Conciseness

The body is dense, table-driven, and assumes Claude's competence ('Tiers, not model names: the naming churns every few months'), with no explanation of known concepts. The only trim opportunity is repetition: the system-prompt-size and max_tokens flags each appear in Mode 2, the Proactive Flags table, and again in Anti-Patterns.

Actionability

Concrete thresholds and named tools throughout (cosine similarity >0.95, >60% hit-rate targets, ~2,000-token caching flag, GPTCache, LiteLLM Proxy, cache_control, a per-request logging field schema, before/after compression examples). Minor gaps: model routing stays at 'a lightweight classifier or rule engine' with no concrete example, and there is no code snippet for the highest-ROI techniques.

Workflow Clarity

Modes are classified before questions are asked, steps are ROI-ordered with an explicit instruction to measure impact at each step, and validation is enforced ('Stop. Logging schema is deliverable #1. Return once baseline data exists'). The Failure Modes and Recovery table provides feedback loops (low cache hit rate -> diagnose; compression degrades quality -> restore the compressed section), matching the top anchor's validate-fix-retry pattern.

Progressive Disclosure

A single-file skill with no bundle files, well-organized headers, scannable tables, and clearly signaled handoffs to sibling skills rather than deep file references. Nothing inlined clearly belongs in a separate file, but at ~220 lines the Mode 2 technique detail is approaching the point where references would improve the overview.

Specificity

The domain is concrete and the technical signals are specific ('max_tokens is not set', 'system prompt... exceeds a few hundred tokens', 'no per-feature cost logging exists'), but the description enumerates triggers rather than the skill's actual actions (routing, caching, prompt compression, auditing) -- the 'what' is only implied by the skill name.

Completeness

The 'when' is exceptionally explicit with concrete trigger phrases and proactive conditions, but the 'what' is never directly stated -- the description relies on the skill name and trigger topics rather than stating what it does (e.g., 'reduces LLM costs via model routing, caching, and prompt compression').

Trigger Term Quality

Comprehensive natural-language triggers users would actually say ('my AI costs are too high', 'LLM spend is out of control', 'which model should I use', 'we're about to launch an AI feature', 'build me an AI endpoint') plus proactive technical signals, covering synonyms and paraphrases thoroughly.

Distinctiveness Conflict Risk

Explicit negative boundaries ('NOT for RAG pipeline design (use rag-architect)', 'NOT for improving prompt quality or effectiveness (use senior-prompt-engineer)') carve out a clear niche with distinct triggers and minimal conflict risk against adjacent skills.