--- name: thinking-model-enhancer description: Advanced thinking model that improves decision-making speed and accuracy. Integrates with memory system to compare and integrate previous thinking models for continuous enhancement. when: "When user requests improved decision-making, enhanced thinking models, or when comparing and integrating thinking approaches" examples: - "启动高级思考模型" - "运行思维模型优化" - "比较和整合思考模型" - "提升决策准确性" - "优化思维过程" - "分析决策流程" metadata: {"openclaw": {"requires": {"bins": ["python3", "bash"], "anyBins": ["python3", "python"]}, "emoji": "🧠", "primaryEnv": ""}} ---

Thinking Model Enhancer

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.

When to use

• When user requests improved decision-making
• When enhanced thinking models are needed
• When comparing and integrating thinking approaches
• For optimizing decision-making processes
• For analyzing and improving cognitive frameworks

Thinking Model Framework

Multi-Stage Cognitive Processing Pipeline

1. Problem Analysis: Decompose the problem into manageable components
2. Model Selection: Choose appropriate thinking model based on problem characteristics
3. Information Collection: Gather relevant data and context from memory and external sources
4. Analysis & Evaluation: Process information using selected model with multi-perspective assessment
5. Synthesis: Combine findings into coherent understanding
6. Decision Formulation: Generate recommendations or conclusions
7. Memory Integration: Store results and lessons learned for future reference

🎯 Domain-Specific Thinking Modes (Extracted from Skills)

1️⃣ Research Thinking Mode (研究型思维模式)

Source: Extracted from Advanced Skill Creator skill (5-step research flow)

When to Use

• Creating new skills or features
• Comprehensive information gathering
• Solution comparison and selection
• Documentation generation

Research Flow Process

1. Memory Query: Query memory for similar past creations
2. Documentation Access: Consult official docs, guides, references
3. Public Research: Search ClawHub, GitHub, community solutions
4. Best Practices: Search for proven patterns and security practices
5. Solution Fusion: Compare and synthesize all sources
6. Output Generation: Produce structured, documented results

Research Priority Chain

Official Documentation > High-Quality Community Skills > Active Community Solutions > Self-Optimization

Output Template Pattern

【Final Recommended Solution】
【File Structure Preview】  
【Complete File Content】

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2️⃣ Diagnostic Thinking Mode (诊断型思维模式)

Source: Extracted from System Repair Expert skill (6-step repair flow)

When to Use

• System troubleshooting and repair
• Error diagnosis and resolution
• Configuration issues
• Performance problems

Diagnostic Flow Process

1. Memory Pattern Match: Query historical error patterns for quick classification
2. Problem Understanding: Fully comprehend issue scope and context
3. Official Solution Search: Check official docs, issues, release notes
4. Tool/Skill Match: Search for existing repair skills on ClawdHub
5. Community Solutions: Search GitHub for workarounds and patches
6. Last Resort: Create temporary fix script (only if all else fails)

Confidence Assessment System

| Confidence Level | Criteria | Action | |-----------------|----------|--------| | High (>90%) | Multiple sources confirm, tested solution | Recommend immediate execution | | Medium (60-90%) | Single source, reasonable confidence | Recommend testing before execution | | Low (<60%) | Unclear sources, requires research | Request more info or deep dive |

Emergency Level Classification

• P0 (Critical): Service down, immediate action required
• P1 (High): Major functionality impaired, urgent
• P2 (Medium): Minor issues, can schedule fix

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🔄 Thinking Model Feedback Loop

The thinking model now forms a complete cycle with skill implementations:

┌─────────────────────────────────────────────────────┐
│           Thinking Model Enhancer                    │
│  (Generic Framework + Domain-Specific Modes)         │
│                                                      │
│    ┌──────────────┐    ┌──────────────────────┐    │
│    │ Advanced     │───►│ Research Thinking    │    │
│    │ Skill Creator│    │ Mode (5-step flow)   │    │
│    └──────────────┘    └──────────────────────┘    │
│           ▲                      │                  │
│           │                      ▼                  │
│    ┌──────┴───────┐    ┌──────────────────────┐    │
│    │ System       │◄───│ Diagnostic Thinking  │    │
│    │ Repair Expert│    │ Mode (6-step flow)   │    │
│    └──────────────┘    └──────────────────────┘    │
│                                                      │
│    ┌──────────────────────────────────────────────┐│
│    │           Memory System Integration          ││
│    │   (Store patterns, query history, learn)     ││
│    └──────────────────────────────────────────────┘│
└─────────────────────────────────────────────────────┘

Feedback Mechanism:

1. Skills extract best practices → Enrich thinking model
2. Thinking model provides framework → Guide skill execution
3. Memory system stores patterns → Enable continuous improvement

Speed Optimization Strategies

• Parallel processing of multiple approaches
• Early elimination of unlikely options
• Pattern recognition for quick categorization
• Heuristic shortcuts for common scenarios
• Focused analysis on critical factors

Accuracy Enhancement Techniques

• Multi-angle evaluation
• Evidence weighting and validation
• Cross-validation verification
• Assumption checking protocols
• Confidence interval assessment

Memory System Integration

• Query memory system for similar past decisions
• Compare current approach with historical models
• Identify patterns and recurring themes
• Integrate successful elements from previous models
• Update model based on outcomes of past decisions
• Retrieve relevant past thinking models from memory
• Compare current approach with stored models
• Identify strengths and weaknesses in each approach
• Store refined model for future use

Thinking Model Comparison Algorithm

Input Analysis

• Parse the current problem or decision
• Identify key variables and constraints
• Determine decision complexity level

Model Selection Guide

Choose the appropriate thinking mode based on problem characteristics:

| Problem Type | Recommended Mode | Keywords to Detect | |-------------|------------------|-------------------| | Creating new features/skills | Research Thinking Mode | "写skill", "创建", "实现功能", "写一个让它" | | System troubleshooting | Diagnostic Thinking Mode | "启动失败", "报错", "错误", "修复", "问题" | | General decision-making | Generic Cognitive Pipeline | Default for unclear cases | | Complex analysis | Multi-Perspective Assessment | "分析", "比较", "评估" |

Auto-Detection: The system should automatically detect keywords and suggest appropriate thinking mode.

Hybrid Approach: For complex problems, combine multiple modes:

• Use Research Mode for information gathering
• Apply Diagnostic Mode for problem identification
• Use Generic Pipeline for final decision synthesis

Processing Stages

1. Rapid Assessment: Quick preliminary evaluation
2. Detailed Analysis: In-depth examination of options
3. Cross-Validation: Verification against multiple criteria
4. Optimization: Refinement based on goals
5. Integration: Combine with memory-stored models

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