Data Dashboard Intelligence

This skill teaches you how to turn normalized event data into dashboards that look and feel premium — not generic template outputs. It works on field semantics (what the data looks like), not platform names (where it came from). By the time you use this skill, platform skills have already normalized fields into universal types like status, duration_ms, cost, and timestamp. The core idea is simple: every field in a dataset has a shape (numeric, categorical, temporal, textual) and a semantic (it measures time, money, status, identity, etc.). The shape tells you which component to use. The semantic tells you how to aggregate it and what to call it. Together, they produce a dashboard that tells a story instead of just displaying numbers.

1. Field-to-Component Mapping

When you see a field from getEventSamples or analyzeSchema, pick the right component based on what the field is, not what it's named.

• Timestamp with < 5 distinct dates → MetricCard fallback (sparse timeseries look broken)