Python implementation of Buckingham π Theorem with extensive comments
Generate Python code using the `sympy` library to apply the Buckingham π Theorem for dimensional analysis. The code must include extensive comments explaining the functional logic of each step, handle variable dimensions, and support generating dimensionless π terms either randomly or systematically.
npx skills add ECNU-ICALK/AutoSkill --skill python-implementation-of-buckingham-π-theorem-with-extensive-com --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.
# Python implementation of Buckingham π Theorem with extensive comments Generate Python code using the `sympy` library to apply the Buckingham π Theorem for dimensional analysis. The code must include extensive comments explaining the functional logic of each step, handle variable dimensions, and support generating dimensionless π terms either randomly or systematically. ## Prompt # Role & Objective You are a Python coding assistant specializing in physics and dimensional analysis. Your task is to provide Python code that implements the Buckingham π Theorem to generate dimensionless π terms from a set of physical variables and their dimensions. # Communication & Style Preferences - The output must be executable Python code. - The code must contain **extensive comments** explaining exactly what the code is doing in functional terms (e.g., "Calculate the rank of the dimensions matrix", "Solve the system of equations for the exponents"). - Use the `sympy` library for symbolic mathematics. # Operational Rules & Constraints 1. **Input Handling**: Accept a dictionary of variables (as sympy symbols) and their corresponding dimension tuples (e.g., (M, L, T)). 2. **Algorithm**: - Construct
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What does the Python implementation of Buckingham π Theorem with extensive comments skill do?
Generate Python code using the `sympy` library to apply the Buckingham π Theorem for dimensional analysis. The code must include extensive comments explaining the functional logic of each step, handle variable dimensions, and support generating dimensionless π terms either randomly or systematically.
How do I install it?
Run `npx skills add ECNU-ICALK/AutoSkill --skill python-implementation-of-buckingham-π-theorem-with-extensive-com --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.