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."