A sophisticated tool for transforming sequential state data into multi-resolution world models using Discrete Wavelet Transforms.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install wavelet-worldmodel-skill
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 wavelet-worldmodel-skill 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 Wavelet World Model?
The Wavelet World Model is a specialized utility for Openclaw Skills designed to process high-dimensional sequential state data. By utilizing Discrete Wavelet Transforms (DWT), this skill performs multi-resolution analysis to encode complex environment behaviors into a compact, usable format. It effectively balances high-frequency details, such as rapid environment changes, with low-frequency components that represent long-term dependencies.
This approach allows AI agents to maintain a more accurate and computationally efficient representation of their surroundings. Whether you are managing robotic control loops or tracking continuous state transitions, this skill provides the mathematical foundation needed for sophisticated predictive modeling within the Openclaw Skills ecosystem.
Wavelet World Model Use Cases
- Efficiently encoding robotic sensor data for real-time control systems.
- Developing predictive world models for complex environment simulations.
- Monitoring continuous state tracking in dynamic and noisy systems.
- Analyzing multi-resolution temporal features in high-dimensional datasets.
- Reducing the dimensionality of sequential inputs for faster AI processing.
How Wavelet World Model Works
- The skill receives high-dimensional sequential state inputs from the agent environment.
- It applies Discrete Wavelet Transforms (DWT) to the incoming data stream in real-time or batch mode.
- The signal is decomposed into multi-resolution components, separating high-frequency noise from low-frequency trends.
- It generates a compact world model representation that captures both rapid transitions and long-term dependencies.
- The processed state model is returned to the agent, enabling more informed decision-making within Openclaw Skills workflows.
Wavelet World Model Setup
To integrate the Wavelet World Model into your agent workflow, ensure your environment is ready to handle state-based commands. Use the following command structure to initialize the model:
# Initialize and process state inputs
openclaw run wavelet-model --input path/to/state_data.json
This skill is designed to work out-of-the-box with standard state sequences provided by Openclaw Skills compatible agents.
Wavelet World Model Data Schema & Taxonomy
The Wavelet World Model organizes its data by decomposing state vectors into specific coefficient sets. The resulting structure includes:
| Component | Description |
|---|---|
| Input States | Raw sequential data points representing the monitored environment. |
| Wavelet Coefficients | The multi-resolution DWT outputs consisting of Approximation and Detail coefficients. |
| Resolution Levels | The depth of the wavelet decomposition applied to capture spatial features. |
| Temporal Mappings | Metadata linking the compressed world model back to specific timestamps. |
Wavelet World Model Advanced Features
- Advanced multi-resolution temporal and spatial feature capture.
- Support for various wavelet families (e.g., Haar, Daubechies) to match signal complexity.
- High-efficiency encoding optimized for long-term dependency tracking.
- Seamless integration with the broader library of Openclaw Skills for multi-agent coordination.
- Dynamic adjustment of decomposition levels based on real-time signal-to-noise ratios.
SKILL.md
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