mathematical-modeling
De la autor
Real-world problem formulation, mathematical abstraction, and applied mathematics for translating between practical problems and mathematical frameworks. Covers the modeling cycle (problem identification, assumptions, formulation, analysis, validation, interpretation), Polya's framework adapted for modeling, common model types (linear, exponential, logistic, periodic, power-law), dimensional analysis (Buckingham Pi theorem), optimization (linear programming, gradient descent, constraint satisfaction), probability models (Markov chains, queuing theory, Monte Carlo simulation), statistical modeling (regression, hypothesis testing, model selection), model criticism (overfitting, underfitting, sensitivity analysis), and real-world case studies. Use when formulating mathematical models, performing dimensional analysis, optimizing systems, running simulations, or evaluating model validity.
Instalare
npx skills add Tibsfox/gsd-skill-creator --skill mathematical-modeling -a claude-code
Se instalează în .claude/skills/mathematical-modeling/ · Fiecare comandă descarcă skill-ul direct de pe GitHub cu utilitarul open source skills.
Fără descărcare: licența acestui skill (Personalizată sau neclară) nu ne permite să-i redistribuim fișierele. Comenzile de instalare le iau direct de pe GitHub.
Vrei ca agentul să-ți propună skill-uri în timp ce scrii? Vezi cum folosești skill-urile din IDE.
Verificări de siguranță pe SKILL.md
Niciun avertisment.
De ce 72
aiqbench calculează scorul de încredere din semnale publice. Nu este un audit de securitate, așa că citește un skill înainte să-l instalezi.
Skill-uri similare
Cele mai apropiate de acesta prin ce fac și când sunt folosite.
- numerical-analysisTibsfox/gsd-skill-creatorCalculus, series, numerical methods, and optimization for computational mathematics. Covers limits (epsilon-delta, L'Hopital, squeeze theorem), derivatives (rules, optimization, related rates), integrals (Riemann sums, Fundamental Theorem of Calculus, integration techniques), series (convergence tests, Taylor and Maclaurin expansions), differential equations (separable, linear, exact), numerical methods (Newton-Raphson, Euler method, Simpson's rule), and computational pitfalls (floating-point arithmetic, catastrophic cancellation, numerical stability). Use when computing derivatives, integrals, limits, series, solving differential equations, or analyzing numerical accuracy.Testare★ 71
- regression-modelingTibsfox/gsd-skill-creatorModeling relationships between variables using regression. Covers simple linear regression, multiple regression, polynomial regression, logistic regression, model fitting (least squares, maximum likelihood), residual analysis, model diagnostics, R-squared, adjusted R-squared, multicollinearity, variable selection, and the Box-Jenkins dictum that all models are wrong but some are useful. Use when predicting outcomes, quantifying relationships, building predictive models, or diagnosing model fit.Altele★ 71
- financial-modelingcbrock84/headcountBuilds and stress-tests financial models for forecasting, scenario planning, and decision support — revenue build, cost structure, driver logic, and the sensitivities that show where a plan breaks. Use this to model a decision's financial consequence, build a forecast or long-range plan, evaluate an investment or hire, or pressure-test someone else's model before relying on it.Finanțe★ 2.1K
- statistical-computingTibsfox/gsd-skill-creatorComputational tools and algorithms for statistical analysis. Covers simulation, resampling methods (bootstrap, permutation tests), Monte Carlo methods, random number generation, numerical optimization (Newton-Raphson, EM algorithm), cross-validation, and reproducible analysis workflows. Emphasizes the bootstrap revolution and the shift from formula-based to computation-based inference. Use when implementing statistical procedures, running simulations, bootstrapping confidence intervals, performing cross-validation, or building reproducible analysis pipelines.Testare★ 71
- math-modelingXiaoMaColtAI/math-modeling-skill当用户要求数学建模、建模竞赛、建模分析、代码求解、结果可视化或生成数学建模论文时使用。支持完整三阶段流程、单独执行任一角色,以及在关键节点使用独立 Subagent 进行阶段内质检;默认只启用质检 Subagent,用户可明确选择额外协作。Altele★ 1.9K
- statistical-analystborghei/Claude-SkillsApplied statistics for business and product questions — test selection, assumption checks, power planning, effect sizes with intervals, multiplicity correction. Use when interpreting an experiment, sizing a study, or vetting a claim.Testare★ 978
Note și recenzii
—
nicio recenzie
Ai încercat acest skill? Notează-l ca să-i ajuți pe alții. Autentifică-te
Discuție
Intră în discuție. Autentifică-te