The body is lean: brief one-line framing per concept followed directly by executable code, with concise best-practice and anti-pattern lists and no padding explaining basic concepts Claude already knows.

It provides complete, executable Python (select_model, frozen CostTracker, call_with_retry, cached message construction, and a composed process pipeline) that is copy-paste ready with specific thresholds and constants.

The '组合使用' section sequences a clear four-step pipeline with an explicit validation checkpoint (the over_budget guard raising BudgetExceededError before the call) and a feedback loop (retry with exponential backoff on transient errors).

The skill is a self-contained single file with no bundle references, organized into clearly labeled sections (concepts, composition, price reference, best practices, anti-patterns) with no nested or deep references, satisfying the well-organized single-file case.

The description lists multiple concrete techniques — '基于任务复杂度的模型路由、预算跟踪、重试逻辑和提示词缓存' (model routing, budget tracking, retry logic, prompt caching) — matching the anchor for several specific concrete actions rather than vague language.

It clearly answers 'what' (cost optimization via four named techniques) but offers no explicit 'when to use it' guidance; per the rubric a missing 'Use when...' clause caps completeness at 2.

It includes relevant technical terms (LLM API, 成本优化, 模型路由) but misses natural user phrasings like 'reduce LLM costs', 'save on API spending', or 'cheaper model', and contains no explicit 'Use when...' trigger clause.

The cost-optimization niche is somewhat specific, but the absence of distinct trigger phrases means it could still overlap with general LLM-calling skills, matching the 'somewhat specific but could overlap' anchor.