Conciseness

Dense with domain-specific knowledge Claude lacks (regulatory citations, exact metric definitions, the fairness-criterion conflict theorem, convention bands, blocking table, evidence rules) with no padding on basics Claude already knows; a few passages (e.g., repeated Fairlearn framing quotes) could be trimmed, so it is efficient but not maximally lean.

Actionability

Provides executable guidance: exact DPD thresholds (0.05/0.10), concrete rule triggers (R1–R6), jq commands, and a full worked output sample; a few steps defer to 'read at the source' rather than giving the inline command, leaving minor gaps.

Workflow Clarity

Steps 1–9 are clearly sequenced, and the Step 9 gating workflow is an explicit ordered checklist with validation checkpoints (re-tier, R1–R6 checks) and refuse-to-promote rules that form a feedback loop, satisfying the validation requirement for a gating/batch operation.

Progressive Disclosure

The body is a well-organized overview that splits detail into two clearly signaled, one-level-deep real references ([references/alibi-explainability.md], [references/worked-example.md]), both verified present, with no nested reference chains.

Specificity

Lists multiple specific concrete actions with comprehensive coverage: 'Assigns an ML model to a low, medium, or high risk tier', 'group metrics per declared sensitive feature, intersectional breakdowns with per-cell counts, vulnerability scan categories, a drift monitoring plan, and per-prediction explanation logs', plus DPD bands, a blocking table, evidence rules, and a gating workflow.

Completeness

Explicitly answers both 'what' (derives the required fairness/explainability evidence and supplies bands, blocking table, rules, and a gating workflow) and 'when' via a concrete 'Use when a model release candidate is up for promotion... or when the evidence review must gate the promotion' clause.

Trigger Term Quality

Good keyword coverage with domain-natural terms ('model card', 'evidence bundle', 'risk tier', 'promote / needs-work / block', 'model release candidate'), but the phrasings are mostly technical and a few natural synonyms a user might say are missing, so it sits below the comprehensive anchor.

Distinctiveness Conflict Risk

Occupies a clear niche (ML fairness/explainability evidence gating for model promotion) with distinct, specific triggers and minimal overlap risk with other skills.