# Send 🤖🤝🧠 better collab with your agent to your agent
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
## Fast path
- Download the package from Yavira.
- Extract it into a folder your agent can access.
- Paste one of the prompts below and point your agent at the extracted folder.
## Suggested prompts
### New install

```text
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Then review README.md for any prerequisites, environment setup, or post-install checks. Tell me what you changed and call out any manual steps you could not complete.
```
### Upgrade existing

```text
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Then review README.md for any prerequisites, environment setup, or post-install checks. Summarize what changed and any follow-up checks I should run.
```
## Machine-readable fields
```json
{
  "schemaVersion": "1.0",
  "item": {
    "slug": "user-cognitive-profiles",
    "name": "🤖🤝🧠 better collab with your agent",
    "source": "tencent",
    "type": "skill",
    "category": "通讯协作",
    "sourceUrl": "https://clawhub.ai/sebastianffx/user-cognitive-profiles",
    "canonicalUrl": "https://clawhub.ai/sebastianffx/user-cognitive-profiles",
    "targetPlatform": "OpenClaw"
  },
  "install": {
    "downloadUrl": "/downloads/user-cognitive-profiles",
    "sourceDownloadUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=user-cognitive-profiles",
    "sourcePlatform": "tencent",
    "targetPlatform": "OpenClaw",
    "packageFormat": "ZIP package",
    "primaryDoc": "SKILL.md",
    "includedAssets": [
      "requirements.txt",
      "references/methodology.md",
      "requirements-test.txt",
      "README.md",
      "examples/sample-profile.json",
      "examples/custom-archetypes.yaml"
    ],
    "downloadMode": "redirect",
    "sourceHealth": {
      "source": "tencent",
      "slug": "user-cognitive-profiles",
      "status": "healthy",
      "reason": "direct_download_ok",
      "recommendedAction": "download",
      "checkedAt": "2026-05-02T05:45:36.267Z",
      "expiresAt": "2026-05-09T05:45:36.267Z",
      "httpStatus": 200,
      "finalUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=user-cognitive-profiles",
      "contentType": "application/zip",
      "probeMethod": "head",
      "details": {
        "probeUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=user-cognitive-profiles",
        "contentDisposition": "attachment; filename=\"user-cognitive-profiles-1.0.0.zip\"",
        "redirectLocation": null,
        "bodySnippet": null,
        "slug": "user-cognitive-profiles"
      },
      "scope": "item",
      "summary": "Item download looks usable.",
      "detail": "Yavira can redirect you to the upstream package for this item.",
      "primaryActionLabel": "Download for OpenClaw",
      "primaryActionHref": "/downloads/user-cognitive-profiles"
    },
    "validation": {
      "installChecklist": [
        "Use the Yavira download entry.",
        "Review SKILL.md after the package is downloaded.",
        "Confirm the extracted package contains the expected setup assets."
      ],
      "postInstallChecks": [
        "Confirm the extracted package includes the expected docs or setup files.",
        "Validate the skill or prompts are available in your target agent workspace.",
        "Capture any manual follow-up steps the agent could not complete."
      ]
    }
  },
  "links": {
    "detailUrl": "https://openagent3.xyz/skills/user-cognitive-profiles",
    "downloadUrl": "https://openagent3.xyz/downloads/user-cognitive-profiles",
    "agentUrl": "https://openagent3.xyz/skills/user-cognitive-profiles/agent",
    "manifestUrl": "https://openagent3.xyz/skills/user-cognitive-profiles/agent.json",
    "briefUrl": "https://openagent3.xyz/skills/user-cognitive-profiles/agent.md"
  }
}
```
## Documentation

### 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. 🤖🤝🧠
## Trust
- Source: tencent
- Verification: Indexed source record
- Publisher: sebastianffx
- Version: 1.0.0
## Source health
- Status: healthy
- Item download looks usable.
- Yavira can redirect you to the upstream package for this item.
- Health scope: item
- Reason: direct_download_ok
- Checked at: 2026-05-02T05:45:36.267Z
- Expires at: 2026-05-09T05:45:36.267Z
- Recommended action: Download for OpenClaw
## Links
- [Detail page](https://openagent3.xyz/skills/user-cognitive-profiles)
- [Send to Agent page](https://openagent3.xyz/skills/user-cognitive-profiles/agent)
- [JSON manifest](https://openagent3.xyz/skills/user-cognitive-profiles/agent.json)
- [Markdown brief](https://openagent3.xyz/skills/user-cognitive-profiles/agent.md)
- [Download page](https://openagent3.xyz/downloads/user-cognitive-profiles)