enhance-prompts

To analyze prompt clarity and structure, provide your system prompt or task template to generate a JSON report containing a summary and specific findings on example quality and reliability. This evaluation helps writers and AI systems generate more precise results across content creation, software engineering, and education workflows.

The best way to improve prompt reliability is to analyze the prompt's structure, tone, and example quality to identify weaknesses. You can apply optional auto-fixes via a --fix workflow to generate a clearer, well-structured version that yields consistent evaluation results.

Yes, you can fix task templates automatically by running an auto-fix workflow using the --fix option. This process analyzes your existing templates for clarity and example quality, then outputs a well-structured version designed to improve prompt reliability without manual rewriting.

Prompt engineering analysis works for conversational prompts, system prompts, and task templates across various AI workflows. It applies consistent evaluation to check structure and example quality, ensuring your conversational prompts generate precise and reliable results.

Your AI prompt generates inconsistent results due to poor clarity, weak structure, or low example quality. Analyzing the prompt produces a JSON report with findings on these specific areas, allowing you to apply targeted improvements for better prompt reliability.