Conciseness

The ~600-line body inlines large enumerations Claude already knows about this well-known library — distribution families, link functions, model-class lists ('OLS, WLS, GLS, GLSAR...'), test-name catalogs (Ljung-Box, Durbin-Watson, Breusch-Godfrey...), and a padded Overview ('Python's premier library') — much of it duplicated again in the 'Reference Documentation' section and in the five reference files. This matches anchor 2 (noticeably verbose, several unnecessary/padded sections) rather than anchor 1 (no extended hand-holding tutorial prose, and the code blocks are dense) and rather than anchor 3 (the padding is pervasive, not just 'some' over-explanation).

Actionability

Concrete, runnable code for the common cases: OLS with add_constant, summary, prediction intervals, and Breusch-Pagan; Logit with odds ratios, margeff, and AUC; ARIMA with ADF testing, ACF/PACF, and forecast frames; GLM with an overdispersion check that branches to Negative Binomial. Fits anchor 4 (mostly executable, minor gaps) rather than anchor 5 because snippets use undefined placeholders (X_data, y_binary, y_series) and named capabilities like mixed models and VAR get no example at all.

Workflow Clarity

Four numbered workflows with explicit checkpoints and conditional feedback ('Check for overdispersion' → 'If overdispersed, fit Negative Binomial'; 'Test for stationarity' → 'Difference if non-stationary'; 'Refit with robust SEs if needed'). This matches anchor 4 — clear sequence with most checkpoints — rather than anchor 3 (checkpoints are explicit, not merely implied) and falls short of anchor 5 because error-recovery loops for convergence failures and final validation steps are named but not elaborated.

Progressive Disclosure

Five real, one-level-deep reference files, each clearly signposted inline ('See references/linear_models.md for...'), summarized in a Reference Documentation section, and backed by grep navigation patterns. Structure matches anchor 4 rather than anchor 5 because the capability enumerations and the Reference Documentation summaries largely duplicate content already carried by the reference files, inflating SKILL.md unnecessarily.

Specificity

Names concrete capabilities — 'specific model classes (OLS, GLM, mixed models, ARIMA) with detailed diagnostics, residuals, and inference' and 'coefficient tables' — covering the library's main areas with minor gaps (no mention of formula API, model comparison, or named tests). It matches anchor 4 (several specific items, minor gaps) rather than anchor 5 because it is noun-heavy rather than action-verb driven, and rather than anchor 3 because it lists far more than 1-2 concrete actions.

Completeness

Explicitly answers both: what ('Statistical models library for Python... detailed diagnostics, residuals, and inference... coefficient tables') and when ('Use when you need specific model classes (OLS, GLM, mixed models, ARIMA)... Best for econometrics, time series, rigorous inference'). Matches anchor 5 exactly; it even adds routing guidance to the statistical-analysis skill, so it cannot be anchor 4 where 'when' is only weakly specified.

Trigger Term Quality

Includes natural terms users would say: 'OLS, GLM, mixed models, ARIMA', 'econometrics', 'time series', 'residuals'. A few common variations are missing — 'regression', 'logistic', 'forecasting', and 'statistical tests' never appear — so it fits anchor 4 (good coverage, a few natural terms missing) rather than anchor 5's comprehensive synonym coverage.

Distinctiveness Conflict Risk

Clear niche with distinct triggers (specific model-class acronyms like OLS/ARIMA and the library name itself) and an explicit boundary clause ('For guided statistical test selection with APA reporting use statistical-analysis') that minimizes overlap risk — matching anchor 5 rather than anchor 4's 'minor overlap risk'.