prompt-optimization-analyzer

To optimize skill prompts, you analyze YAML frontmatter and Markdown bodies to identify token waste, missing triggers, and vague descriptions. This diagnostic process yields actionable recommendations to refine prompts for better trigger reliability and efficiency.

Common anti-patterns in prompt design include missing triggers, vague descriptions, and token inefficiency. Analyzing the Markdown body and YAML frontmatter helps detect these issues, ensuring skill prompts are clear, efficient, and effective upon publication.

You analyze a Markdown prompt for token efficiency by checking its YAML frontmatter and body for token waste and anti-patterns. This diagnostic analysis identifies optimization opportunities to streamline the prompt and enhance its performance.

Prompt analysis works without external dependencies by internally evaluating YAML frontmatter and Markdown text. It checks for missing triggers and vague descriptions, providing optimization suggestions to refine the prompt independently.

Your skill prompt may fail to trigger reliably due to missing triggers or token inefficiency hidden in the YAML frontmatter. A diagnostic analysis identifies these specific anti-patterns and provides suggestions to improve trigger reliability.

The best way to streamline prompts for token efficiency is to run a diagnostic analysis on the YAML frontmatter and Markdown body. This identifies token waste and vague descriptions, providing actionable recommendations to refine the prompt.