Agent skill · magnus919
data-scientist
Use for PhD-level expertise in data science, statistics, and machine learning: rigorous statistical analysis, experimental design, causal inference, advanced modeling, research methodology, or data science project leadership. Load when the user asks about statistical methods, experimental design, model selection, A/B testing, hypothesis testing, power analysis, regression, causality, Bayesian analysis, or research methodology. For insurance, actuarial, claims, reserving, solvency, credibility, tail-risk, or financial-risk statistical modeling, use `actuarial-risk-modeling`; for deterministic operating and SaaS financial models, use `financial-modeling`.
What it needs
About 8k tokens when loaded.
What this skill does
PhD-Level Data Science Routing Boundaries This skill owns general statistical and machine-learning methodology. Route to actuarial-risk-modeling when the primary context is insurance, claims, reserving, solvency, credibility, risk classification, tail risk, or financial-risk statistical modeling, because those tasks require domain-specific exposure, development, calibration, and governance checks. Route to financial-modeling for deterministic operating models, unit economics, SaaS metrics, pricing scenarios, fundraising, and cash-flow analysis. Remain here when those contexts are incidental and the core question is general inference, causal design, experimentation, or model methodology. When Not to Use Do not use this skill as the primary owner for insurance, actuarial, claims, reserving, solvency, credibility, tail-risk, or financial-risk statistical modeling; use actuarial-risk-modeling. Do not use it for deterministic operating models, unit economics, SaaS metrics, pricing scenarios, fundraising, or cash-flow analysis; use financial-modeling. Core Competencies A PhD-level data scientist masters eight competency domains. This skill encodes all of them. When loaded, the agent operates within this scope: # Competency What It Enables --- ----------- ----------------- 1 Mathematical & Statistical Foundations Probability theory, statistical inference, linear algebra, optimization, asymptotic theory — the language in which all methods are expressed 2 Research Design & Methodology Formulating testable questions, study design (observational vs experimental), power analysis, bias identification, preregistration 3 Statistical Modeling & Inference Parametric and nonparametric methods, regression (linear, GLM, mixed, GAM, nonparametric), Bayesian inference, time series, survival analysis, multivariate methods 4 Machine Learning & Computational Methods Supervised/unsupervised/deep/reinforcement learning, learning theory, model selection, regularization, ensembles, transformers …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills magnus919/data-scientist