Conciseness

The body is lean with no concept-padding; the intro, a minimal code snippet, and a reference table all earn their place, assuming Claude's competence. It is above score 2 because there is no unnecessary explanation to trim.

Actionability

Provides fully executable code ("import causalpy as cp; df = cp.load_data(\"did\")") and a concrete table of exact dataset keys, copy-paste ready. It is above score 2 because the guidance is complete and executable rather than pseudocode or vague.

Workflow Clarity

This is a simple single-task skill whose one action (load a dataset by key) is unambiguous, which the rubric's simple_skills note allows to score 3 without a multi-step workflow.

Progressive Disclosure

Under 50 lines with no external bundle files needed, organized into clear sections (Usage, Available Datasets, Guidance) and cross-references to sibling skills; the rubric permits a 3 for compact, well-organized skills with no deep reference nesting.

Specificity

Names the domain and one core action ("Load built-in CausalPy example datasets") with several use contexts, but does not list multiple distinct concrete actions like the score-3 anchor. It is above score 1 because a concrete action and clear domain are present, but below score 3 because it stays at a single action rather than enumerating several.

Completeness

Explicitly states what it does (load built-in example datasets) and includes an explicit "Use when the user needs sample data or asks which demo datasets are available" trigger clause, satisfying both what and when.

Trigger Term Quality

Covers natural terms users would say — "sample data", "demo datasets", "example datasets", and "which demo datasets are available" — matching the good coverage anchor rather than the sparse score-2 example.

Distinctiveness Conflict Risk

Scoped to CausalPy's built-in example datasets with distinct triggers, making it unlikely to fire for unrelated skills.