Conciseness

The ~660-line body duplicates whole topics: AE-severity guidance appears nearly verbatim in "Clinical trial AE analysis" and again in "Analysis conventions"; spline guidance is repeated across "PRIMARY SCRIPTS", "CRITICAL" item 3, "Bundled Scripts", and the strain co-culture section; the expression-ANOVA rules and their script invocations are likewise given twice. It also explains concepts Claude already knows ("OR = 1.0 means no association", "HR = 2.0 means twice the instantaneous risk", "Lower [AIC] is better"). This is noticeably verbose with several padded/duplicated sections — the 2 anchor — rather than 3, because the duplication alone would cut the file by roughly a third without losing information.

Actionability

Nearly every guidance block is copy-paste executable: full CLI invocations with flags for each bundled script (e.g. the logistic_regression_or.py call with --outcome-order, --encode-map, --interaction), the tu run command, executable Python snippets, and paired ❌ WRONG / ✅ RIGHT examples covering the common failure modes. This matches the fully-executable anchor; not 4 because there are no material gaps — even fallbacks (stat_tests.py for no-scipy environments) are covered.

Workflow Clarity

There is a clear sequence (RULE ZERO pre-computed-results check → primary scripts → Phase 0 data validation → Phase 1 model fitting → Phase 2 diagnostics → Phase 3 interpretation) with genuine checkpoints: Phase 0 outcome-variable verification, sanity heuristics that act as feedback loops ("F > 50 ... means you aggregated"), and a final Completeness Checklist. It falls short of 5 because the workflow is split across four competing entry-point sections (RULE ZERO, PRIMARY SCRIPTS, CRITICAL, Workflow) with overlapping instructions, leaving some ambiguity about which path governs, and the diagnostics phase defers detail to a missing reference file.

Progressive Disclosure

The six references/*.md files cited in the body do exist and are one level deep, but the body also cites files absent from the bundle — anova_and_tests.md, common_patterns_summary.md, TOOLS_REFERENCE.md, QUICK_START.md, EXAMPLES.md, and test_skill.py (all listed in the File Structure map) — so several navigation pointers dead-end. Meanwhile ~200 lines of repeated analysis conventions remain inlined in SKILL.md where they belong in a reference file. This fits the 3 anchor (structure present, but content that should be separate is inline and the reference set is partly broken); not 4 because of the missing-file references, and not 2 because the existing references are clearly signaled and the file is well sectioned.

Specificity

The description enumerates many concrete capabilities — "linear/logistic/ordinal/Poisson regression, ANOVA, Kruskal-Wallis, chi-square, Mann-Whitney, Cox survival, spline fits (R ns()), odds ratios, Cohen's d, F-statistic, p-value computation" — plus a concrete specialization ("clinical-trial AE analysis (SDTM DM/AE), severity ordinal regression"). This matches the comprehensive-coverage anchor; it is not a 4 because there are no meaningful gaps in the method list relative to the skill's actual scope.

Completeness

The "what" is clearly answered (a full method inventory), but there is no "Use when..." clause or equivalent explicit trigger guidance — "Specializes in clinical-trial AE analysis" describes a focus area, not when to invoke the skill. Per the judging guideline, a missing 'Use when' clause caps completeness at 3; it is above 2 because the what-half is explicit and detailed.

Trigger Term Quality

Strong natural terms a user would actually say ("odds ratio", "p-value", "ANOVA", "chi-square", "Cox survival", "Cohen's d", "spline"), but common phrasings like "hazard ratio", "t-test", "sample size / power analysis", and "Kaplan-Meier" are absent. Good coverage with a few natural terms missing fits the 4 anchor; not 5 because those frequently-used synonyms are missing, not 3 because the included keywords are varied and user-facing rather than generic.

Distinctiveness Conflict Risk

The SDTM DM/AE, severity-ordinal-regression, and R-ns() spline niche makes it mostly distinct from generic data-analysis or bioinformatics skills. Minor overlap risk remains with any general statistics skill (ANOVA, regression, p-value are broad triggers); not 5 because those generic method names would also plausibly fire a rival statistics skill.