Lists multiple specific concrete actions: 'Writes, refactors, and evaluates prompts', 'generating optimized prompt templates, structured output schemas, evaluation rubrics, and test suites'. These are clear, actionable capabilities.

Clearly answers both what ('Writes, refactors, and evaluates prompts for LLMs') and when with explicit 'Use when...' clause covering six distinct trigger scenarios including designing new prompts, refactoring, implementing specific techniques, and building evaluation frameworks.

Excellent coverage of natural terms users would say: 'prompts', 'LLM', 'chain-of-thought', 'few-shot learning', 'system prompts', 'JSON', 'function-calling', 'token efficiency', 'evaluation frameworks'. These match how practitioners naturally discuss prompt engineering.

Highly distinctive niche focused specifically on prompt engineering for LLMs. The specific triggers like 'chain-of-thought', 'few-shot learning', 'system prompts', and 'function-calling schemas' clearly differentiate this from general coding or documentation skills.

The content is lean and efficient, avoiding explanations of concepts Claude already knows. Each section serves a clear purpose without padding or unnecessary context about what prompts or LLMs are.

Provides concrete, copy-paste ready prompt examples with clear before/after comparisons. The zero-shot vs few-shot examples and the optimization example are fully executable templates with placeholder syntax.

The 5-step core workflow is clearly sequenced with an explicit validation checkpoint at step 3 (accuracy < 80% triggers failure pattern analysis). The iterate step explicitly states 'one change at a time' for debugging discipline.

Excellent structure with a clear reference table pointing to 6 separate files for detailed guidance, each with a 'Load When' context. The main skill provides a complete overview while appropriately deferring deep dives to one-level-deep references.