A structured decision-making system for AI agents that utilizes weighted confidence scores, redline interceptions, and historical memory to optimize choices.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install kingofzhao-decision-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 kingofzhao-decision-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 Decision Framework Skill?
The Decision Framework is a sophisticated Openclaw Skills tool designed to move AI beyond simple intuition toward structured, logical reasoning. It operates on the core philosophy that a good decision is defined by a structured process using known information to maximize expected value, rather than just the final outcome. By separating knowns from unknowns, it provides a transparent path for AI agents to evaluate complex trade-offs.
This skill is particularly powerful for developers and teams building autonomous agents that need to handle high-stakes environments. It incorporates a weighted scoring algorithm that prioritizes risk mitigation—penalizing potential downsides more heavily than rewarding upsides—to ensure safety and stability in automated workflows.
Decision Framework Skill Use Cases
- Evaluating whether to adopt a new technology stack versus maintaining current stability.
- Risk assessment for irreversible system changes or production deployments.
- Automated business decision-making where confidence levels and financial trade-offs are involved.
- Building self-evolving agents that learn from past decision outcomes through historical memory logs.
How Decision Framework Skill Works
- K (Known): The agent identifies all known facts and assigns a confidence level to each.
- U (Unknown): The agent lists uncertainties, assessing their potential impact and the cost required to acquire missing information.
- W (Weigh): The system applies the Decision Scoring Formula, calculating the Option Score by weighting known benefits/costs against probabilistic unknown upsides and downsides.
- R (Record): The decision, reasoning, and expected results are logged to a markdown file for future post-mortem analysis.
Decision Framework Skill Setup
Install the skill via the Openclaw Skills hub using the following command:
clawhub install decision-framework
Decision Framework Skill Data Schema & Taxonomy
The skill organizes decision data using a chronological file-based memory system stored in the decisions/ directory:
| Feature | Description |
|---|---|
| File Format | {date}_{id}.md (Markdown) |
| Metadata | Problem description, known/unknown lists, and weighted scores. |
| Post-Mortem | Fields for actual results, deviation analysis, and lessons learned. |
| Redline Config | Boolean flags and wait-timers for high-risk or irreversible decisions. |
Decision Framework Skill Advanced Features
- Asymmetric Risk Preference: Automatically weights unknown downsides (1.5x) more heavily than upsides (0.5x) to prevent catastrophic failures.
- Redline Interception: Hard-coded safety checks that trigger 24-hour cooling-off periods for irreversible actions or mandatory consultations for social-impact decisions.
- Decision Memory Evolution: A feedback loop system that updates decision parameters based on historical deviation analysis.
- Confidence Thresholds: Automatically recommends information gathering if known info confidence falls below 50%.
SKILL.md
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