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 for Python' with specific model classes and outputs) and when ('Use when you need specific model classes... Best for econometrics, time series, rigorous inference'). Also includes explicit disambiguation guidance pointing to statistical-analysis for a different use case.

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 the related 'statistical-analysis' skill by specifying its niche (specific model classes, diagnostics, econometrics) versus guided test selection with APA reporting. The explicit disambiguation clause reduces conflict risk significantly.

Extremely verbose at ~400+ lines. Extensively lists model types, features, and capabilities that Claude already knows (e.g., what OLS is, what Poisson regression does, what AIC/BIC are). The 'Core Statistical Modeling Capabilities' section is essentially a catalog of statsmodels features that adds little actionable value. The 'Common Pitfalls' list has 15 items, many of which are basic statistical knowledge. The 'When to Use This Skill' section and repeated 'When to use' subsections are redundant with the skill description.

The Quick Start Guide provides fully executable, copy-paste ready code examples for OLS, Logistic Regression, ARIMA, and GLM. The Formula API section, Model Selection code, and Cross-Validation examples are all concrete and executable with proper imports and complete code blocks.

The four common workflows (Linear Regression, Binary Classification, Count Data, Time Series) list clear sequential steps, but they are described as numbered prose without validation checkpoints or explicit feedback loops. The GLM quick start example does show a conditional check for overdispersion, which is good, but the formal workflow sections lack explicit 'validate before proceeding' gates.

References to five separate reference files are well-organized and clearly signaled with descriptions and search patterns. However, no bundle files were provided, so these references may not exist. More importantly, the SKILL.md itself contains enormous amounts of content that should be in those reference files (the entire 'Core Statistical Modeling Capabilities' section with its detailed model listings), making the main file a near-monolithic document despite having a reference structure.