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prompt-enhancer

Automatically rewrites rough user inputs into optimized, structured prompts for dramatically better AI responses. Prefix any message with "p:" to activate.

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安装方式

直接复制以下提示词,发送给你的 AI 助手即可完成安装。

请先检查是否已安装 SkillHub 商店,若未安装,请根据 https://skillhub-1388575217.cos.ap-guangzhou.myqcloud.com/install/skillhub.md 安装 SkillHub 商店,然后安装 prompt-enhancer 技能。 若已安装,则直接安装 prompt-enhancer 技能。

Overview

Skill Key
gyaan-dub/prompt-enhancer
Author
gyaan-dub
Source Repo
openclaw/skills
Version
1.0.0
Source Path
skills/gyaan-dub/prompt-enhancer
Latest Commit SHA
8abd1e57d293ee152fc0fd367f15e17f874f1076

Extracted Content

SKILL.md excerpt

# Prompt Enhancer

You have a **Prompt Enhancer** skill. When a user prefixes their message with `p:` or `prompt:`, you must enhance their rough input into a high-quality structured prompt, then execute that enhanced prompt to produce a superior response.

This is a two-step process: first rewrite the prompt, then answer the rewritten prompt.

## Trigger Detection

Check every incoming user message for the trigger prefix:

- The message starts with `p:` or `prompt:` (case-insensitive, leading whitespace is OK)
- Everything after the prefix (trimmed) is the **raw user intent**
- If the prefix appears mid-sentence, do NOT trigger — only match at the start

If there is no trigger prefix, process the message normally. This skill does nothing for unprefixed messages.

## Empty Input Handling

If the user sends just `p:` or `prompt:` with no content (or only whitespace after the prefix), reply with:

> What would you like me to help with? Usage: Start your message with `p:` followed by what you want.
> Example: `p: write me a python script that sorts a list`

Do not proceed further.

## Step 1: Enhance the Prompt

Take the raw user intent and mentally rewrite it into an optimized prompt using these principles:

### 1. Role Assignment
Assign yourself a specific expert role relevant to the task.
Example: "As a senior full-stack developer specializing in React and Node.js..."

### 2. Task Clarification
Restate the task with precision and specificity. Infer what the user actually needs, including things they didn't explicitly mention. Break complex tasks into clear subtasks or steps if appropriate.

### 3. Context Inference
Fill in reasonable assumptions about what the user probably wants. A user asking for "a landing page" probably wants responsive design, a CTA, modern styling, etc. If assumptions are significant, note them briefly so you can adjust if needed.

### 4. Output Format Specification
Decide exactly how to structure the response. Examples: provide code in a singl...

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