Thinking Framework allows AI to adopt the deep cognitive architecture and psychological operating logic of specific thinkers, organizations, or philosophies to reason through problems from their unique perspective.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install thinking-framework
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install thinking-framework using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Thinking Framework?
Thinking Framework is a sophisticated cognitive emulation tool designed for Openclaw Skills that moves beyond surface-level persona acting. Instead of simply quoting a historical figure or organization, this skill performs a deep 15-dimension excavation of a target's mental operating system. It maps mental models, decision heuristics, and even underlying psychological drivers to allow the AI to reason from within that specific architecture.
By synthesizing cognitive layers with psychological excavation, the AI provides insights that reflect the actual logic and trade-offs of elite thinkers. This ensures that the output is not just a summary of what someone said, but a live application of how they think, applied directly to your specific modern challenges or technical problems.
Thinking Framework Use Cases
- Strategizing business decisions by loading the framework of successful founders or legendary CEOs.
- Stress-testing project plans using the rigorous mental models of specific military or engineering organizations.
- Analyzing complex ethical dilemmas through the lens of specific philosophical schools like Stoicism or Existentialism.
- Solving technical or scientific hurdles by adopting the reasoning moves and evidence standards of specific disciplines.
- Reframing personal or professional challenges by identifying the signature insights and defining trade-offs of world-class thinkers.
How Thinking Framework Works
- Request Decoding: The skill identifies the target (thinker, org, or system), the relevant epoch, and the required depth of the analysis.
- Evidence Mapping: It categorizes available data into Grade A (primary sources), Grade B (behavioral records), Grade C (inferred patterns), and Grade X (mythology) to ensure technical truth.
- 15-Dimension Excavation: The AI processes the target through a Cognitive Layer (C1–C7) and a Psychological Layer (P1–P8), mapping everything from risk posture to core motivational drives.
- Synthesis Generation: It identifies a Signature Insight, a Defining Trade-off, and a Load-bearing Paradox unique to that framework.
- Activation: A Framework Card is presented to the user, and the AI enters an active reasoning mode where every subsequent response is generated from inside that loaded cognitive system.
Thinking Framework Setup
To integrate Thinking Framework into your environment, ensure your Openclaw Skills library is up to date and reference the core layers in your prompt sequence:
# Standard load includes Layers 1, 2, and 3
openclaw install thinking-framework
Trigger the activation by using commands such as "load [Target] framework" or "think through [Target]'s lens" within your AI session.
Thinking Framework Data Schema & Taxonomy
The skill organizes its cognitive architecture across a multi-layered reference system to ensure modularity and depth:
| Layer | Component | Description |
|---|---|---|
| Layer 1 | Cognitive Architecture | 7-dimension conscious logic (Mental models, heuristics, etc.) |
| Layer 2 | Psychological Excavation | 8-dimension deep drivers (Core wounds, dominant drives, shadow) |
| Layer 3 | Operational Protocol | Behavioral guidelines for active framework mode |
| Layer 4 | Synthesis Integration | Methods for blending multiple frameworks or deep composite loads |
| Model Guidance | Optimization | Calibration settings for frontier, mid-tier, and small local models |
Thinking Framework Advanced Features
- Composite Blending: Use the
blend [A] with [B]command to synthesize two different thinking styles into a new, unique hybrid framework. - Failure Mapping: Perform a structural blind spot analysis to see where a specific framework misfires or breaks down.
- Psychological Deep-Dive: Expand the internal motivational engine analysis for a more nuanced understanding of why certain decisions are made.
- Multi-Epoch Loading: Specify a time qualifier like "early career" or "wartime" to capture shifts in a thinker's cognitive evolution.
- Evidence Quality Reporting: Query the AI's confidence levels to distinguish between documented facts and behavioral inferences.
SKILL.md
Loading
Related Openclaw Skills
ifly-translate
A streamlined Python-based translation skill leveraging iFlytek's Machine Translation API for high-fidelity cross-lingual communication.
a2a-Market Stake Freeze
A specialized rule engine for managing the stake lock lifecycle, ensuring participant commitment and security during A2A negotiations and order executions.
Neomano VPS Monitoring
A robust monitoring integration for DigitalOcean Droplets that fetches real-time performance metrics via the DigitalOcean API.
Resume Evaluator Pro
An AI-driven recruitment assistant that batch processes resumes and ranks candidates based on custom job descriptions.
Data Analysis Workflow
A comprehensive workspace template designed to standardize data science projects, from exploratory analysis to model deployment.
Pharmacoeconomic Evaluation Skill
A professional toolkit for conducting comprehensive pharmacoeconomic evaluations and health technology assessments using standardized models and Python-based analytical scripts.
Featured*
