Conciseness

The body is dense and directive — no explanations of concepts Claude already knows, tables carry information efficiently — but the Quick Reference table substantially duplicates the per-phase sections that follow it, and a few rows (e.g., "Read: corresponding DTOs → understand available fields") restate obvious steps. This fits 'efficient; minor instances of over-explanation that could be trimmed' rather than the lean every-token-earns-its-place anchor.

Actionability

Guidance is copy-paste ready throughout: concrete Glob/Grep patterns ("Grep: backend/src/modules/analytics/api/"), an executable library audit command ("docker exec -t visionarias_client_dev npm ls 2>/dev/null | grep -iE 'chart|recharts|visx|tremor|nivo|apex'"), specific MCP query templates, and directed search queries ("site:business-api.tiktok.com \"{metric_name}\""). This matches the fully-executable anchor covering common cases; minor placeholders like {timestamp} are parameterization, not gaps.

Workflow Clarity

Two clearly sequenced flows (D1→D4→Spec, P1→P4→Spec) with mode detection upfront and explicit validation gates: "Do NOT proceed without knowing what data exists…", "Must produce a one-liner", "Gate: User confirms the proposal". Knowledge Routing Step 3 provides an error-recovery path (declare metric unavailable, propose nearest alternative), and the Edge Cases table acts as a checklist — matching the anchor with explicit validation, feedback loops, and checklists.

Progressive Disclosure

Good structure with clearly signaled, one-level-deep references ("Read references/chart-selection-guide.md", "Load references/viz-spec-template.md"), and chart-selection knowledge is properly split out of SKILL.md. However, the body cites "references/data-viz-conventions.md" which does not exist in references/, while references/metrics-map.md exists but is never cited — broken and orphaned bundle files are minor organization gaps, so this is a 4 rather than a 5.

Specificity

Lists five concrete actions — "Creates bar/line charts, renders data tables, aggregates KPI metrics, builds filterable dashboard layouts, and generates HTML previews" — plus a scoped domain ("data-heavy screens, analytics dashboards, metric visualizations, or inline charts for Copilot"), matching the comprehensive-coverage anchor. It is not a 4 because no meaningful action in the skill's scope is left uncovered.

Completeness

Explicitly answers both questions: what ("Creates bar/line charts, renders data tables, aggregates KPI metrics…") and when ("Use when building data-heavy screens, analytics dashboards, metric visualizations, or inline charts for Copilot"). This matches the anchor for clearly and explicitly answering both with concrete trigger phrases; not a 4 because the 'when' is fully explicit, not merely present.

Trigger Term Quality

Triggers include natural terms in two languages — 'dashboard', 'métricas', 'gráfico', 'chart', 'visualización', 'analytics', 'KPI', 'reporte' — plus conversational phrases a user would actually say ('muestra los datos', 'cómo van los números'), matching the comprehensive synonym coverage anchor. Not a 4: the bilingual synonym and colloquial-phrase coverage leaves no natural term obviously missing.

Distinctiveness Conflict Risk

Has a clear niche (chart/dashboard design with HTML previews, Copilot inline mode), but generic triggers like 'chart', 'analytics', 'dashboard', and 'KPI' overlap with other data-visualization or general-dataviz skills that a user might intend instead. Mostly distinct with minor overlap risk against closely related viz skills — anchor 4 — rather than a 5 with minimal conflict risk.