Lean and instruction-dense: assumes Claude's competence, skips general LLM/cost concepts, and lets executable SQL blocks carry the guidance rather than prose padding.

Provides fully executable SQL queries with exact field names and a copy-paste-ready trace-reading JSON call, plus a concrete decision tree for interpreting results.

Six numbered, clearly sequenced steps with explicit checkpoints (bounded time range, identified-user filter, baseline comparison, read traces before claiming causality) and decision-tree feedback for ambiguous aggregates.

No bundle files are present, but the body is well-organized into overview + Tools + Core rules + numbered Workflow + response shape; the one inline reference to exploring-llm-costs/references/cache-accounting.md is a single-level, clearly signaled external skill pointer.

Lists multiple concrete actions from the body's framing — rank users by cost and explain the drivers via volume, model choice, prompt/output size, cache behavior, retries, trace type, and custom dimensions.

Clearly answers what (analyze the most expensive users and explain why they cost so much) and when via the explicit 'Use when the user asks about...' clause with concrete triggers.

Uses natural phrasing a user would actually say ('top spenders', 'expensive users', 'per-user LLM cost', 'user-level cost drivers') with good coverage of variations for high spend.

Niche is clear (per-user AI observability cost analysis) and triggers are distinct from general cost or trace skills; the body explicitly cross-references the adjacent skills, lowering conflict risk.