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

Problem Analysis: Decompose the problem into manageable components
Model Selection: Choose appropriate thinking model based on problem characteristics
Information Collection: Gather relevant data and context from memory and external sources
Analysis & Evaluation: Process information using selected model with multi-perspective assessment
Synthesis: Combine findings into coherent understanding
Decision Formulation: Generate recommendations or conclusions
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

Memory Query: Query memory for similar past creations
Documentation Access: Consult official docs, guides, references
Public Research: Search ClawHub, GitHub, community solutions
Best Practices: Search for proven patterns and security practices
Solution Fusion: Compare and synthesize all sources
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】

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

Memory Pattern Match: Query historical error patterns for quick classification
Problem Understanding: Fully comprehend issue scope and context
Official Solution Search: Check official docs, issues, release notes
Tool/Skill Match: Search for existing repair skills on ClawdHub
Community Solutions: Search GitHub for workarounds and patches
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

🔄 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:

Skills extract best practices → Enrich thinking model
Thinking model provides framework → Guide skill execution
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

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

Memory Operations

Query memory system for similar past decisions
Compare current model with historical models
Identify patterns and recurring themes
Integrate successful elements from previous models
Update model based on outcomes of past decisions

Implementation Requirements

Execute thinking model framework in sequence
Integrate with memory system for continuous learning
Balance speed and accuracy based on context
Document decision-making process for future reference
Store refined models in memory for ongoing improvement
Allow for customization based on problem domain
Enable comparison between different thinking approaches
Support iterative refinement of the model
Enable Skill Integration: Extract and incorporate best practices from skill implementations
Maintain Feedback Loop: Ensure bidirectional learning between thinking model and skills
Auto-Detection: Automatically detect problem type and suggest appropriate thinking mode
Confidence Documentation: Rate and document confidence levels for all recommendations

System Prompt Integration

When using this thinking model, incorporate the following system prompt elements:

"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."

Cognitive Application Guidelines

✅ Apply the multi-stage cognitive processing pipeline systematically
✅ Adjust the balance between speed and accuracy based on problem complexity
✅ Leverage memory integration to compare with previous similar decisions
✅ Use the speed optimization strategies when time is constrained
✅ Employ accuracy enhancement techniques for critical decisions
✅ Document the decision-making process for future learning
✅ Auto-detect problem type and apply appropriate domain-specific thinking mode
✅ Extract lessons from skills to continuously improve the thinking model
✅ Maintain feedback loop between thinking model and skill implementations

Enhanced Prompt for Skill Creation Context

When creating skills, activate Research Thinking Mode:

"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】."

Enhanced Prompt for Troubleshooting Context

When diagnosing issues, activate Diagnostic Thinking Mode:

"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."

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