🤖🤝🧠 better collab with your agent

User Cognitive Profiles

🤖🤝🧠 Discover how you communicate with AI and optimize your agent interactions. This skill analyzes your ChatGPT conversation history to identify cognitive archetypes — recurring patterns in how you think, communicate, and collaborate. Use these insights to calibrate your OpenClaw agent for more effective, personalized interactions.

Why This Matters

Human-AI communication is not one-size-fits-all. Just as you adapt your communication style between contexts (work meeting vs. casual chat), effective AI assistance requires matching your cognitive architecture. The Problem: Default AI behavior assumes a generic user Your communication style varies dramatically by context (professional vs. personal) Misaligned AI responses feel inefficient or frustrating The Solution: Analyze your actual conversation patterns Identify your dominant cognitive archetypes Configure your agent to match your communication style

1. Export Your ChatGPT Data

Go to ChatGPT → Settings → Data Controls → Export Data Click "Export" and confirm Wait for the email (usually arrives within 24 hours) Download the ZIP file from the email link Extract it — you'll find conversations.json

2. Run the Analysis

cd /path/to/user-cognitive-profiles python3 scripts/analyze_profile.py \ --input ~/Downloads/chatgpt-export/conversations.json \ --output ~/.openclaw/my-cognitive-profile.json \ --archetypes 3

3. Apply to Your Agent

• Add to your SOUL.md or AGENTS.md:
• ## User Cognitive Profile
• <!-- Source: generated by user-cognitive-profiles skill -->
• **Primary Archetype:** Efficiency Optimizer
• **Avg Message Length:** 47 words
• **Context Switching:** High (professional vs. personal modes)
• **Key Patterns:** Prefers direct answers, values examples over theory
• ### Communication Calibration
• Default to concise responses
• Provide examples + theory + hands-on steps
• Watch for professional/personal mode shifts

Cognitive Archetypes

The analysis identifies archetypes based on four dimensions: DimensionLowHighMessage LengthBrief commandsExtended analysisStructureOrganic flowSystematic breakdownDepthPractical focusTheoretical explorationToneTransactionalCollaborative

Common Archetypes

🔧 Efficiency Optimizer Messages: Short, direct, action-oriented Wants: Quick answers, minimal explanation AI Role: Tool to get things done Example: "Set up email. Use pass. Go." 🏗️ Systems Architect Messages: Long, structured, comprehensive Wants: Deep analysis, trade-offs, strategic thinking AI Role: Collaborative partner for complex problems Example: 300-word technical breakdown with multiple considerations 🧭 Philosophical Explorer Messages: Varies widely, questions assumptions Wants: Meaning, patterns, cross-domain connections AI Role: Socratic partner for insight generation Example: "How does this relate to [completely different domain]?" 🎨 Creative Synthesizer Messages: Connects disparate ideas, uses analogies Wants: Novel combinations, pattern recognition AI Role: Ideation partner and pattern mirror Example: "This is like jazz improvisation..."

Define Your Own Archetypes

Create ~/.openclaw/my-archetypes.yaml: archetypes: - name: "Research Mode" keywords: - "research" - "analyze" - "compare" - "trade-off" patterns: - long_messages - multiple_questions - citation_requests - name: "Quick Mode" keywords: - "quick" - "brief" - "simple" - "just" patterns: - short_messages - imperative_tone - minimal_context Run with custom archetypes: python3 scripts/analyze_profile.py \ --input conversations.json \ --archetypes-config ~/.openclaw/my-archetypes.yaml

Adjust Cluster Count

More archetypes = finer granularity, but harder to act on: # Simple: 2-3 archetypes python3 scripts/analyze_profile.py --archetypes 2 # Detailed: 5-7 archetypes python3 scripts/analyze_profile.py --archetypes 5 # Complex: 10+ (for power users) python3 scripts/analyze_profile.py --archetypes 10

Profile JSON Structure

{ "metadata": { "total_conversations": 3784, "date_range": "2024-01-01 to 2025-01-31", "analysis_date": "2026-02-02" }, "archetypes": [ { "id": 0, "name": "Systems Architect", "confidence": 0.87, "metrics": { "avg_message_length": 382, "avg_response_length": 450, "question_ratio": 0.23, "code_block_ratio": 0.45 }, "keywords": ["architecture", "design", "trade-off", "system"], "sample_conversations": ["uuid-1", "uuid-2"], "recommendations": { "ai_role": "Senior Architect", "communication_style": "Detailed, systematic, collaborative", "response_length": "long", "structure": "hierarchical" } } ], "context_shifts": [ { "trigger": "technical_keywords", "from_archetype": "Efficiency Optimizer", "to_archetype": "Systems Architect" } ], "insights": { "primary_mode": "Systems Architect", "context_switching": "high", "communication_preferences": [ "Examples before theory", "Hands-on application", "Cross-domain analogies" ] } }

Key Metrics Explained

MetricDescriptionWhy It Mattersavg_message_lengthAverage words per user messageShort = efficiency mode, Long = exploration modequestion_ratio% of turns that are questionsHigh = collaborative, Low = directivecode_block_ratio% of messages with codeTechnical vs. conceptual focuscontext_shiftsDetected mode transitionsIndicates multiple archetypes at playconfidenceCluster cohesion scoreHigher = more distinct pattern

Privacy & Security

All processing is local. The script: ✅ Runs entirely on your machine ✅ Never uploads data to external services ✅ Stores results in your local OpenClaw workspace ✅ You control what gets shared (if anything) Recommended workflow: Export ChatGPT data Run analysis locally Review my-cognitive-profile.json Manually add relevant insights to SOUL.md (Optional) Delete the export and raw profile

Compare Profiles Over Time

Track how your communication evolves: # January analysis python3 scripts/analyze_profile.py \ --input conversations_jan.json \ --output profile_jan.json # June analysis python3 scripts/analyze_profile.py \ --input conversations_jun.json \ --output profile_jun.json # Compare python3 scripts/compare_profiles.py profile_jan.json profile_jun.json

Export for Other Agents

• Generate a prompt snippet for Claude, GPT, or other agents:
• python3 scripts/analyze_profile.py \
• --input conversations.json \
• --format prompt-snippet \
• --output agent-prompt.txt
• Output:
• ## User Communication Profile
• Primary style: Systems Architect (detailed, analytical)
• Secondary style: Efficiency Optimizer (brief, pragmatic)
• Context switching: High (watch for mode shifts)
• Preferences: Examples + theory + hands-on steps
• Treat as: Senior technical partner, not assistant

"conversations.json not found"

The export ZIP contains multiple files. Make sure you're pointing to: chatgpt-export/ ├── conversations.json <-- This one ├── user.json └── ...

"No conversations detected"

Your export might be empty or corrupted. Check: head -20 conversations.json Should show: [{"title": "...", "messages": [...]}, ...]

"All archetypes have similar confidence"

Try adjusting the cluster count: # Too granular python3 scripts/analyze_profile.py --archetypes 10 # Try simpler python3 scripts/analyze_profile.py --archetypes 3

"Analysis takes too long"

For large conversation histories (10k+ messages): # Sample for faster analysis python3 scripts/analyze_profile.py \ --input conversations.json \ --sample 1000 # Analyze random 1000 conversations

Automatic Profile Loading

Add to your OpenClaw workspace AGENTS.md: ## On Session Start 1. Read `~/.openclaw/my-cognitive-profile.json` if exists 2. Adapt communication style to primary archetype 3. Watch for context shift indicators

Dynamic Mode Detection

For agents that can switch modes mid-conversation: # Pseudocode for agent integration def detect_mode_shift(current_message, profile): for shift in profile["context_shifts"]: if shift["trigger"] in current_message: return shift["to_archetype"] return profile["insights"]["primary_mode"]

Contributing

Have a new archetype that works well? Submit a PR with: Archetype definition in examples/ Sample data (anonymized) Validation that it clusters distinctly

References

references/methodology.md — Technical details on clustering algorithm references/archetype-taxonomy.md — Full archetype definitions examples/ — Sample profiles and configurations Built for humans who want their AI to truly understand them. 🤖🤝🧠