Conciseness

The body is dense domain guidance with no library tutorials or concept padding, but it is noticeably redundant: 'Core Checks' and 'Specific Check Guidance' cover the same ground twice (duplicates/keys, missingness, freshness/schema drift, distribution shifts appear in both sections), and the five shape-specific check lists could be tightened. This fits the 'mostly efficient but could be tightened' anchor — not 4 because the duplicated check catalogs are more than 'minor instances' of excess, and not 2 because there is no explanatory filler or concept teaching.

Actionability

Although it is instruction-only with no code, the guidance is concretely executable: named checks per category, specifics like sentinel values "'', 'unknown', 'n/a', 0, or -1", robust-method defaults ('quantiles, MAD, or IQR before defaulting to z-scores'), a concrete cross-field rule example ('is_cancelled = false with a non-null cancelled_at'), and a structured output template with per-finding fields. Minor gap versus the 5 anchor: no example SQL/Python snippets or thresholds for the checks, so it is not fully copy-paste ready.

Workflow Clarity

The 8-step workflow is clearly sequenced (context → path → profile → core checks → shape-specific checks → temporal → risks → fixes) with embedded checkpoints ('Confirm grain before interpreting anomalies'), a severity classification scheme, and a 7-part report structure with a per-finding template. Minor validation gaps keep it below 5: there are no explicit feedback loops (e.g., re-check after a suspected cause is confirmed) and checkpoint language is suggestive rather than mandated — but the sequence itself is coherent and gap-free, so it sits above the 3 anchor.

Progressive Disclosure

The file is well-organized with clear headers and clearly signaled cross-skill references ($build-report, $jupyter-notebooks, $validate-data, $design-kpis), but everything lives in a single ~160-line SKILL.md with no reference files: the check catalogs ('Core Checks', 'Specific Check Guidance', 'Automated Test Guidance') are reference-like material that could be split out, and the >50-line simple-skill exception does not apply. This matches the 'some structure but content that should be separate is inline' anchor rather than the 4 anchor, since the bulk of the standards material is inlined rather than 'mostly appropriately placed' in separate files.

Specificity

The description names the domain precisely and lists several concrete actions — 'check data quality, reconcile conflicting sources or metric definitions, or decide whether evidence is safe to cite' — applied to specific object types ('structured data, query results, dashboards, or analytical evidence'). It stops short of comprehensive coverage (no mention of specific check types like nulls, duplicates, freshness), matching the 'several specific actions with minor gaps' anchor rather than the fully comprehensive anchor at 5 or the '1-2 concrete actions' anchor at 3, since three distinct actions plus four target types are enumerated.

Completeness

It explicitly answers both questions: what ('Assess whether structured data, query results, dashboards, or analytical evidence are trustworthy enough to use') and when ('Use when the task is to check data quality, reconcile conflicting sources or metric definitions, or decide whether evidence is safe to cite') with concrete trigger phrases. This matches the top anchor exactly; the 4-anchor ('when' could be more explicit) does not apply since the trigger clause names three specific task situations.

Trigger Term Quality

Natural user phrasings like 'check data quality', 'reconcile conflicting sources', 'dashboards', and 'safe to cite' are present, matching the 'good keyword coverage; a few natural terms missing' anchor. It is not a 5 because common synonyms users would say are absent — e.g., 'data validation', 'QA the data', 'is this data reliable/trustworthy', or file/format-specific terms — and not a 3 because the terms present go well beyond 'some relevant keywords'.

Distinctiveness Conflict Risk

The data-trustworthiness niche is clear with distinct triggers (quality checking, source reconciliation, citation safety), but there is minor overlap risk with closely related skills such as analysis/report QA ('validate a chart or report') or general data analysis, matching the 'mostly distinct; minor overlap risk' anchor rather than the minimal-conflict anchor at 5 or the 'could still overlap' anchor at 3.