A spatial light radar that uses real network broadcasts to discover, map, and analyze smart lighting environments in a 4sqm voxel memory grid.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install s2-light-perception
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-light-perception 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 Light Perception?
S2 Light Perception is a high-performance spatial light radar designed to bridge the gap between physical smart hardware and digital environmental awareness. Unlike simulated tools, it utilizes authentic UDP broadcasts and SSDP multicast to actively scan a Local Area Network for smart light sources. By implementing native, zero-dependency Python protocols for Hue and Wiz systems, it provides a lightweight yet powerful way to integrate physical light states into the S2-SP-OS ecosystem.
This Openclaw Skills integration focuses on spatial memory, creating a 4sqm voxel light memzero that encapsulates hardware states into actionable spatial data. Beyond simple control, it features edge-computed offline linkage to assess how current lighting conditions impact human circadian rhythms and melatonin levels, offering intelligent suggestions for healthier living environments.
S2 Light Perception Use Cases
- Real-time discovery of unassigned Hue or Wiz smart lights on a local network.
- Mapping physical light intensity into a digital voxel grid for spatial computing.
- Monitoring environmental light impact on biological rhythms and melatonin production.
- Auditing local network IoT device states without relying on cloud-based APIs.
- Automating lighting adjustments based on 4sqm spatial zone triggers.
How S2 Light Perception Works
- Active Discovery: The skill initiates a real UDP broadcast (Port 38899) and SSDP multicast (Port 1900) to identify compatible smart bulbs on the LAN.
- Protocol Negotiation: It identifies the device protocol (Hue over HTTP or Wiz over UDP) and establishes a direct socket connection.
- Spatial Mapping: Hardware states such as brightness, color temperature, and connectivity are captured and transformed into a voxel-based spatial memory structure.
- Insight Generation: The system analyzes the data to estimate lux levels and provides offline linkage suggestions based on circadian health metrics.
- State Feedback: Detailed hardware status and error logs are parsed, falling back to safe defaults (e.g., 5 lux) if a device is disconnected.
S2 Light Perception Setup
To use this skill within the Openclaw Skills framework, ensure you have Python 3 installed. No third-party libraries are required as it uses native socket implementations.
# Step 1: Scan the network for available light sources
python3 light.py --mode discover
# Step 2: Read specific device state and map to a zone
python3 light.py --mode read --ip <DEVICE_IP> --protocol wiz --zone bedroom --grid x1_y1 --consent-granted true
S2 Light Perception Data Schema & Taxonomy
The skill organizes lighting data into a structured spatial taxonomy for precise tracking:
| Attribute | Description | Data Type |
|---|---|---|
voxel_id |
Identifier for the 4sqm grid coordinate | String (e.g., x1_y1) |
estimated_lux |
Calculated light intensity based on device state | Integer (5 to 1000+) |
protocol |
Communication method used (hue/wiz/mqtt) | String |
vendor_specific_nl |
Raw hardware logs and error details | JSON Object |
linkage_suggestions |
Biometric-based lighting recommendations | Array |
S2 Light Perception Advanced Features
- Real Active Radar: Uses authentic network packets rather than cached or simulated data for 100% accuracy.
- Zero-Dependency Architecture: Built using native Python
socketandurllibto ensure maximum compatibility and speed. - Memzero Voxel Wrapping: Advanced spatial memory technology that treats light as a persistent physical object in a coordinate system.
- Circadian Edge Computing: Local processing of light-to-melatonin impact without sending data to external cloud servers.
- Safe State Handling: Intelligent fallback mechanisms that adjust estimated lux levels when network interruptions occur.
SKILL.md
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