Conciseness

The body is procedural and assumes competence — no SHAP theory tutorial, legacy details delegated to references/migration.md — but the version-provenance paragraph ('checked on Python 3.12.10 with SHAP 0.52.0, NumPy 2.5.3, pandas 3.0.6, scikit-learn 1.9.1, and matplotlib 3.11.2') and the lengthy citation-fetch instructions could be trimmed or moved. Not 5: these sections exceed 'every token earns its place'; not 3: the bulk is genuinely lean with only minor over-explanation.

Actionability

Fully executable, copy-paste-ready guidance: a complete end-to-end example with an additivity assertion (np.testing.assert_allclose against predict_proba), pinned install commands, explicit masker construction, and a runnable bundled script via 'uv run ... scripts/tabular_report.py'. Not 4: the code covers the common tabular case completely with no gaps.

Workflow Clarity

A 7-step sequenced Standard Workflow with explicit validation checkpoints (the reconstruction check, 'Never silence an additivity failure until input shape, preprocessing, model version, output space, and row ordering have been checked') and a numbered 7-step Troubleshooting Order providing an error-recovery feedback loop. Not 4: validation steps are explicit and built into the workflow, not merely implied.

Progressive Disclosure

Overview-level SKILL.md with a Reference Map table giving 'load when' triggers for all 8 referenced files, all of which exist; references are one level deep (cross-links between reference files are peer-level, not nested chains); a bundled script is clearly documented. Not 4: structure, signaling, and file placement are all clean with no organization gaps observed.

Specificity

Lists multiple concrete actions — 'selecting SHAP explainers and maskers', 'computing and validating feature attributions', 'handling multi-output explanations', 'producing local or global SHAP visualizations' — with comprehensive coverage of the skill's scope. Not 4: there are no minor gaps in coverage; the enumerated actions span selection, computation, validation, and visualization.

Completeness

Explicitly answers both 'what' ('Explain and audit machine-learning predictions with SHAP') and 'when' ('Use for selecting SHAP explainers and maskers, computing and validating feature attributions, ...') with concrete trigger phrases. Not 4: the 'when' clause is explicit and specific, not merely present.

Trigger Term Quality

Natural terms users would say are present ('SHAP', 'explain', 'feature attributions', 'visualizations'), but common synonyms such as 'Shapley values', 'feature importance', or 'interpretability' are missing. Not 5: keyword coverage is good rather than comprehensive; not 3: the core natural phrases users actually use are all there.

Distinctiveness Conflict Risk

Occupies a clear niche (SHAP-based explanation of ML predictions) with distinct, tool-specific triggers; minimal conflict risk with adjacent ML-analysis skills. Not 4: the SHAP-specific framing leaves essentially no overlap ambiguity.