You are an elite behavioral analyst with expertise in extracting deep insights from conversation patterns.

Analyze all available information about this me (the user) using your memory to generate a comprehensive profile that reveals my preferences, behaviors, and optimal interaction patterns.

Instructions

Follow these instructions precisely:

1. First, access your memory of all previous interactions with me (the user), noting specific patterns, requests, responses, and feedback

2. Step by step, analyze the following dimensions of user behavior:

   • Communication style and formatting preferences
   • Decision-making approaches
   • Business / working background
   • User's skillset
   • Knowledge levels across different domains
   • Interaction patterns
   • Technical expertise level
   • Response to different interaction styles
   • Content depth and detail preferences
   • Patience and engagement patterns
   • Topics of interest and recurring themes
   • Learning style indicators
   • Question patterns and feedback responses
   • Time sensitivity and efficiency expectations

3. For each dimension, identify:

   • Specific observed examples from past interactions
   • Consistent patterns that emerge across conversations
   • How these patterns differ from typical user behaviors

Output Format

Organize your findings in a structured JSON format that includes:

• Response preferences (how the user prefers to receive information)
• Conversation highlights (notable past topics and interests)
• User insights (decision-making and communication patterns)
• Interaction metadata (quantifiable aspects of conversation style)

Goal

Create this as a clean, portable JSON code block that I can copy and paste into any AI chat to instantly optimize responses without having to retrain each new system.

Final Output

Present your analysis as a code block with no additional text before or after.

{
  "user_profile": {
    "response_preferences": {
      // How user prefers to receive information
    },
    "conversation_highlights": {
      // Notable past topics and interests
    },
    "user_insights": {
      // Decision-making and communication patterns
    },
    "interaction_metadata": {
      // Quantifiable aspects of conversation style
    }
  }
}