A sophisticated diagnostic tool for measuring information diversity and redundancy within multi-agent group communications.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install agent-entropy-meter
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 agent-entropy-meter 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 Agent Entropy Meter?
The Agent Entropy Meter is a specialized analytical tool designed to quantify the efficiency of interactions between AI agents. As a key component of the Openclaw Skills ecosystem, it allows developers to measure the actual information value of agent messages by applying mathematical principles like Shannon Entropy and the Redundancy Ratio. This skill is vital for optimizing multi-agent systems where high communication overhead can lead to increased costs and decreased decision-making quality.
By identifying echo chambers and knowledge silos, this tool provides the data necessary to refine agent prompts and workflows. It helps ensure that each agent in a swarm contributes unique, high-entropy information rather than duplicating existing knowledge. Integrating this with other Openclaw Skills ensures a robust, measurable framework for agentic collaboration.
Agent Entropy Meter Use Cases
- Analyzing agent communication efficiency to identify and reduce token waste.
- Detecting knowledge silos or redundancy bottlenecks within complex agentic workflows.
- Evaluating multi-agent system health to ensure agents are contributing unique perspectives.
- Quantifying knowledge overlap when merging or expanding agent groups.
How Agent Entropy Meter Works
- The skill ingests message distributions and category data from participating agents.
- It calculates Shannon Entropy (H) to measure the uncertainty and information content in the message stream.
- The Redundancy Ratio (R) is computed to determine how much information is being repeated unnecessarily.
- It measures Inter-Agent Mutual Information to assess the independence of outputs between specific agent pairs.
- The system calculates the Knowledge Overlap Coefficient using Jaccard similarity of topic sets.
- A comprehensive report is generated, complete with interpretation guides and ASCII-based visualizations.
Agent Entropy Meter Setup
To utilize this functionality within your Openclaw Skills environment, require the module in your project logic. Ensure your agent framework can provide message category probabilities.
npm install @openclaw/agent-entropy-meter
const meter = require('./skills/agent-entropy-meter');
// Initialize with message distribution data
const entropy = meter.shannonEntropy([0.5, 0.3, 0.2]);
Agent Entropy Meter Data Schema & Taxonomy
The skill produces a structured data report focusing on four primary metrics to evaluate agent performance.
| Metric | Definition | Technical Basis |
|---|---|---|
| Shannon Entropy | Information content per message | H(X) = -Σ p(xi) log2 p(xi) |
| Redundancy Ratio | Percentage of repeated information | R = 1 - H(X) / H_max |
| Mutual Information | Shared info between two agents | I(A;B) = H(A) + H(B) - H(A,B) |
| Knowledge Overlap | Similarity of knowledge domains | Jaccard Similarity Index |
Agent Entropy Meter Advanced Features
- Automated report generation with built-in ASCII bar charts for terminal-based monitoring.
- Integration support for mermaid-visualizer and excalidraw-diagram for architectural redundancy mapping.
- Threshold-based interpretation guides to classify agent health (e.g., Identifying 'Echo Chambers' vs 'Diverse Info').
- Custom category mapping for specialized domain knowledge analysis within Openclaw Skills.
SKILL.md
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