
  7. 7.Share effective prompts with product team

Common Pitfalls

  • ⚠Not validating competitive research—verify facts before sharing
  • ⚠Accepting user stories without involving engineering team
  • ⚠Over-relying on frameworks without qualitative judgment
  • ⚠Not customizing outputs to company culture and communication style
  • ⚠Skipping stakeholder validation of generated requirements

Best Practices▌

✓ Do

  • +Validate research and competitive analysis with real data
  • +Collaborate with engineering when generating technical requirements
  • +Customize frameworks and templates to your company context
  • +Use skill for first drafts, refine with stakeholder input
  • +Document successful prompt patterns for PM tasks
  • +Combine AI efficiency with human judgment and intuition

✗ Don't

  • −Don't publish competitive analysis without fact-checking
  • −Don't finalize user stories without engineering review
  • −Don't make prioritization decisions solely on AI scoring
  • −Don't skip customer validation of generated requirements
  • −Don't ignore company-specific context and culture

💡 Pro Tips

  • ★Provide context: company goals, constraints, customer feedback
  • ★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
  • ★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
  • ★Use skill for 70% generation + 30% customization to company needs

When to Use This▌

✓ 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.

Learning Path▌

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Discussion

Product Hunt–style comments (not star reviews)
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general reviews

Ratings

4.4★★★★★70 reviews
  • ★★★★★Diya Choi· Dec 28, 2024

    Keeps context tight: pint-compute is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • ★★★★★Ishan Okafor· Dec 20, 2024

    We added pint-compute from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • ★★★★★Ishan Abebe· Dec 20, 2024

    Solid pick for teams standardizing on skills: pint-compute is focused, and the summary matches what you get after install.

  • ★★★★★Hassan Choi· Dec 16, 2024

    pint-compute is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • ★★★★★Mei Taylor· Dec 16, 2024

    Registry listing for pint-compute matched our evaluation — installs cleanly and behaves as described in the markdown.

  • ★★★★★Li Thompson· Dec 12, 2024

    pint-compute reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • ★★★★★Olivia Sanchez· Nov 23, 2024

    Keeps context tight: pint-compute is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • ★★★★★Henry Nasser· Nov 15, 2024

    pint-compute is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • ★★★★★Aanya Chen· Nov 11, 2024

    Solid pick for teams standardizing on skills: pint-compute is focused, and the summary matches what you get after install.

  • ★★★★★Charlotte Gupta· Nov 11, 2024

    We added pint-compute from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

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