Lists multiple specific concrete actions: writes/refactors/evaluates prompts, generates optimized prompt templates, structured output schemas, evaluation rubrics, and test suites. Very detailed and actionable.

Clearly answers both 'what' (writes, refactors, evaluates prompts, generates templates/schemas/rubrics/test suites) and 'when' with an explicit 'Use when...' clause listing six specific trigger scenarios.

Excellent coverage of natural terms users would say: 'prompts', 'LLM', 'chain-of-thought', 'few-shot learning', 'system prompts', 'JSON', 'function-calling', 'token efficiency', 'evaluation', 'personas', 'guardrails'. These are terms prompt engineers and developers naturally use.

Occupies a clear niche around prompt engineering specifically. The triggers are distinct — terms like 'prompt templates', 'chain-of-thought', 'few-shot learning', 'system prompts', and 'prompt evaluation frameworks' are unlikely to conflict with general coding or document skills.

The skill is mostly efficient but includes some unnecessary content. The 'When to Use This Skill' section largely duplicates the description, the 'Coverage Note' at the bottom is filler, and some constraint items state obvious best practices Claude already knows (e.g., 'Version prompts and track changes systematically'). The MUST DO/MUST NOT DO lists are somewhat verbose with items that are general software engineering wisdom rather than prompt-engineering-specific guidance.

The before/after prompt examples and zero-shot vs few-shot comparisons are concrete and useful. However, the core workflow is fairly high-level and abstract ('Understand requirements', 'Design initial prompt'), and the skill lacks executable code or commands for evaluation, testing, or schema validation. The output templates section describes what to deliver but doesn't provide actual templates or schemas.

The 5-step core workflow is clearly sequenced and includes a validation checkpoint at step 3 with a specific threshold (accuracy < 80%) and failure pattern identification. However, the validation is somewhat vague — there's no concrete guidance on how to measure accuracy, what tools to use, or how to structure the feedback loop. For a skill involving iterative refinement and evaluation, the workflow could benefit from more explicit checkpoints and concrete validation steps.

The reference table is well-structured with clear 'Load When' guidance and one-level-deep references to six topic-specific files. However, no bundle files are provided, meaning none of these references actually exist, which significantly undermines the progressive disclosure structure. The main SKILL.md also includes substantial inline content (examples, constraints, output templates) that could arguably be split into references, making the file longer than necessary.