Conciseness

The body is efficient — no concept padding, clear Use/Do-Not-Use sections, table-driven argument docs — but the 23-row core-arguments table and the gain/cover/frequency explanations partly duplicate material that belongs in references/cli-guide.md. It is not 5 because some trimming/moving is possible; not 3 because there is no real over-explanation of things Claude already knows.

Actionability

Concrete, executable commands throughout (primary command with placeholders, package install one-liner, three worked examples, validation run with expected output paths). It is not 5 because the example and validation commands reference tests/data/dt_sample*.csv files that are not present in the bundle, so they fail as written; not 3 because the primary workflow runs fine with the user's own data.

Workflow Clarity

A clear 3-step Minimal Workflow, an explicit Validation section with exact expected output files to verify, documented exit behavior ("exits with SKILL_MISSING_INPUT"), and a Common Errors table with a recovery pointer to troubleshooting — explicit checkpoints and an error-recovery loop. It is not 4 because validation checkpoints are fully explicit, not mostly present.

Progressive Disclosure

Good structure with a "Read These Files When Needed" table mapping needs to real, one-level-deep reference files (algorithm.md, cli-guide.md, troubleshooting.md all exist). It is not 5 because the body advertises "Bundled test data" at tests/data/, which does not exist in the actual bundle — a broken navigation pointer.

Specificity

The description lists multiple concrete actions spanning the full pipeline — "building XGBoost models on tabular data", "automatic task detection, train-test split, performance tables, feature importance ranking tables, and PNG importance plots" — with comprehensive coverage and correct third-person voice ("Supports..."). It is not 4 because there are no meaningful gaps in capability coverage.

Completeness

It explicitly answers both questions: an explicit trigger clause ("Use when building XGBoost models on tabular data and returning feature importance ranking outputs") and a concrete what ("Supports binary classification and regression with automatic task detection, train-test split, performance tables..."). It is not 4 because the "when" clause is explicit and specific rather than merely adequate.

Trigger Term Quality

Natural user terms like "XGBoost", "tabular data", "feature importance", "classification", and "regression" are present, but coverage lacks synonyms (e.g., "gradient boosting") and file extensions (e.g., .csv). It is not 5 because the rubric's top anchor expects comprehensive coverage including synonyms and extensions.

Distinctiveness Conflict Risk

The XGBoost-on-tabular-data-with-feature-importance niche is distinct and unlikely to fire for unrelated skills; trigger terms are specific. It is not 4 because the triggers are clearly distinct with minimal overlap risk, not just "mostly distinct".