skill-description-optimizer

To improve Claude skill description trigger accuracy, provide the current skill name, its frontmatter description, and evaluation results to generate refined, more accurate description variants that reduce false positives and false negatives.

Frontmatter description optimization for prompt evaluation is the process of analyzing train and test sets to iterate on a skill's trigger description, avoiding overfitting and maximizing accurate triggering for specific user queries.

You reduce false positives and false negatives in skill triggering by generating and evaluating targeted trigger query sets, then iterating using train and test evaluation to produce a concise, distinctive final frontmatter description.

Yes, providing train and test evaluation results is required to optimize a skill description, as the optimizer uses them to generate targeted trigger opportunities and avoid overfitting when presenting improved description variants.

The best way to generate distinctive frontmatter description variants for Claude skills is to analyze existing evaluation results and iterate using train and test sets, producing concise descriptions selected for maximum trigger accuracy.

Your skill description fails to trigger accurately because it lacks sufficient distinctiveness, which can be resolved by analyzing evaluation results and iterating with train and test query sets to refine the frontmatter description.