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. Extensive lists of model types, families, link functions, and features that Claude already knows. The 'When to Use This Skill' section, 'Core Statistical Modeling Capabilities' catalog, and 'Common Pitfalls' list of 15 items are largely redundant with Claude's existing knowledge. Much of this reads like library documentation rather than actionable skill guidance.

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 realistic usage patterns.

The four common workflows (Linear Regression, Binary Classification, Count Data, Time Series) list clear sequential steps and include implicit validation checkpoints (e.g., 'check residual diagnostics', 'test for stationarity'). However, they lack explicit validation gates (no 'only proceed when X passes' language) and no feedback loops for error recovery, which is important for statistical modeling where assumption violations require model changes.

References to five detailed reference files are well-organized and clearly signaled at the bottom. However, the SKILL.md itself contains enormous amounts of inline content that should be in those reference files — the 'Core Statistical Modeling Capabilities' section alone is a massive catalog that duplicates what the references presumably cover. The overview should be much leaner with more content pushed to references.