A high-performance spatial intelligence engine that parses physical environments into actionable hardware matrices for AI agents.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install s2-universal-space-parser
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 s2-universal-space-parser 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 S2 Universal Space Parser?
The S2 Universal Space Parser is the definitive dictionary for spatial intelligence, capable of mapping 62 distinct spatial types into a structured 6-element hardware matrix. As a core component of the Openclaw Skills ecosystem, it serves as the front-end data collection station for the S2-SWM (Smart Space World Model), enabling AI agents to understand and interact with the physical world with unprecedented precision.
By leveraging a local MCP server, this skill bridges the gap between Large Language Models and physical hardware, facilitating real-time communication and causal data harvesting for advanced world-model training. It represents a significant step toward AGI by creating a fifth physical mapping paradigm for silicon-based life habitats.
S2 Universal Space Parser Use Cases
- Designing and planning smart home configurations for over 60 distinct room types.
- Harvesting causal physical state data (St to At to St+1) for training advanced world models.
- Integrating physical hardware control with MCP-enabled AI agents like Claude Desktop or Openclaw.
- Mapping zero-knowledge environments using the universal scanner integration.
- Visualizing spatial intelligence data through provided React and Next.js frontend hooks.
How S2 Universal Space Parser Works
- Initialize the local MCP server to establish a communication bridge between the AI agent and the hardware layer.
- Identify and parse the target space using the 62-type spatial intelligence dictionary.
- Execute physical actions through the hardware matrix while the Chronos Data Harvester records state transitions.
- Log all spatial causal events to the local s2_swm_training_data.jsonl file for future world model refinement.
- Optionally visualize live hardware status and harvested data via the provided Web UI components.
S2 Universal Space Parser Setup
To get started with these Openclaw Skills, ensure you have Python 3.10 or higher installed.
# Install the Model Context Protocol dependency
pip install mcp
# Launch the local MCP server for LLM integration
python3 s2_mcp_server.py
# Parse a target space configuration via CLI
python3 s2_parser_engine.py --space "Smart Living Room"
S2 Universal Space Parser Data Schema & Taxonomy
The skill organizes spatial data and causal events using the following structure:
| Component | Description | Data Format |
|---|---|---|
| Spatial Types | 62 unique environment classifications | Matrix Schema |
| Causal Logs | St to At to St+1 state transitions | JSONL |
| Hardware Matrix | 6-element control mapping | Array |
| Metadata | S2-DID legal identity (22-bit) | String |
| Training Data | Local S2-SWM causality records | .jsonl file |
S2 Universal Space Parser Advanced Features
- Local MCP server integration for low-latency stdio communication with AI agents.
- S2-Chronos Memzero data harvesting for continuous, local-first world-model training.
- Integrated support for S2-DID identity issuance via embodied robot gateways.
- Pre-built React/Tailwind frontend hooks for real-time spatial monitoring and dashboards.
- Multi-agent support for collaborative environment perception and physical execution.
SKILL.md
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