numerical-methods-guide
Apply numerical methods and scientific computing techniques
Profile →npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill numerical-methods-guide --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.
# Numerical Methods Guide A skill for applying numerical methods in scientific computing and research. Covers root finding, numerical integration, ODE solvers, optimization, interpolation, and error analysis with practical implementations in Python. ## Root Finding ### Newton's Method and Alternatives ```python import numpy as np def newton_method(f, df, x0: float, tol: float = 1e-10, max_iter: int = 100) -> dict: """ Newton's method for finding roots of f(x) = 0. Args: f: Function whose root we seek df: Derivative of f x0: Initial guess tol: Convergence tolerance max_iter: Maximum iterations """ x = x0 history = [x] for i in range(max_iter): fx = f(x) dfx = df(x) if abs(dfx) < 1e-15: return {"root": x, "converged": False, "reason": "Zero derivative encountered"} x_new = x - fx / dfx history.append(x_new) if abs(x_new - x) < tol: return { "root": x_new, "converged": True, "iterations": i + 1, "f_at_root": f(x_new), "convergence": "quadratic" } x = x_new return {"root": x, "converged": False, "reason": "Max iterations reached"} ``` ### Method Selection Guide | Method | Convergence | Requires | Robustness | |--------|------------|----------|-----------| | Bisection | Linear (slow) |
- Root Finding
- Newton's Method and Alternatives
- Method Selection Guide
- Numerical Integration
- Quadrature Methods
- Ordinary Differential Equations
- Solving Initial Value Problems
- Solver Selection
- Optimization
- Minimization Methods
- Error Analysis
- Sources of Numerical Error
What does the numerical-methods-guide skill do?
Apply numerical methods and scientific computing techniques
How do I install it?
Run `npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill numerical-methods-guide --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 brycewang-stanford/Auto-Empirical-Research-Skills, a repository with 3,244 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.