Conciseness

The body is lean and assumes competence: it never explains what analytics or events are, and every section carries organization-specific facts Claude cannot know (naming convention `<prefix>.<noun>.<verb>`, registry paths, the datalake destination). Minor trimming is possible — e.g. the two-paragraph framing about software engineers vs. data people could be condensed — so it sits at 'efficient; minor instances of over-explanation' rather than the every-token-earns-its-place anchor 5.

Actionability

Guidance is fully executable: a concrete local-verification command (`DEBUG_LEVEL=1 uv run vibe`), exact registry paths (`datalake-dbt/event_registry/<service>.yml`), a precise naming rule with examples, and three complete, copy-paste-ready BigQuery SQL queries with placeholder substitution clearly marked (`-- <- your event name(s)`). It also explains how to read each query's output, covering the common verification cases.

Workflow Clarity

The 9-step process is clearly sequenced with explicit validation checkpoints at all three levels (local log check, staging queries, production queries) and feedback loops for error recovery — e.g. "Every property you meant to always send should read 1.0; below that, some code path or surface isn't sending it" and "A property you expected but don't see at all never fired" tell the reader how to diagnose failures before promoting. This matches the anchor of clear sequence with explicit validation and feedback loops.

Progressive Disclosure

The body has good structure — numbered sections, well-labeled subsections, and a separate "Verification queries" section — but it is a single ~145-line file with no bundle, and roughly 60 lines of inline SQL plus the event-pattern catalog are natural candidates for a references file that keeps SKILL.md as a lean overview. This fits 'good structure; most content appropriately placed; minor organization gaps'; not 5 because the skill exceeds the under-50-line simple-skill case where sections alone suffice.

Specificity

The description lists multiple concrete, distinct actions — "Decides which events and properties the metrics need (funnels, adoption, status flows)", "checks compatibility with the existing datalake and event registry", "walks through local, staging, and production verification" — with no vague filler. Third-person voice is used throughout, matching the anchor 'lists multiple specific concrete actions; comprehensive coverage'. Not 4 because coverage spans the full scope (design, compatibility, verification) rather than having minor gaps.

Completeness

Both parts are explicit: the 'what' is stated concretely (decide events/properties, check datalake and event registry compatibility, verify across environments) and the 'when' is an explicit trigger clause — "Use when implementing any feature — every feature must ship with telemetry". This clearly matches the anchor for both what and when with concrete trigger phrases; not 4 because the 'when' is explicit and specific rather than merely present.

Trigger Term Quality

Natural terms users would say are present: "analytics instrumentation", "telemetry", "events", "properties", "funnels", "feature". It is missing a few common synonyms users might naturally use such as "tracking", "metrics", or "event logging", so it falls just short of the comprehensive-with-synonyms anchor 5 but is noticeably above the midpoint.

Distinctiveness Conflict Risk

The domain is a clear niche (analytics instrumentation), but the trigger "Use when implementing any feature — every feature must ship with telemetry" fires on virtually every feature-implementation task, giving high overlap with general coding and feature-development skills. This matches 'somewhat specific but could still overlap with similar skills'; not 4 because the deliberately universal trigger is a real conflict risk, and not 2 because the core topic wording is distinct.