{ "schemaVersion": "1.0", "item": { "slug": "thinking-model-enhancer", "name": "thinking-model-enhancer", "source": "tencent", "type": "skill", "category": "AI 智能", "sourceUrl": "https://clawhub.ai/xqicxx/thinking-model-enhancer", "canonicalUrl": "https://clawhub.ai/xqicxx/thinking-model-enhancer", "targetPlatform": "OpenClaw" }, "install": { "downloadMode": "redirect", "downloadUrl": "/downloads/thinking-model-enhancer", "sourceDownloadUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=thinking-model-enhancer", "sourcePlatform": "tencent", "targetPlatform": "OpenClaw", "installMethod": "Manual import", "extraction": "Extract archive", "prerequisites": [ "OpenClaw" ], "packageFormat": "ZIP package", "includedAssets": [ "CHANGELOG.md", "README.md", "SKILL.md", "PUBLISH_GUIDE.md", "UPLOAD_INSTRUCTIONS.md", "modules/p3_optimization.py" ], "primaryDoc": "SKILL.md", "quickSetup": [ "Download the package from Yavira.", "Extract the archive and review SKILL.md first.", "Import or place the package into your OpenClaw setup." ], "agentAssist": { "summary": "Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.", "steps": [ "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." ], "prompts": [ { "label": "New install", "body": "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." }, { "label": "Upgrade existing", "body": "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." } ] }, "sourceHealth": { "source": "tencent", "status": "healthy", "reason": "direct_download_ok", "recommendedAction": "download", "checkedAt": "2026-04-30T16:55:25.780Z", "expiresAt": "2026-05-07T16:55:25.780Z", "httpStatus": 200, "finalUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=network", "contentType": "application/zip", "probeMethod": "head", "details": { "probeUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=network", "contentDisposition": "attachment; filename=\"network-1.0.0.zip\"", "redirectLocation": null, "bodySnippet": null }, "scope": "source", "summary": "Source download looks usable.", "detail": "Yavira can redirect you to the upstream package for this source.", "primaryActionLabel": "Download for OpenClaw", "primaryActionHref": "/downloads/thinking-model-enhancer" }, "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." ] }, "downloadPageUrl": "https://openagent3.xyz/downloads/thinking-model-enhancer", "agentPageUrl": "https://openagent3.xyz/skills/thinking-model-enhancer/agent", "manifestUrl": "https://openagent3.xyz/skills/thinking-model-enhancer/agent.json", "briefUrl": "https://openagent3.xyz/skills/thinking-model-enhancer/agent.md" }, "agentAssist": { "summary": "Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.", "steps": [ "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." ], "prompts": [ { "label": "New install", "body": "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." }, { "label": "Upgrade existing", "body": "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." } ] }, "documentation": { "source": "clawhub", "primaryDoc": "SKILL.md", "sections": [ { "title": "Thinking Model Enhancer", "body": "Advanced thinking model designed to improve decision-making speed and accuracy. Integrates with memory system to compare and integrate previous thinking models for continuous enhancement." }, { "title": "When to use", "body": "When user requests improved decision-making\nWhen enhanced thinking models are needed\nWhen comparing and integrating thinking approaches\nFor optimizing decision-making processes\nFor analyzing and improving cognitive frameworks" }, { "title": "Multi-Stage Cognitive Processing Pipeline", "body": "Problem Analysis: Decompose the problem into manageable components\nModel Selection: Choose appropriate thinking model based on problem characteristics\nInformation Collection: Gather relevant data and context from memory and external sources\nAnalysis & Evaluation: Process information using selected model with multi-perspective assessment\nSynthesis: Combine findings into coherent understanding\nDecision Formulation: Generate recommendations or conclusions\nMemory Integration: Store results and lessons learned for future reference" }, { "title": "1️⃣ Research Thinking Mode (研究型思维模式)", "body": "Source: Extracted from Advanced Skill Creator skill (5-step research flow)\n\nWhen to Use\n\nCreating new skills or features\nComprehensive information gathering\nSolution comparison and selection\nDocumentation generation\n\nResearch Flow Process\n\nMemory Query: Query memory for similar past creations\nDocumentation Access: Consult official docs, guides, references\nPublic Research: Search ClawHub, GitHub, community solutions\nBest Practices: Search for proven patterns and security practices\nSolution Fusion: Compare and synthesize all sources\nOutput Generation: Produce structured, documented results\n\nResearch Priority Chain\n\nOfficial Documentation > High-Quality Community Skills > Active Community Solutions > Self-Optimization\n\nOutput Template Pattern\n\n【Final Recommended Solution】\n【File Structure Preview】 \n【Complete File Content】" }, { "title": "2️⃣ Diagnostic Thinking Mode (诊断型思维模式)", "body": "Source: Extracted from System Repair Expert skill (6-step repair flow)\n\nWhen to Use\n\nSystem troubleshooting and repair\nError diagnosis and resolution\nConfiguration issues\nPerformance problems\n\nDiagnostic Flow Process\n\nMemory Pattern Match: Query historical error patterns for quick classification\nProblem Understanding: Fully comprehend issue scope and context\nOfficial Solution Search: Check official docs, issues, release notes\nTool/Skill Match: Search for existing repair skills on ClawdHub\nCommunity Solutions: Search GitHub for workarounds and patches\nLast Resort: Create temporary fix script (only if all else fails)\n\nConfidence Assessment System\n\nConfidence LevelCriteriaActionHigh (>90%)Multiple sources confirm, tested solutionRecommend immediate executionMedium (60-90%)Single source, reasonable confidenceRecommend testing before executionLow (<60%)Unclear sources, requires researchRequest more info or deep dive\n\nEmergency Level Classification\n\nP0 (Critical): Service down, immediate action required\nP1 (High): Major functionality impaired, urgent\nP2 (Medium): Minor issues, can schedule fix" }, { "title": "🔄 Thinking Model Feedback Loop", "body": "The thinking model now forms a complete cycle with skill implementations:\n\n┌─────────────────────────────────────────────────────┐\n│ Thinking Model Enhancer │\n│ (Generic Framework + Domain-Specific Modes) │\n│ │\n│ ┌──────────────┐ ┌──────────────────────┐ │\n│ │ Advanced │───►│ Research Thinking │ │\n│ │ Skill Creator│ │ Mode (5-step flow) │ │\n│ └──────────────┘ └──────────────────────┘ │\n│ ▲ │ │\n│ │ ▼ │\n│ ┌──────┴───────┐ ┌──────────────────────┐ │\n│ │ System │◄───│ Diagnostic Thinking │ │\n│ │ Repair Expert│ │ Mode (6-step flow) │ │\n│ └──────────────┘ └──────────────────────┘ │\n│ │\n│ ┌──────────────────────────────────────────────┐│\n│ │ Memory System Integration ││\n│ │ (Store patterns, query history, learn) ││\n│ └──────────────────────────────────────────────┘│\n└─────────────────────────────────────────────────────┘\n\nFeedback Mechanism:\n\nSkills extract best practices → Enrich thinking model\nThinking model provides framework → Guide skill execution\nMemory system stores patterns → Enable continuous improvement" }, { "title": "Speed Optimization Strategies", "body": "Parallel processing of multiple approaches\nEarly elimination of unlikely options\nPattern recognition for quick categorization\nHeuristic shortcuts for common scenarios\nFocused analysis on critical factors" }, { "title": "Accuracy Enhancement Techniques", "body": "Multi-angle evaluation\nEvidence weighting and validation\nCross-validation verification\nAssumption checking protocols\nConfidence interval assessment" }, { "title": "Memory System Integration", "body": "Query memory system for similar past decisions\nCompare current approach with historical models\nIdentify patterns and recurring themes\nIntegrate successful elements from previous models\nUpdate model based on outcomes of past decisions\nRetrieve relevant past thinking models from memory\nCompare current approach with stored models\nIdentify strengths and weaknesses in each approach\nStore refined model for future use" }, { "title": "Input Analysis", "body": "Parse the current problem or decision\nIdentify key variables and constraints\nDetermine decision complexity level" }, { "title": "Model Selection Guide", "body": "Choose the appropriate thinking mode based on problem characteristics:\n\nProblem TypeRecommended ModeKeywords to DetectCreating new features/skillsResearch Thinking Mode\"写skill\", \"创建\", \"实现功能\", \"写一个让它\"System troubleshootingDiagnostic Thinking Mode\"启动失败\", \"报错\", \"错误\", \"修复\", \"问题\"General decision-makingGeneric Cognitive PipelineDefault for unclear casesComplex analysisMulti-Perspective Assessment\"分析\", \"比较\", \"评估\"\n\nAuto-Detection: The system should automatically detect keywords and suggest appropriate thinking mode.\n\nHybrid Approach: For complex problems, combine multiple modes:\n\nUse Research Mode for information gathering\nApply Diagnostic Mode for problem identification\nUse Generic Pipeline for final decision synthesis" }, { "title": "Processing Stages", "body": "Rapid Assessment: Quick preliminary evaluation\nDetailed Analysis: In-depth examination of options\nCross-Validation: Verification against multiple criteria\nOptimization: Refinement based on goals\nIntegration: Combine with memory-stored models" }, { "title": "Memory Operations", "body": "Query memory system for similar past decisions\nCompare current model with historical models\nIdentify patterns and recurring themes\nIntegrate successful elements from previous models\nUpdate model based on outcomes of past decisions" }, { "title": "Implementation Requirements", "body": "Execute thinking model framework in sequence\nIntegrate with memory system for continuous learning\nBalance speed and accuracy based on context\nDocument decision-making process for future reference\nStore refined models in memory for ongoing improvement\nAllow for customization based on problem domain\nEnable comparison between different thinking approaches\nSupport iterative refinement of the model\nEnable Skill Integration: Extract and incorporate best practices from skill implementations\nMaintain Feedback Loop: Ensure bidirectional learning between thinking model and skills\nAuto-Detection: Automatically detect problem type and suggest appropriate thinking mode\nConfidence Documentation: Rate and document confidence levels for all recommendations" }, { "title": "System Prompt Integration", "body": "When using this thinking model, incorporate the following system prompt elements:\n\n\"You are now an OpenClaw (formerly ClawDBot / Moltbot) thinking model specialist, implementing the advanced thinking model framework for enhanced decision-making. Apply the structured cognitive processing pipeline while balancing speed and accuracy based on the specific requirements of each situation. Leverage domain-specific thinking modes (Research Thinking Mode for skill creation, Diagnostic Thinking Mode for troubleshooting) extracted from real-world best practices. Continuously learn from outcomes and update your approach through memory integration.\"" }, { "title": "Cognitive Application Guidelines", "body": "✅ Apply the multi-stage cognitive processing pipeline systematically\n✅ Adjust the balance between speed and accuracy based on problem complexity\n✅ Leverage memory integration to compare with previous similar decisions\n✅ Use the speed optimization strategies when time is constrained\n✅ Employ accuracy enhancement techniques for critical decisions\n✅ Document the decision-making process for future learning\n✅ Auto-detect problem type and apply appropriate domain-specific thinking mode\n✅ Extract lessons from skills to continuously improve the thinking model\n✅ Maintain feedback loop between thinking model and skill implementations" }, { "title": "Enhanced Prompt for Skill Creation Context", "body": "When creating skills, activate Research Thinking Mode:\n\n\"When creating skills or features, follow the Research Thinking Mode: 1) Query memory for similar past creations, 2) Consult official documentation, 3) Research public solutions on ClawHub/GitHub, 4) Compare best practices, 5) Synthesize and output structured solution. Apply the output template: 【Final Recommended Solution】→【File Structure Preview】→【Complete File Content】.\"" }, { "title": "Enhanced Prompt for Troubleshooting Context", "body": "When diagnosing issues, activate Diagnostic Thinking Mode:\n\n\"When troubleshooting problems, follow the Diagnostic Thinking Mode: 1) Query memory for similar error patterns, 2) Understand the full problem scope, 3) Search official solutions, 4) Check ClawdHub for repair skills, 5) Search community workarounds, 6) Create last-resort fix only if needed. Assess confidence level (High/Medium/Low) for each recommendation.\"" } ], "body": "Thinking Model Enhancer\n\nAdvanced thinking model designed to improve decision-making speed and accuracy. Integrates with memory system to compare and integrate previous thinking models for continuous enhancement.\n\nWhen to use\nWhen user requests improved decision-making\nWhen enhanced thinking models are needed\nWhen comparing and integrating thinking approaches\nFor optimizing decision-making processes\nFor analyzing and improving cognitive frameworks\nThinking Model Framework\nMulti-Stage Cognitive Processing Pipeline\nProblem Analysis: Decompose the problem into manageable components\nModel Selection: Choose appropriate thinking model based on problem characteristics\nInformation Collection: Gather relevant data and context from memory and external sources\nAnalysis & Evaluation: Process information using selected model with multi-perspective assessment\nSynthesis: Combine findings into coherent understanding\nDecision Formulation: Generate recommendations or conclusions\nMemory Integration: Store results and lessons learned for future reference\n🎯 Domain-Specific Thinking Modes (Extracted from Skills)\n1️⃣ Research Thinking Mode (研究型思维模式)\n\nSource: Extracted from Advanced Skill Creator skill (5-step research flow)\n\nWhen to Use\nCreating new skills or features\nComprehensive information gathering\nSolution comparison and selection\nDocumentation generation\nResearch Flow Process\nMemory Query: Query memory for similar past creations\nDocumentation Access: Consult official docs, guides, references\nPublic Research: Search ClawHub, GitHub, community solutions\nBest Practices: Search for proven patterns and security practices\nSolution Fusion: Compare and synthesize all sources\nOutput Generation: Produce structured, documented results\nResearch Priority Chain\nOfficial Documentation > High-Quality Community Skills > Active Community Solutions > Self-Optimization\n\nOutput Template Pattern\n【Final Recommended Solution】\n【File Structure Preview】 \n【Complete File Content】\n\n2️⃣ Diagnostic Thinking Mode (诊断型思维模式)\n\nSource: Extracted from System Repair Expert skill (6-step repair flow)\n\nWhen to Use\nSystem troubleshooting and repair\nError diagnosis and resolution\nConfiguration issues\nPerformance problems\nDiagnostic Flow Process\nMemory Pattern Match: Query historical error patterns for quick classification\nProblem Understanding: Fully comprehend issue scope and context\nOfficial Solution Search: Check official docs, issues, release notes\nTool/Skill Match: Search for existing repair skills on ClawdHub\nCommunity Solutions: Search GitHub for workarounds and patches\nLast Resort: Create temporary fix script (only if all else fails)\nConfidence Assessment System\nConfidence Level\tCriteria\tAction\nHigh (>90%)\tMultiple sources confirm, tested solution\tRecommend immediate execution\nMedium (60-90%)\tSingle source, reasonable confidence\tRecommend testing before execution\nLow (<60%)\tUnclear sources, requires research\tRequest more info or deep dive\nEmergency Level Classification\nP0 (Critical): Service down, immediate action required\nP1 (High): Major functionality impaired, urgent\nP2 (Medium): Minor issues, can schedule fix\n🔄 Thinking Model Feedback Loop\n\nThe thinking model now forms a complete cycle with skill implementations:\n\n┌─────────────────────────────────────────────────────┐\n│ Thinking Model Enhancer │\n│ (Generic Framework + Domain-Specific Modes) │\n│ │\n│ ┌──────────────┐ ┌──────────────────────┐ │\n│ │ Advanced │───►│ Research Thinking │ │\n│ │ Skill Creator│ │ Mode (5-step flow) │ │\n│ └──────────────┘ └──────────────────────┘ │\n│ ▲ │ │\n│ │ ▼ │\n│ ┌──────┴───────┐ ┌──────────────────────┐ │\n│ │ System │◄───│ Diagnostic Thinking │ │\n│ │ Repair Expert│ │ Mode (6-step flow) │ │\n│ └──────────────┘ └──────────────────────┘ │\n│ │\n│ ┌──────────────────────────────────────────────┐│\n│ │ Memory System Integration ││\n│ │ (Store patterns, query history, learn) ││\n│ └──────────────────────────────────────────────┘│\n└─────────────────────────────────────────────────────┘\n\n\nFeedback Mechanism:\n\nSkills extract best practices → Enrich thinking model\nThinking model provides framework → Guide skill execution\nMemory system stores patterns → Enable continuous improvement\nSpeed Optimization Strategies\nParallel processing of multiple approaches\nEarly elimination of unlikely options\nPattern recognition for quick categorization\nHeuristic shortcuts for common scenarios\nFocused analysis on critical factors\nAccuracy Enhancement Techniques\nMulti-angle evaluation\nEvidence weighting and validation\nCross-validation verification\nAssumption checking protocols\nConfidence interval assessment\nMemory System Integration\nQuery memory system for similar past decisions\nCompare current approach with historical models\nIdentify patterns and recurring themes\nIntegrate successful elements from previous models\nUpdate model based on outcomes of past decisions\nRetrieve relevant past thinking models from memory\nCompare current approach with stored models\nIdentify strengths and weaknesses in each approach\nStore refined model for future use\nThinking Model Comparison Algorithm\nInput Analysis\nParse the current problem or decision\nIdentify key variables and constraints\nDetermine decision complexity level\nModel Selection Guide\n\nChoose the appropriate thinking mode based on problem characteristics:\n\nProblem Type\tRecommended Mode\tKeywords to Detect\nCreating new features/skills\tResearch Thinking Mode\t\"写skill\", \"创建\", \"实现功能\", \"写一个让它\"\nSystem troubleshooting\tDiagnostic Thinking Mode\t\"启动失败\", \"报错\", \"错误\", \"修复\", \"问题\"\nGeneral decision-making\tGeneric Cognitive Pipeline\tDefault for unclear cases\nComplex analysis\tMulti-Perspective Assessment\t\"分析\", \"比较\", \"评估\"\n\nAuto-Detection: The system should automatically detect keywords and suggest appropriate thinking mode.\n\nHybrid Approach: For complex problems, combine multiple modes:\n\nUse Research Mode for information gathering\nApply Diagnostic Mode for problem identification\nUse Generic Pipeline for final decision synthesis\nProcessing Stages\nRapid Assessment: Quick preliminary evaluation\nDetailed Analysis: In-depth examination of options\nCross-Validation: Verification against multiple criteria\nOptimization: Refinement based on goals\nIntegration: Combine with memory-stored models\nMemory Operations\nQuery memory system for similar past decisions\nCompare current model with historical models\nIdentify patterns and recurring themes\nIntegrate successful elements from previous models\nUpdate model based on outcomes of past decisions\nImplementation Requirements\nExecute thinking model framework in sequence\nIntegrate with memory system for continuous learning\nBalance speed and accuracy based on context\nDocument decision-making process for future reference\nStore refined models in memory for ongoing improvement\nAllow for customization based on problem domain\nEnable comparison between different thinking approaches\nSupport iterative refinement of the model\nEnable Skill Integration: Extract and incorporate best practices from skill implementations\nMaintain Feedback Loop: Ensure bidirectional learning between thinking model and skills\nAuto-Detection: Automatically detect problem type and suggest appropriate thinking mode\nConfidence Documentation: Rate and document confidence levels for all recommendations\nSystem Prompt Integration\n\nWhen using this thinking model, incorporate the following system prompt elements:\n\n\"You are now an OpenClaw (formerly ClawDBot / Moltbot) thinking model specialist, implementing the advanced thinking model framework for enhanced decision-making. Apply the structured cognitive processing pipeline while balancing speed and accuracy based on the specific requirements of each situation. Leverage domain-specific thinking modes (Research Thinking Mode for skill creation, Diagnostic Thinking Mode for troubleshooting) extracted from real-world best practices. Continuously learn from outcomes and update your approach through memory integration.\"\n\nCognitive Application Guidelines\n✅ Apply the multi-stage cognitive processing pipeline systematically\n✅ Adjust the balance between speed and accuracy based on problem complexity\n✅ Leverage memory integration to compare with previous similar decisions\n✅ Use the speed optimization strategies when time is constrained\n✅ Employ accuracy enhancement techniques for critical decisions\n✅ Document the decision-making process for future learning\n✅ Auto-detect problem type and apply appropriate domain-specific thinking mode\n✅ Extract lessons from skills to continuously improve the thinking model\n✅ Maintain feedback loop between thinking model and skill implementations\nEnhanced Prompt for Skill Creation Context\n\nWhen creating skills, activate Research Thinking Mode:\n\n\"When creating skills or features, follow the Research Thinking Mode: 1) Query memory for similar past creations, 2) Consult official documentation, 3) Research public solutions on ClawHub/GitHub, 4) Compare best practices, 5) Synthesize and output structured solution. Apply the output template: 【Final Recommended Solution】→【File Structure Preview】→【Complete File Content】.\"\n\nEnhanced Prompt for Troubleshooting Context\n\nWhen diagnosing issues, activate Diagnostic Thinking Mode:\n\n\"When troubleshooting problems, follow the Diagnostic Thinking Mode: 1) Query memory for similar error patterns, 2) Understand the full problem scope, 3) Search official solutions, 4) Check ClawdHub for repair skills, 5) Search community workarounds, 6) Create last-resort fix only if needed. Assess confidence level (High/Medium/Low) for each recommendation.\"" }, "trust": { "sourceLabel": "tencent", "provenanceUrl": "https://clawhub.ai/xqicxx/thinking-model-enhancer", "publisherUrl": "https://clawhub.ai/xqicxx/thinking-model-enhancer", "owner": "xqicxx", "version": "1.0.0", "license": null, "verificationStatus": "Indexed source record" }, "links": { "detailUrl": "https://openagent3.xyz/skills/thinking-model-enhancer", "downloadUrl": "https://openagent3.xyz/downloads/thinking-model-enhancer", "agentUrl": "https://openagent3.xyz/skills/thinking-model-enhancer/agent", "manifestUrl": "https://openagent3.xyz/skills/thinking-model-enhancer/agent.json", "briefUrl": "https://openagent3.xyz/skills/thinking-model-enhancer/agent.md" } }