A structured framework for evaluating multiple options and uncertain outcomes using probability-based expected value calculations.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install decision-trees
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-trees 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 Trees?
The Decision Trees skill provides a systematic approach to navigating complex scenarios where outcomes are uncertain. By utilizing visual tree-like structures, this tool allows users to map out decision nodes, chance events, and final payoffs to arrive at a mathematically sound recommendation. It is a vital component of the Openclaw Skills library for those needing to move beyond gut feelings toward data-driven logic.
This skill is particularly effective for business strategy, personal life changes, and technical operations. While it excels at structuring thoughts and identifying worst-case scenarios, it remains a transparent white box model that helps developers and stakeholders align on the logic behind critical choices.
Decision Trees Use Cases
- Business strategy evaluations such as product launches or market expansions.
- Personal career decisions, including job changes or relocation planning.
- Trading and investment analysis for position sizing and risk management.
- Operational planning for vendor selection and capacity management.
- Technical risk assessment for software architecture choices.
How Decision Trees Works
- Define the primary action options available to the user.
- Identify all possible outcomes or chance events following each action.
- Assign probabilities to each outcome, ensuring the sum of branches equals 100%.
- Attribute a numerical value or utility (e.g., USD, happiness units) to each end node.
- Calculate the Expected Value (EV) by summing the products of probabilities and values.
- Provide a recommendation based on the highest EV while considering qualitative risks.
Decision Trees Setup
To begin using this skill within your Openclaw Skills environment, ensure you have the Python dependencies installed. You can run the analysis interactively or via a configuration file.
# Run the interactive decision builder
python3 scripts/decision_tree.py --interactive
# Or process a predefined JSON tree structure
python3 scripts/decision_tree.py --json your_tree.json
Decision Trees Data Schema & Taxonomy
The skill uses a structured JSON format to define decision logic. This allows for reproducible analysis and integration with other Openclaw Skills workflows.
| Key | Type | Description |
|---|---|---|
decision |
String | The primary question or choice being evaluated. |
options |
Array | A list of possible paths or actions. |
name |
String | The identifier for an option or outcome. |
probability |
Float | The likelihood of an outcome (0.0 to 1.0). |
value |
Integer/Float | The numerical payoff or utility of an outcome. |
Decision Trees Advanced Features
- Interactive CLI mode for rapid brainstorming and iterative tree building.
- JSON-based automation for integrating decision logic into external pipelines.
- Expected Value calculation engine for objective, rational comparisons.
- Support for custom utility units (e.g., joy, points) beyond financial metrics.
- Multi-branch outcome mapping to handle complex, cascading chance events.
SKILL.md
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