The body avoids explaining basic concepts but is lengthy and repeats ideas (source-as-starting-point guidance appears in both Skill Configuration and Workflow step 2; several skill handoffs recur across sections), so it is mostly efficient but could be tightened.

As an instruction-only skill it gives concrete, specific guidance — exact fields to verify (grain, joins, exclusions, freshness, lineage), named diagnostic modes, and precise interpretation rules (numerator vs denominator, mix vs within-segment) — which the rubric rewards for actionable non-code skills.

A clear six-step sequence with explicit checkpoints — 'Do not search for causes until the pattern are verified', the Source Access Guardrail stop-and-ask, iteration stopping rule, and a pre-handoff verification checklist — provides explicit validation and feedback loops.

With no bundle files the body is self-contained and well-sectioned, but the detailed driver-interpretation lists and diagnostic-mode examples are dense inline content that could be split into reference files, leaving it at 'some structure but content that should be separate is inline'.

Names concrete actions ('Diagnose why a metric changed', 'identify likely drivers') across multiple movement types (movement, anomaly, gap, discrepancy), matching the 'lists multiple specific concrete actions' anchor.

Explicitly answers both what ('Diagnose why a metric changed or differs from expectation') and when ('Use when the task is to identify likely drivers...'), with an explicit 'Use when' trigger clause.

Natural analyst terms ('metric changed', 'differs from expectation', 'anomaly', 'gap', 'discrepancy') give good coverage of how users would phrase the need, matching the 'good coverage of natural terms' anchor.

The metric-diagnostics niche and its trigger terms (movement, anomaly, gap, discrepancy) are specific enough to be unlikely to fire for sibling analysis skills.