Much of the body restates concepts Claude already knows — e.g. 'Apply chain-of-thought by asking the model to reason step-by-step... which improves accuracy' and 'Control output characteristics with temperature (0.0-0.3 for factual, 0.7-1.0 for creative)' — which matches the verbose/explains-known-concepts anchor rather than the leaner levels.

It offers concrete technique guidance ('Use few-shot examples (2-5)', 'Request structured output with explicit JSON schemas'), but provides no copy-paste prompt templates or worked examples, so it is incomplete against the fully-executable anchor.

The body is organized into clear sections (Principles, Techniques, Common Patterns, Pitfalls), but it presents a categorized reference rather than a sequenced process with validation checkpoints, fitting the steps-present-but-checkpoints-missing anchor.

At under 50 lines with no need for external references and clean section organization, it satisfies the simple-skill note that progressive disclosure can score 3 on well-organized sections alone.

Names the prompt-engineering domain and several techniques ('chain-of-thought, few-shot learning, evaluation, and LLM optimization'), but these are capability/concept labels rather than concrete actions like 'extract, fill, merge', so it stops short of the multiple-specific-actions anchor.

It states what the skill covers but provides no 'Use when...' clause or equivalent trigger guidance, so per the guideline completeness is capped at 2.

'Prompt engineering' is a natural term users would say, but 'chain-of-thought, few-shot learning, LLM optimization' lean technical and common variations like 'write prompts' or 'improve prompts' are missing, matching the some-relevant-keywords-but-missing-variations anchor.

'Prompt engineering expert' carves a recognizable niche, but without explicit trigger guidance it could still overlap with general writing or coding skills, fitting the somewhat-specific-but-could-overlap anchor.