Use when designing prompts for LLMs, optimizing model performance, building evaluation frameworks, or implementing advanced prompting techniques like chain-of-thought, few-shot learning, or structured outputs.
7.0
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0
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AI & LLM
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Excellent prompt engineering skill with comprehensive coverage of LLM prompt design, optimization, and evaluation. The description clearly articulates when to invoke the skill. Structure is exemplary with a well-organized reference table that delegates details to separate files. The core workflow, constraints (MUST DO/MUST NOT DO), and output templates provide actionable guidance. Task knowledge is strong with systematic methodology covering design, testing, iteration, and deployment. References to evaluation frameworks, optimization techniques, and structured outputs indicate depth. Novelty is high as systematic prompt engineering with evaluation frameworks meaningfully reduces trial-and-error token costs compared to ad-hoc prompting by a CLI agent. Minor opportunity: could explicitly mention prompt versioning systems or collaborative workflows, but overall this is a highly useful, well-structured skill.
Jeffallan
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