Conciseness

The step-by-step guidance is dense and practical, but the same pycanon GitHub URL is repeated inline roughly six times and the ARX URL four times instead of being cited once, and the Overview gives textbook definitions of l-diversity/t-closeness that Claude already knows. Not 2 because there is no padded hand-holding beyond these; not 4 because the repeated identical citations are a clear, recurring tightening opportunity.

Actionability

Copy-paste ready content throughout: pip install commands, executable Step 3/Step 5 Python using real pycanon signatures, a concrete qi-policy.yaml, a complete gate script and GitHub Actions workflow in references/ci-gate.md, ARX Java code in references/arx-api.md, and a worked example with real numbers (k=14, l=4, t=0.17). Covers the common verification path end to end.

Workflow Clarity

Eight clearly sequenced steps with a numbered 'How to use' summary, explicit validation (Step 4 threshold comparison, Step 6 non-zero-exit CI gate), an explicit feedback loop ('When a dataset fails, re-mask or generalise with ARX... and re-verify'), and a risk-tier table plus worked example showing the fail/re-mask/pass cycle. This is exactly the validate -> fix -> retry pattern the rubric rewards for gating/batch operations.

Progressive Disclosure

Good structure: two real, one-level-deep reference files clearly signaled with descriptive links and summarized in a References section, with detail (CI workflow, ARX API) appropriately split out. Not 5 because the body names a bundle file 'scripts/k_anonymity_gate.py' that does not exist in the bundle (no scripts/ directory; the code is only inline in references/ci-gate.md), a minor navigation/organization gap.

Specificity

"Computes equivalence classes over chosen quasi-identifiers with pycanon or ARX", "reports re-identification risk", and "gates CI" are multiple concrete, distinct actions covering the skill's full scope (measure, report, enforce). Not 4 because coverage is comprehensive for this domain rather than having minor gaps.

Completeness

Explicitly answers both: what ("Computes equivalence classes... reports re-identification risk and gates CI") and when ("Use when a masked dataset must be proven to meet a k-anonymity, l-diversity or t-closeness threshold before it reaches a non-production environment"). Concrete trigger phrasing with a clear what/when split; third-person voice throughout.

Trigger Term Quality

Natural terms a privacy engineer would say are present: "k-anonymity", "l-diversity", "t-closeness", "masked dataset", "re-identification risk", "threshold". Not 5 because common synonyms like "de-identified"/"anonymized dataset" or "de-identification" are absent; not 3 because the domain's natural vocabulary is well covered, far beyond 'some relevant keywords'.

Distinctiveness Conflict Risk

A clear niche (post-masking verification of stored datasets) with distinct triggers ("masked dataset", the three privacy models, "gates CI") that do not collide with adjacent skills like PII detection or masking-pipeline construction. Minimal conflict risk.