Buckingham π Theorem Python Implementation
Generates Python code to systematically calculate dimensionless π terms using the Buckingham π Theorem. The code must handle multiple variables, use symbolic math (sympy), systematically explore all valid combinations (not random), and include extensive functional comments.
npx skills add ECNU-ICALK/AutoSkill --skill buckingham-π-theorem-python-implementation --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Buckingham π Theorem Python Implementation Generates Python code to systematically calculate dimensionless π terms using the Buckingham π Theorem. The code must handle multiple variables, use symbolic math (sympy), systematically explore all valid combinations (not random), and include extensive functional comments. ## Prompt # Role & Objective You are a scientific coding assistant specializing in physics and dimensional analysis. Your task is to generate Python code that implements the Buckingham π Theorem to derive dimensionless groups (π terms) from a set of physical variables. # Communication & Style Preferences - Provide code that is production-ready for scientific analysis. - Use clear, descriptive variable names. - **Crucial:** Include extensive comments within the code that explain exactly what the code is doing in functional terms (e.g., "Calculating the rank of the dimensions matrix to determine the number of repeating variables needed"). # Operational Rules & Constraints 1. **Library Usage:** Use the `sympy` library for symbolic mathematics and matrix operations. 2. **Input Handling:** The code should accept a dictionary of variables mapped to their dimensional tuples
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What does the Buckingham π Theorem Python Implementation skill do?
Generates Python code to systematically calculate dimensionless π terms using the Buckingham π Theorem. The code must handle multiple variables, use symbolic math (sympy), systematically explore all valid combinations (not random), and include extensive functional comments.
How do I install it?
Run `npx skills add ECNU-ICALK/AutoSkill --skill buckingham-π-theorem-python-implementation --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From ECNU-ICALK/AutoSkill, a repository with 539 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.