Lists multiple specific concrete actions and model classes: OLS, GLM, mixed models, ARIMA, along with specific outputs like detailed diagnostics, residuals, inference, and coefficient tables.

Clearly answers both 'what' (statistical models library with specific model classes and diagnostics) and 'when' ('Use when you need specific model classes... with detailed diagnostics'). Also includes explicit disambiguation guidance ('For guided statistical test selection with APA reporting use statistical-analysis').

Includes strong natural keywords users would say: 'OLS', 'GLM', 'mixed models', 'ARIMA', 'econometrics', 'time series', 'inference', 'coefficient tables', 'residuals', 'diagnostics'. These are terms a user working in statistics would naturally use.

Clearly distinguishes itself from a related 'statistical-analysis' skill by specifying its niche (specific model classes, econometrics, time series, rigorous inference) and explicitly directing users to the other skill for APA reporting and guided test selection.

Extremely verbose at ~400+ lines. Massive lists of model types, features, and capabilities that Claude already knows. The 'Core Statistical Modeling Capabilities' section is essentially a reformatted documentation index. The 'When to Use This Skill' bullet list, best practices, common pitfalls (15 items), and workflow descriptions are largely things Claude already understands about statistics. Much of this could be cut by 60-70%.

The Quick Start Guide provides fully executable, copy-paste ready code examples for OLS, Logistic Regression, ARIMA, and GLM. The Formula API section, model comparison code, and cross-validation examples are all concrete and runnable. Key patterns like sm.add_constant() are demonstrated clearly.

The four common workflows (Linear Regression, Binary Classification, Count Data, Time Series) list clear sequences of steps, but they lack explicit validation checkpoints and feedback loops. Steps like 'Check residual diagnostics' don't specify what to do if diagnostics fail. The GLM example does show an overdispersion check with a conditional branch, which is good, but the workflow sections themselves are abstract numbered lists without validation gates.

References to five separate reference files (linear_models.md, glm.md, discrete_choice.md, time_series.md, stats_diagnostics.md) are well-signaled and one level deep. However, no bundle files were provided, so these references may be broken. More importantly, the SKILL.md itself contains enormous amounts of content that should be in those reference files rather than inline — the 'Core Statistical Modeling Capabilities' section alone is a wall of categorized lists that belongs in references.