By name: prompt-engineer description: Converts user content into a structured prompt using a reusable template.
Prompt Engineer Command
Description
Converts user content into a well-structured prompt using the Universal Prompt Structure Template. Analyzes session context and formats content for optimal AI interaction.
Usage
/prompt-engineer [content]
Arguments
content(required): The detailed content/topic to convert into a structured prompt
Implementation
When invoked with content, I will:
- Analyze the provided content - Identify domain (technical, business, creative), complexity level, and core requirements
- Search current session - Look for relevant context, documents, or previous discussions that add value
- Apply Universal Prompt Template - Structure using appropriate template sections based on content analysis
- Output complete structured prompt - Ready-to-use prompt following the template format
Template Sections Applied
- Always Include: Task Context, Tone Context, Detailed Task Description, Immediate Request, Thinking Instructions, Output Formatting
- Include When Relevant: Background Data, Examples, Conversation History, Prefilled Response
Example Usage
/prompt-engineer "Create a customer service chatbot for our e-commerce platform that handles order inquiries"
Output Format
The command outputs a complete structured prompt following this pattern:
# Structured Prompt for: [Topic]
## 1. Task Context
[AI role and purpose based on content analysis]
## 2. Tone Context
[Communication style matching user's approach]
## 3. Background Data (if relevant)
[Session context and relevant documents]
## 4. Detailed Task Description & Rules
[Specific requirements and constraints]
## 5. Examples (if applicable)
[Demonstration of expected interactions]
## 6. Immediate Task Description
<question>{{CONTENT_RESTATED}}</question>
## 7. Thinking Step by Step
[Systematic processing instructions]
## 8. Output Formatting
[Desired response structure]
Benefits
- Context-Aware: Incorporates relevant session history and documentation
- Tone-Matched: Adapts to user's communication style (direct, technical, professional)
- Complete: Includes all necessary prompt engineering elements
- Reusable: Generated prompts work across different AI systems
- Systematic: Follows proven prompt structure template