A professional investment decision-making tool inspired by Charlie Munger's Poor Charlie's Almanack to eliminate cognitive biases and evaluate opportunities through multidisciplinary mental models.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install decision-checklist
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 decision-checklist 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 Decision Checklist: Charlie Munger Investment Framework?
The decision-checklist is a specialized tool within the Openclaw Skills ecosystem designed for investors and financial analysts. It systematically applies the principles of Charlie Munger and Warren Buffett to vet investment ideas, focusing heavily on the Circle of Competence and Inversion Thinking.
By processing raw investment ideas through a rigorous filter of 25 cognitive biases and multidisciplinary models (ranging from biology to engineering), this skill helps users move beyond emotional trading. It provides a structured, quantitative score for qualitative ideas, ensuring that every position in a portfolio is backed by sound, objective reasoning and a clear margin of safety.
Decision Checklist: Charlie Munger Investment Framework Use Cases
- Pre-investment vetting to identify hidden cognitive biases like anchoring or confirmation bias.
- Assessing if a specific company or industry falls within your personal Circle of Competence.
- Performing inversion analysis to understand potential points of failure before committing capital.
- Applying multidisciplinary mental models to gain deeper insights into a company's moat and long-term sustainability.
- Determining optimal position sizing based on a weighted risk-reward score.
How Decision Checklist: Charlie Munger Investment Framework Works
- The user provides an investment idea, company details, supporting reasons, known risks, and planned position size.
- The skill performs a Circle of Competence Check to determine the user's depth of understanding of the business model.
- It executes a Cognitive Bias Scan, checking the investment thesis against core biases like incentive-caused bias and social proof.
- Inversion Thinking is applied to simulate failure scenarios and identify what could destroy the business.
- The tool applies Multidisciplinary Models (Math, Psychology, Economics, Biology, Engineering) to the data to find unique insights.
- A final weighted score is generated, resulting in a formal investment recommendation ranging from "Strongly Recommended" to "Not Recommended."
Decision Checklist: Charlie Munger Investment Framework Setup
To integrate this framework into your AI agent environment using Openclaw Skills, use the following configuration:
# Example of triggering the checklist via CLI
openclaw-agent invoke decision-checklist --params '{"investment_idea": "Long position on Company X", "company": "Company X", "reasons": ["High growth", "Strong brand"], "risks": ["Regulatory changes"], "position_size": "10%", "time_horizon": "5 years"}'
Decision Checklist: Charlie Munger Investment Framework Data Schema & Taxonomy
The skill organizes analysis into five key metadata domains:
| Domain | Content Description |
|---|---|
| Circle of Competence | Scores (1-5) on industry understanding and predictability. |
| Cognitive Bias Map | Evidence-based check of 8 core psychological misjudgments. |
| Inversion Analysis | List of failure triggers and competitive threats. |
| Mental Models | Insights derived from Math (Compound interest), Biology (Evolution), and more. |
| Decision Score | A final weighted table (0-100) combining all the above metrics. |
Decision Checklist: Charlie Munger Investment Framework Advanced Features
- Multi-agent support for 'Red Teaming' investment ideas where one agent defends the thesis and this skill attacks it.
- Custom weight adjustment for the scoring matrix (e.g., increasing the weight of Margin of Safety).
- Historical re-evaluation hooks to record and review past decisions for post-mortem analysis.
- Automated inversion prompts to force the AI to simulate 'competitor' strategies against the target company.
SKILL.md
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