What problem does it solve? Traditional logging scatters context across many small log lines, making incident debugging a slow grep-based search. This Skill provides a mental model for designing wide events—one comprehensive, high-dimensionality event per request—so observability data is optimized for querying rather than writing. ## Core Features & Use Cases - Annotation Strategy: Defines which context dimensions (identity, user, business, performance, outcome) to attach to every span using Effect.annotateCurrentSpan or Effect.annotateLogs. - Anti-Pattern Detection: Identifies scattered console.log chains, low-dimensionality spans, and technical-only instrumentation missing user and business context. - Queryability Test: Verifies instrumentation can answer questions like "failures where tier=premium and feature.new_flow=true" before shipping. - Use Case: When instrumenting a checkout service, apply this Skill to decide span attributes covering user tier, cart value, feature flags, and error codes so a single structured query can diagnose premium-user failures. ## Quick Start Ask the AI to review the span annotations in your Effect service and recommend which wide-event context fields to add for better incident queryability.