A structured thinking engine that applies world-class mental models to surface non-obvious insights for complex decisions.
The fastest way to install a skill directly from the registry.
npx clawhub@latest install decision-mental-models
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
Copy this prompt to OpenClaw to install it automatically.
Help me install decision-mental-models using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Get the raw skill files in a ZIP archive.
The Decision Mental Models skill transforms your AI agent into a sophisticated reasoning partner. Rather than providing generic advice or simple answers, it utilizes a library of 20 established cognitive frameworks—including Second-Order Thinking, Occam's Razor, and the Pareto Principle—to deconstruct problems. By matching your specific situation to the most relevant models, it reveals the underlying structure of a dilemma and identifies hidden blind spots.
This skill is designed for high-stakes environments where intuitive thinking often fails. It forces a rigorous examination of assumptions and downstream effects, ensuring that every insight is grounded in logical frameworks used by world-class investors and strategists. Using this specialized tool within Openclaw Skills ensures your decision-making process is consistent, transparent, and intellectually honest.
To integrate this capability into your workflow using Openclaw Skills, ensure your environment is configured to point to the skill repository.
# Example command to add the mental models skill to your local agent
openclaw install decision-mental-models
Ensure your config.yaml or relevant settings file references the skill's logic to trigger on complex reasoning queries.
The skill organizes its reasoning through a structured taxonomy of cognitive frameworks. Each model in the library is defined by the following metadata:
| Attribute | Description |
|---|---|
| Model Name | The formal designation (e.g., Sunk Cost Fallacy). |
| Logic Definition | The core principle or mechanism of the framework. |
| Trigger Condition | Specific problem archetypes where the model is most effective. |
| Core Question | The primary investigative prompt the model uses to challenge a situation. |
| Worked Example | A historical or real-world benchmark for calibration. |
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