[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6G2WVN9M2AXPQK517D2583P":3,"skill-same:01M6G2WVN9M2AXPQK517D2583P":120},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":15,"categories":16,"keywords":17,"publishedAt":38,"linkOnly":39,"markdownContent":40,"markdownBody":41,"frontMatter":42,"refName":51,"commitSha":52,"skillDirectory":53,"forksCount":54,"repositoryHtmlUrl":55,"repositoryHomepageUrl":6,"ownerHtmlUrl":56,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":57,"install":58,"resources":71},"01M6G2WVN9M2AXPQK517D2583P","user-cognitive-profiles",null,"Analyze ChatGPT conversation exports to discover cognitive archetypes and optimize AI-human communication patterns. Enables personalized agent interactions based on detected user profiles.","kbarbel640-del\u002Fskills","kbarbel640-del","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F255315490?v=4","01M6CJPPQGCCESXMG3K7BBGQ5J","01M6CJPPX2PB2BCZERFVM2C2B3",1,"MIT","https:\u002F\u002Fgithub.com\u002Fkbarbel640-del\u002Fskills\u002Fblob\u002Fmain\u002Fskills\u002Fsebastianffx\u002Fuser-cognitive-profiles\u002FSKILL.md",[],[18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37],"user","user cognitive profiles","profiles","archetypes","nlp","github","user-research","discover","human","optimize","patterns","conversation","research","openclaw","python3","analyze","persona","Optimization","chatgpt","communication","2026-11-10 10:12:30",false,"---\nname: user-cognitive-profiles\ndescription: \"Analyze ChatGPT conversation exports to discover cognitive archetypes and optimize AI-human communication patterns. Enables personalized agent interactions based on detected user profiles.\"\nhomepage: https:\u002F\u002Fgithub.com\u002Fopenclaw\u002Fuser-cognitive-profiles\nmetadata:\n  {\n    \"openclaw\":\n      {\n        \"emoji\": \"???\",\n        \"requires\": { \"bins\": [\"python3\"] },\n        \"tags\": [\"communication\", \"persona\", \"user-research\", \"optimization\", \"nlp\"],\n      },\n  }\n---\n\n# User Cognitive Profiles\n\n??? Discover how you communicate with AI and optimize your agent interactions.\n\nThis 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.\n\n---\n\n## Why This Matters\n\nHuman-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**.\n\n**The Problem:**\n- Default AI behavior assumes a generic user\n- Your communication style varies dramatically by context (professional vs. personal)\n- Misaligned AI responses feel inefficient or frustrating\n\n**The Solution:**\n- Analyze your actual conversation patterns\n- Identify your dominant cognitive archetypes\n- Configure your agent to match your communication style\n\n---\n\n## Quick Start\n\n### 1. Export Your ChatGPT Data\n\n1. Go to **ChatGPT → Settings → Data Controls → Export Data**\n2. Click \"Export\" and confirm\n3. Wait for the email (usually arrives within 24 hours)\n4. Download the ZIP file from the email link\n5. Extract it — you'll find `conversations.json`\n\n### 2. Run the Analysis\n\n```bash\ncd \u002Fpath\u002Fto\u002Fuser-cognitive-profiles\npython3 scripts\u002Fanalyze_profile.py \\\n  --input ~\u002FDownloads\u002Fchatgpt-export\u002Fconversations.json \\\n  --output ~\u002F.openclaw\u002Fmy-cognitive-profile.json \\\n  --archetypes 3\n```\n\n### 3. Apply to Your Agent\n\nAdd to your `SOUL.md` or `AGENTS.md`:\n\n```markdown\n## User Cognitive Profile\n\u003C!-- Source: generated by user-cognitive-profiles skill -->\n- **Primary Archetype:** Efficiency Optimizer\n- **Avg Message Length:** 47 words\n- **Context Switching:** High (professional vs. personal modes)\n- **Key Patterns:** Prefers direct answers, values examples over theory\n\n### Communication Calibration\n- Default to concise responses\n- Provide examples + theory + hands-on steps\n- Watch for professional\u002Fpersonal mode shifts\n```\n\n---\n\n## Cognitive Archetypes\n\nThe analysis identifies archetypes based on **four dimensions**:\n\n| Dimension | Low | High |\n|-----------|-----|------|\n| **Message Length** | Brief commands | Extended analysis |\n| **Structure** | Organic flow | Systematic breakdown |\n| **Depth** | Practical focus | Theoretical exploration |\n| **Tone** | Transactional | Collaborative |\n\n### Common Archetypes\n\n#### ? Efficiency Optimizer\n- **Messages:** Short, direct, action-oriented\n- **Wants:** Quick answers, minimal explanation\n- **AI Role:** Tool to get things done\n- **Example:** \"Set up email. Use pass. Go.\"\n\n#### ?️ Systems Architect\n- **Messages:** Long, structured, comprehensive\n- **Wants:** Deep analysis, trade-offs, strategic thinking\n- **AI Role:** Collaborative partner for complex problems\n- **Example:** 300-word technical breakdown with multiple considerations\n\n#### ? Philosophical Explorer\n- **Messages:** Varies widely, questions assumptions\n- **Wants:** Meaning, patterns, cross-domain connections\n- **AI Role:** Socratic partner for insight generation\n- **Example:** \"How does this relate to [completely different domain]?\"\n\n#### ? Creative Synthesizer\n- **Messages:** Connects disparate ideas, uses analogies\n- **Wants:** Novel combinations, pattern recognition\n- **AI Role:** Ideation partner and pattern mirror\n- **Example:** \"This is like jazz improvisation...\"\n\n---\n\n## Customization\n\n### Define Your Own Archetypes\n\nCreate `~\u002F.openclaw\u002Fmy-archetypes.yaml`:\n\n```yaml\narchetypes:\n  - name: \"Research Mode\"\n    keywords:\n      - \"research\"\n      - \"analyze\"\n      - \"compare\"\n      - \"trade-off\"\n    patterns:\n      - long_messages\n      - multiple_questions\n      - citation_requests\n    \n  - name: \"Quick Mode\"\n    keywords:\n      - \"quick\"\n      - \"brief\"\n      - \"simple\"\n      - \"just\"\n    patterns:\n      - short_messages\n      - imperative_tone\n      - minimal_context\n```\n\nRun with custom archetypes:\n\n```bash\npython3 scripts\u002Fanalyze_profile.py \\\n  --input conversations.json \\\n  --archetypes-config ~\u002F.openclaw\u002Fmy-archetypes.yaml\n```\n\n### Adjust Cluster Count\n\nMore archetypes = finer granularity, but harder to act on:\n\n```bash\n# Simple: 2-3 archetypes\npython3 scripts\u002Fanalyze_profile.py --archetypes 2\n\n# Detailed: 5-7 archetypes\npython3 scripts\u002Fanalyze_profile.py --archetypes 5\n\n# Complex: 10+ (for power users)\npython3 scripts\u002Fanalyze_profile.py --archetypes 10\n```\n\n---\n\n## Understanding the Output\n\n### Profile JSON Structure\n\n```json\n{\n  \"metadata\": {\n    \"total_conversations\": 3784,\n    \"date_range\": \"2024-01-01 to 2025-01-31\",\n    \"analysis_date\": \"2026-02-02\"\n  },\n  \"archetypes\": [\n    {\n      \"id\": 0,\n      \"name\": \"Systems Architect\",\n      \"confidence\": 0.87,\n      \"metrics\": {\n        \"avg_message_length\": 382,\n        \"avg_response_length\": 450,\n        \"question_ratio\": 0.23,\n        \"code_block_ratio\": 0.45\n      },\n      \"keywords\": [\"architecture\", \"design\", \"trade-off\", \"system\"],\n      \"sample_conversations\": [\"uuid-1\", \"uuid-2\"],\n      \"recommendations\": {\n        \"ai_role\": \"Senior Architect\",\n        \"communication_style\": \"Detailed, systematic, collaborative\",\n        \"response_length\": \"long\",\n        \"structure\": \"hierarchical\"\n      }\n    }\n  ],\n  \"context_shifts\": [\n    {\n      \"trigger\": \"technical_keywords\",\n      \"from_archetype\": \"Efficiency Optimizer\",\n      \"to_archetype\": \"Systems Architect\"\n    }\n  ],\n  \"insights\": {\n    \"primary_mode\": \"Systems Architect\",\n    \"context_switching\": \"high\",\n    \"communication_preferences\": [\n      \"Examples before theory\",\n      \"Hands-on application\",\n      \"Cross-domain analogies\"\n    ]\n  }\n}\n```\n\n### Key Metrics Explained\n\n| Metric | Description | Why It Matters |\n|--------|-------------|----------------|\n| `avg_message_length` | Average words per user message | Short = efficiency mode, Long = exploration mode |\n| `question_ratio` | % of turns that are questions | High = collaborative, Low = directive |\n| `code_block_ratio` | % of messages with code | Technical vs. conceptual focus |\n| `context_shifts` | Detected mode transitions | Indicates multiple archetypes at play |\n| `confidence` | Cluster cohesion score | Higher = more distinct pattern |\n\n---\n\n## Privacy & Security\n\n**All processing is local.** The script:\n- ✅ Runs entirely on your machine\n- ✅ Never uploads data to external services\n- ✅ Stores results in your local OpenClaw workspace\n- ✅ You control what gets shared (if anything)\n\n**Recommended workflow:**\n1. Export ChatGPT data\n2. Run analysis locally\n3. Review `my-cognitive-profile.json`\n4. Manually add relevant insights to `SOUL.md`\n5. (Optional) Delete the export and raw profile\n\n---\n\n## Advanced Usage\n\n### Compare Profiles Over Time\n\nTrack how your communication evolves:\n\n```bash\n# January analysis\npython3 scripts\u002Fanalyze_profile.py \\\n  --input conversations_jan.json \\\n  --output profile_jan.json\n\n# June analysis\npython3 scripts\u002Fanalyze_profile.py \\\n  --input conversations_jun.json \\\n  --output profile_jun.json\n\n# Compare\npython3 scripts\u002Fcompare_profiles.py profile_jan.json profile_jun.json\n```\n\n### Export for Other Agents\n\nGenerate a prompt snippet for Claude, GPT, or other agents:\n\n```bash\npython3 scripts\u002Fanalyze_profile.py \\\n  --input conversations.json \\\n  --format prompt-snippet \\\n  --output agent-prompt.txt\n```\n\nOutput:\n```markdown\n## User Communication Profile\n- Primary style: Systems Architect (detailed, analytical)\n- Secondary style: Efficiency Optimizer (brief, pragmatic)\n- Context switching: High (watch for mode shifts)\n- Preferences: Examples + theory + hands-on steps\n- Treat as: Senior technical partner, not assistant\n```\n\n---\n\n## Troubleshooting\n\n### \"conversations.json not found\"\n\nThe export ZIP contains multiple files. Make sure you're pointing to:\n```\nchatgpt-export\u002F\n├── conversations.json  \u003C-- This one\n├── user.json\n└── ...\n```\n\n### \"No conversations detected\"\n\nYour export might be empty or corrupted. Check:\n```bash\nhead -20 conversations.json\n```\n\nShould show: `[{\"title\": \"...\", \"messages\": [...]}, ...]`\n\n### \"All archetypes have similar confidence\"\n\nTry adjusting the cluster count:\n```bash\n# Too granular\npython3 scripts\u002Fanalyze_profile.py --archetypes 10\n\n# Try simpler\npython3 scripts\u002Fanalyze_profile.py --archetypes 3\n```\n\n### \"Analysis takes too long\"\n\nFor large conversation histories (10k+ messages):\n```bash\n# Sample for faster analysis\npython3 scripts\u002Fanalyze_profile.py \\\n  --input conversations.json \\\n  --sample 1000  # Analyze random 1000 conversations\n```\n\n---\n\n## Integration with OpenClaw\n\n### Automatic Profile Loading\n\nAdd to your OpenClaw workspace `AGENTS.md`:\n\n```markdown\n## On Session Start\n1. Read `~\u002F.openclaw\u002Fmy-cognitive-profile.json` if exists\n2. Adapt communication style to primary archetype\n3. Watch for context shift indicators\n```\n\n### Dynamic Mode Detection\n\nFor agents that can switch modes mid-conversation:\n\n```python\n# Pseudocode for agent integration\ndef detect_mode_shift(current_message, profile):\n    for shift in profile[\"context_shifts\"]:\n        if shift[\"trigger\"] in current_message:\n            return shift[\"to_archetype\"]\n    return profile[\"insights\"][\"primary_mode\"]\n```\n\n---\n\n## Contributing\n\nHave a new archetype that works well? Submit a PR with:\n1. Archetype definition in `examples\u002F`\n2. Sample data (anonymized)\n3. Validation that it clusters distinctly\n\n---\n\n## References\n\n- `references\u002Fmethodology.md` — Technical details on clustering algorithm\n- `references\u002Farchetype-taxonomy.md` — Full archetype definitions\n- `examples\u002F` — Sample profiles and configurations\n\n---\n\n*Built for humans who want their AI to truly understand them.* ???\n","# User Cognitive Profiles\n\n??? Discover how you communicate with AI and optimize your agent interactions.\n\nThis 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.\n\n---\n\n## Why This Matters\n\nHuman-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**.\n\n**The Problem:**\n- Default AI behavior assumes a generic user\n- Your communication style varies dramatically by context (professional vs. personal)\n- Misaligned AI responses feel inefficient or frustrating\n\n**The Solution:**\n- Analyze your actual conversation patterns\n- Identify your dominant cognitive archetypes\n- Configure your agent to match your communication style\n\n---\n\n## Quick Start\n\n### 1. Export Your ChatGPT Data\n\n1. Go to **ChatGPT → Settings → Data Controls → Export Data**\n2. Click \"Export\" and confirm\n3. Wait for the email (usually arrives within 24 hours)\n4. Download the ZIP file from the email link\n5. Extract it — you'll find `conversations.json`\n\n### 2. Run the Analysis\n\n```bash\ncd \u002Fpath\u002Fto\u002Fuser-cognitive-profiles\npython3 scripts\u002Fanalyze_profile.py \\\n  --input ~\u002FDownloads\u002Fchatgpt-export\u002Fconversations.json \\\n  --output ~\u002F.openclaw\u002Fmy-cognitive-profile.json \\\n  --archetypes 3\n```\n\n### 3. Apply to Your Agent\n\nAdd to your `SOUL.md` or `AGENTS.md`:\n\n```markdown\n## User Cognitive Profile\n\u003C!-- Source: generated by user-cognitive-profiles skill -->\n- **Primary Archetype:** Efficiency Optimizer\n- **Avg Message Length:** 47 words\n- **Context Switching:** High (professional vs. personal modes)\n- **Key Patterns:** Prefers direct answers, values examples over theory\n\n### Communication Calibration\n- Default to concise responses\n- Provide examples + theory + hands-on steps\n- Watch for professional\u002Fpersonal mode shifts\n```\n\n---\n\n## Cognitive Archetypes\n\nThe analysis identifies archetypes based on **four dimensions**:\n\n| Dimension | Low | High |\n|-----------|-----|------|\n| **Message Length** | Brief commands | Extended analysis |\n| **Structure** | Organic flow | Systematic breakdown |\n| **Depth** | Practical focus | Theoretical exploration |\n| **Tone** | Transactional | Collaborative |\n\n### Common Archetypes\n\n#### ? Efficiency Optimizer\n- **Messages:** Short, direct, action-oriented\n- **Wants:** Quick answers, minimal explanation\n- **AI Role:** Tool to get things done\n- **Example:** \"Set up email. Use pass. Go.\"\n\n#### ?️ Systems Architect\n- **Messages:** Long, structured, comprehensive\n- **Wants:** Deep analysis, trade-offs, strategic thinking\n- **AI Role:** Collaborative partner for complex problems\n- **Example:** 300-word technical breakdown with multiple considerations\n\n#### ? Philosophical Explorer\n- **Messages:** Varies widely, questions assumptions\n- **Wants:** Meaning, patterns, cross-domain connections\n- **AI Role:** Socratic partner for insight generation\n- **Example:** \"How does this relate to [completely different domain]?\"\n\n#### ? Creative Synthesizer\n- **Messages:** Connects disparate ideas, uses analogies\n- **Wants:** Novel combinations, pattern recognition\n- **AI Role:** Ideation partner and pattern mirror\n- **Example:** \"This is like jazz improvisation...\"\n\n---\n\n## Customization\n\n### Define Your Own Archetypes\n\nCreate `~\u002F.openclaw\u002Fmy-archetypes.yaml`:\n\n```yaml\narchetypes:\n  - name: \"Research Mode\"\n    keywords:\n      - \"research\"\n      - \"analyze\"\n      - \"compare\"\n      - \"trade-off\"\n    patterns:\n      - long_messages\n      - multiple_questions\n      - citation_requests\n    \n  - name: \"Quick Mode\"\n    keywords:\n      - \"quick\"\n      - \"brief\"\n      - \"simple\"\n      - \"just\"\n    patterns:\n      - short_messages\n      - imperative_tone\n      - minimal_context\n```\n\nRun with custom archetypes:\n\n```bash\npython3 scripts\u002Fanalyze_profile.py \\\n  --input conversations.json \\\n  --archetypes-config ~\u002F.openclaw\u002Fmy-archetypes.yaml\n```\n\n### Adjust Cluster Count\n\nMore archetypes = finer granularity, but harder to act on:\n\n```bash\n# Simple: 2-3 archetypes\npython3 scripts\u002Fanalyze_profile.py --archetypes 2\n\n# Detailed: 5-7 archetypes\npython3 scripts\u002Fanalyze_profile.py --archetypes 5\n\n# Complex: 10+ (for power users)\npython3 scripts\u002Fanalyze_profile.py --archetypes 10\n```\n\n---\n\n## Understanding the Output\n\n### Profile JSON Structure\n\n```json\n{\n  \"metadata\": {\n    \"total_conversations\": 3784,\n    \"date_range\": \"2024-01-01 to 2025-01-31\",\n    \"analysis_date\": \"2026-02-02\"\n  },\n  \"archetypes\": [\n    {\n      \"id\": 0,\n      \"name\": \"Systems Architect\",\n      \"confidence\": 0.87,\n      \"metrics\": {\n        \"avg_message_length\": 382,\n        \"avg_response_length\": 450,\n        \"question_ratio\": 0.23,\n        \"code_block_ratio\": 0.45\n      },\n      \"keywords\": [\"architecture\", \"design\", \"trade-off\", \"system\"],\n      \"sample_conversations\": [\"uuid-1\", \"uuid-2\"],\n      \"recommendations\": {\n        \"ai_role\": \"Senior Architect\",\n        \"communication_style\": \"Detailed, systematic, collaborative\",\n        \"response_length\": \"long\",\n        \"structure\": \"hierarchical\"\n      }\n    }\n  ],\n  \"context_shifts\": [\n    {\n      \"trigger\": \"technical_keywords\",\n      \"from_archetype\": \"Efficiency Optimizer\",\n      \"to_archetype\": \"Systems Architect\"\n    }\n  ],\n  \"insights\": {\n    \"primary_mode\": \"Systems Architect\",\n    \"context_switching\": \"high\",\n    \"communication_preferences\": [\n      \"Examples before theory\",\n      \"Hands-on application\",\n      \"Cross-domain analogies\"\n    ]\n  }\n}\n```\n\n### Key Metrics Explained\n\n| Metric | Description | Why It Matters |\n|--------|-------------|----------------|\n| `avg_message_length` | Average words per user message | Short = efficiency mode, Long = exploration mode |\n| `question_ratio` | % of turns that are questions | High = collaborative, Low = directive |\n| `code_block_ratio` | % of messages with code | Technical vs. conceptual focus |\n| `context_shifts` | Detected mode transitions | Indicates multiple archetypes at play |\n| `confidence` | Cluster cohesion score | Higher = more distinct pattern |\n\n---\n\n## Privacy & Security\n\n**All processing is local.** The script:\n- ✅ Runs entirely on your machine\n- ✅ Never uploads data to external services\n- ✅ Stores results in your local OpenClaw workspace\n- ✅ You control what gets shared (if anything)\n\n**Recommended workflow:**\n1. Export ChatGPT data\n2. Run analysis locally\n3. Review `my-cognitive-profile.json`\n4. Manually add relevant insights to `SOUL.md`\n5. (Optional) Delete the export and raw profile\n\n---\n\n## Advanced Usage\n\n### Compare Profiles Over Time\n\nTrack how your communication evolves:\n\n```bash\n# January analysis\npython3 scripts\u002Fanalyze_profile.py \\\n  --input conversations_jan.json \\\n  --output profile_jan.json\n\n# June analysis\npython3 scripts\u002Fanalyze_profile.py \\\n  --input conversations_jun.json \\\n  --output profile_jun.json\n\n# Compare\npython3 scripts\u002Fcompare_profiles.py profile_jan.json profile_jun.json\n```\n\n### Export for Other Agents\n\nGenerate a prompt snippet for Claude, GPT, or other agents:\n\n```bash\npython3 scripts\u002Fanalyze_profile.py \\\n  --input conversations.json \\\n  --format prompt-snippet \\\n  --output agent-prompt.txt\n```\n\nOutput:\n```markdown\n## User Communication Profile\n- Primary style: Systems Architect (detailed, analytical)\n- Secondary style: Efficiency Optimizer (brief, pragmatic)\n- Context switching: High (watch for mode shifts)\n- Preferences: Examples + theory + hands-on steps\n- Treat as: Senior technical partner, not assistant\n```\n\n---\n\n## Troubleshooting\n\n### \"conversations.json not found\"\n\nThe export ZIP contains multiple files. Make sure you're pointing to:\n```\nchatgpt-export\u002F\n├── conversations.json  \u003C-- This one\n├── user.json\n└── ...\n```\n\n### \"No conversations detected\"\n\nYour export might be empty or corrupted. Check:\n```bash\nhead -20 conversations.json\n```\n\nShould show: `[{\"title\": \"...\", \"messages\": [...]}, ...]`\n\n### \"All archetypes have similar confidence\"\n\nTry adjusting the cluster count:\n```bash\n# Too granular\npython3 scripts\u002Fanalyze_profile.py --archetypes 10\n\n# Try simpler\npython3 scripts\u002Fanalyze_profile.py --archetypes 3\n```\n\n### \"Analysis takes too long\"\n\nFor large conversation histories (10k+ messages):\n```bash\n# Sample for faster analysis\npython3 scripts\u002Fanalyze_profile.py \\\n  --input conversations.json \\\n  --sample 1000  # Analyze random 1000 conversations\n```\n\n---\n\n## Integration with OpenClaw\n\n### Automatic Profile Loading\n\nAdd to your OpenClaw workspace `AGENTS.md`:\n\n```markdown\n## On Session Start\n1. Read `~\u002F.openclaw\u002Fmy-cognitive-profile.json` if exists\n2. Adapt communication style to primary archetype\n3. Watch for context shift indicators\n```\n\n### Dynamic Mode Detection\n\nFor agents that can switch modes mid-conversation:\n\n```python\n# Pseudocode for agent integration\ndef detect_mode_shift(current_message, profile):\n    for shift in profile[\"context_shifts\"]:\n        if shift[\"trigger\"] in current_message:\n            return shift[\"to_archetype\"]\n    return profile[\"insights\"][\"primary_mode\"]\n```\n\n---\n\n## Contributing\n\nHave a new archetype that works well? Submit a PR with:\n1. Archetype definition in `examples\u002F`\n2. Sample data (anonymized)\n3. Validation that it clusters distinctly\n\n---\n\n## References\n\n- `references\u002Fmethodology.md` — Technical details on clustering algorithm\n- `references\u002Farchetype-taxonomy.md` — Full archetype definitions\n- `examples\u002F` — Sample profiles and configurations\n\n---\n\n*Built for humans who want their AI to truly understand them.* ???\n",{"name":5,"homepage":43,"metadata":44,"description":7},"https:\u002F\u002Fgithub.com\u002Fopenclaw\u002Fuser-cognitive-profiles",{"openclaw":45},{"tags":46,"emoji":48,"requires":49},[37,34,24,47,22],"optimization","???",{"bins":50},[32],"main","e7e336ac628c3e3bd771aff1aaa469c93778f94a","skills\u002Fsebastianffx\u002Fuser-cognitive-profiles",0,"https:\u002F\u002Fgithub.com\u002Fkbarbel640-del\u002Fskills","https:\u002F\u002Fgithub.com\u002Fkbarbel640-del","2026-08-23 22:39:37",{"source":8,"skillName":5,"commands":59,"prompt":69,"zipAvailable":70},[60,63,66],{"tool":61,"command":62},"npx","npx skills add kbarbel640-del\u002Fskills --skill 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