Lists multiple specific concrete actions: test selection, assumption checking, power analysis, and APA-formatted results. These are distinct, well-defined statistical analysis tasks.

Clearly answers both what (guided statistical analysis with test selection, assumption checking, power analysis, APA-formatted reporting) and when ('Use when you need help choosing appropriate tests for your data'). Also includes a helpful boundary condition distinguishing it from statsmodels for programmatic implementation.

Includes strong natural keywords users would say: 'statistical analysis', 'test selection', 'assumption checking', 'power analysis', 'APA-formatted results', 'academic research reporting'. These are terms researchers and students naturally use.

Clearly carves out a distinct niche focused on guided statistical test selection and academic reporting, and explicitly differentiates itself from programmatic statistical modeling ('For implementing specific models programmatically use statsmodels'). This boundary-setting reduces conflict risk significantly.

The skill is extremely verbose at ~500+ lines. It explains concepts Claude already knows (what effect sizes are, what p-values mean, advantages of Bayesian methods, definitions of common pitfalls like p-hacking). Sections like 'When to Use This Skill,' 'Best Practices,' 'Common Pitfalls,' and 'Key Advantages' of Bayesian methods are largely redundant for Claude. The textbook recommendations and online resources at the end add no value.

The skill provides fully executable Python code examples for t-tests, ANOVA, regression, Bayesian analysis, power analysis, and assumption checking. Code is copy-paste ready with specific library calls (pingouin, statsmodels, pymc) and concrete APA report templates that serve as actionable output examples.

There is a decision tree and a getting-started checklist that outline the workflow sequence, and assumption checking is emphasized before analysis. However, the workflow lacks explicit validation checkpoints with feedback loops—there's no 'if assumption check fails, do X, then re-check' loop built into the main workflow, and the decision tree uses vague section references rather than concrete steps with verification gates.

The skill references external files (references/*.md, scripts/*.py) appropriately, but the main SKILL.md itself contains far too much inline content that should be in those reference files. The test selection guide, full code examples for every test type, effect size tables, and APA report templates could all be in referenced documents, keeping the main file as a concise overview.