What problem does it solve?
The Auto Prompt Enhancer transforms vague user requests into clear, structured prompts by applying XML tagging, multi-shot examples, and chain-of-thought reasoning. It ensures consistent, high-quality prompts that guide AI responses and reduce back-and-forth clarification.
Core Features & Use Cases
- XML-structured prompts: Converts fuzzy requests into a repeatable, machine-readable format using core tags such as <task>, <context>, <examples>, <approach>, and <expected_output>.
- Multishot examples: Provides 3-5 concrete examples to cover edge cases and common scenarios, improving accuracy and reducing misinterpretation.
- Chain-of-thought reasoning: Optionally includes step-by-step thinking to reveal the rationale behind the enhancement, enabling debugging and learning.
- Role definitions and prefilling: Enforces a defined expert role and displays a prefilled, structured prompt to ensure consistent outputs.
- Design-first guidance: Encourages creating design docs or templates before coding for complex tasks (development/design tasks).
Quick Start
Use the auto-prompt-enhancer skill to transform vague prompts into structured, actionable prompts. It will output an enhanced XML-based prompt that you can immediately feed into Claude or another LLM to proceed with implementation.
