Conciseness

The body is dominated by dense, executable code with directive comments rather than explanations of concepts Claude already knows, e.g. the checklists "Never fit transformers on the full dataset — fit on train, transform test". Not 5 because of minor trimmable material: the filler opener "World-class senior data scientist skill for production-grade AI/ML/Data systems." and generic boilerplate commands ("python -m pytest tests/ -v --cov=src/", "python -m black src/ && python -m pylint src/"); not 3 because there is no substantive padding beyond these.

Actionability

All four workflows are complete, copy-paste-ready functions with imports, parameters, and return values (e.g. calculate_sample_size, analyze_experiment, build_feature_pipeline, diff_in_diff), plus concrete CLI invocations with explicit input/output arguments ("--input experiment_spec.json --output experiment_design.json"). This matches the anchor: fully executable code covering the common cases; not 4 because there are no gaps in the code itself.

Workflow Clarity

Each workflow ends with a numbered checklist containing explicit validation checkpoints ("Check for sample ratio mismatch: abs(n_control - n_treatment) / expected < 0.01", "Check overfit_gap > 0.05", "Run a baseline (e.g. DummyClassifier) and verify the model beats it", "Validate parallel trends in pre-period"), and the pipeline section defines an explicit failure feedback loop: "any status other than 'completed' means the stage failed — fix before moving to the next pipeline stage". Not 4 because both checklists for complex processes and error-recovery loops are present, matching the top anchor.

Progressive Disclosure

The "Reference Documentation" section clearly signals three reference files and "Common Commands" invokes three bundled scripts, but none of these exist — there is no references/ or scripts/ directory in the bundle, so all six referenced paths are dead links. Not 4 because missing files entirely is more than "minor organization gaps": navigation is broken and the ~200-line body carries all substantive content monolithically; not 2 because section structure and reference signaling are good, only the targets are absent.

Specificity

The description lists multiple concrete actions with sub-specifics: "Covers A/B testing (sample sizing, two-proportion z-tests, Bonferroni correction), difference-in-differences, feature engineering pipelines (Scikit-learn, XGBoost), cross-validated model evaluation (AUC-ROC, AUC-PR, SHAP), and MLflow experiment tracking" — five action areas, each named with concrete techniques. Not 4 because coverage is comprehensive rather than having minor gaps; the one buzzword opener ("World-class") does not dilute the concrete action list.

Completeness

Both questions are explicitly answered: the "what" via "Covers A/B testing ... difference-in-differences ... MLflow experiment tracking", and the "when" via an explicit trigger clause — "Use when designing or analysing controlled experiments, building and evaluating classification or regression models, performing causal analysis on observational data, engineering features for structured tabular datasets, or translating statistical findings into data-driven business decisions". This matches the anchor-5 example pattern exactly; not 4 because the "when" clause is already explicit and concrete.

Trigger Term Quality

Natural phrases users would say are present: "A/B testing", "experiment design", "classification or regression models", "causal analysis", "structured tabular datasets", "data-driven business decisions". Not 5 because common variations are missing — e.g. "machine learning" / "ML" never appears, nor do "forecasting", "uplift", or tool-name triggers a user might naturally mention; not 3 because keyword coverage is genuinely good with several distinct natural trigger phrases.

Distinctiveness Conflict Risk

The niche is mostly distinct — "two-proportion z-tests, Bonferroni correction", "difference-in-differences", and "causal analysis on observational data" are specialized triggers unlikely to fire for unrelated skills. Not 5 because the field is broad: generic requests like "build a predictive model" or "analyze this dataset" could overlap with general ML-engineering or data-analysis skills; not 3 because the statistical/experimental-design focus keeps overlap minor rather than routine.