A professional toolkit for logical reasoning, misinformation detection, and structured decision-making frameworks.
The fastest way to install a skill directly from the registry.
npx clawhub@latest install clear-thinking
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 clear-thinking using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Get the raw skill files in a ZIP archive.
The Clear Thinking & Decisions skill provides a robust architecture for navigating complex information and overcoming cognitive biases. By leveraging Openclaw Skills, developers and users can integrate proven mental models—such as the System 1 and System 2 framework—directly into their AI agent workflows. This skill is designed to transform the way agents process conflicting data, ensuring that decisions are based on evidence rather than emotional manipulation or logical fallacies.
At its core, the skill acts as a cognitive filter, identifying common bugs in human reasoning like the sunk cost fallacy or confirmation bias. Whether you are building an agent to assist with financial analysis or career coaching, this addition to your library of Openclaw Skills ensures that the output is grounded in intellectual humility and statistical literacy.
To add this capability to your agent environment, install the package via the Openclaw Skills registry using the following command:
npx clawhub install howtousehumans/clear-thinking
This skill requires the filesystem tool to manage session state and decision logs.
The skill organizes analysis and decision metadata through a structured session state to ensure consistency across interactions.
| Key | Type | Description |
|---|---|---|
| decision_type | String | Classifies the decision as Reversible or Irreversible. |
| information_level | Integer | Estimates current knowledge percentage (0-100). |
| fallacies_identified | Array | List of logical fallacies detected in the input context. |
| frameworks_applied | Array | Logic models used (e.g., 70% Rule, Regret Minimization). |
| commitment_period | String | The timeframe defined for post-decision review. |
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