Analyze ChatGPT conversation exports to discover cognitive archetypes and optimize AI-human communication patterns.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install user-cognitive-profiles
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install user-cognitive-profiles using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is User Cognitive Profiles?
User Cognitive Profiles is a specialized tool designed to bridge the gap between generic AI responses and personalized human communication. By analyzing your historical ChatGPT conversation data, this skill identifies recurring cognitive archetypes—patterns in how you think, structure requests, and collaborate with AI. This process allows users of Openclaw Skills to move beyond one-size-fits-all interactions and create agents that truly understand their unique cognitive architecture.
Whether you are a brief and direct Efficiency Optimizer or a detailed Systems Architect, this skill provides the data-driven insights needed to calibrate your AI agent's soul and behavior. It ensures that your assistant adapts to your specific context, whether you are switching between high-level strategic planning or rapid-fire task execution.
User Cognitive Profiles Use Cases
- Calibrating AI agent responses to match your preferred level of technical depth and detail.
- Identifying communication style shifts between professional and personal contexts to automate mode-switching.
- Generating personalized system prompts that reduce friction and improve instruction following.
- Tracking the evolution of your technical communication and collaboration patterns over time.
How User Cognitive Profiles Works
- The user exports their official ChatGPT conversation history in JSON format.
- A local Python script parses the history to analyze message length, structure, depth, and tone.
- The skill clusters these data points into 3-10 distinct cognitive archetypes using local NLP processing.
- A comprehensive JSON profile is generated, detailing metrics like question ratios and code block density.
- The user applies these insights to their agent configuration files to personalize the communication lifecycle.
User Cognitive Profiles Setup
First, ensure you have Python 3 installed. Export your data from ChatGPT Settings and extract the conversations.json file. Then, run the following commands:
# Navigate to the skill directory
cd user-cognitive-profiles
# Run the analysis on your exported data
python3 scripts/analyze_profile.py \
--input ~/Downloads/chatgpt-export/conversations.json \
--output ~/.openclaw/my-cognitive-profile.json \
--archetypes 3
User Cognitive Profiles Data Schema & Taxonomy
The skill produces a detailed JSON profile with the following structure:
| Object | Description |
|---|---|
metadata |
Includes total conversation count, date ranges, and analysis timestamps. |
archetypes |
An array of detected patterns including confidence scores, average message length, and recommended AI roles. |
context_shifts |
Identifies keywords or triggers that signal a change in user communication style. |
insights |
High-level summary of communication preferences such as 'Examples before theory'. |
User Cognitive Profiles Advanced Features
- Custom Archetype Definitions: Define your own patterns via YAML configuration to track specific research or coding modes.
- Profile Comparison: Use the comparison script to see how your communication style has shifted across different six-month intervals.
- Dynamic Mode Detection: Pseudocode and logic for agents to switch their persona mid-conversation based on real-time trigger detection.
- Local-First Privacy: All processing happens on your machine, ensuring your Openclaw Skills data never leaves your environment.
SKILL.md
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