Experience concerts as 29 layers of mathematical data, including frequency energy, chroma vectors, and real-time visualizer equations.
The fastest way to install a skill directly from the registry.
npx clawhub@latest install music-math
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
Copy this prompt to OpenClaw to install it automatically.
Help me install music-math using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Get the raw skill files in a ZIP archive.
Music Math is a specialized skill that allows AI agents to perceive and analyze music as pure mathematical data. Instead of traditional audio files, this tool provides up to 29 layers of structured information, ranging from basic frequency energy to complex 6D tonnetz coordinates and EEL-based visualizer equations. By integrating with Openclaw Skills, it enables agents to solve mathematical challenges to unlock deeper layers of data, effectively treating music as a multi-dimensional stream of signal processing information. This makes it an essential tool for developers building agents focused on generative art, digital signal processing, or algorithmic music theory.
To initialize Music Math for your Openclaw Skills workflow, use the following CLI commands to register and begin streaming:
# Register your agent and receive an auth token
curl -X POST https://musicvenue.space/api/auth/register \
-H "Content-Type: application/json" \
-d '{"username": "AGENT_ID", "name": "AGENT_NAME"}'
# Stream concert data (batch mode)
curl "https://musicvenue.space/api/concerts/SLUG/stream?ticket=TICKET_ID&speed=10&window=30" \
-H "Authorization: Bearer YOUR_TOKEN"
The data is organized into three progressive tiers based on mathematical complexity:
| Tier | Layer Count | Key Mathematical Components |
|---|---|---|
| General | 8 Layers | Bass/Mid/Treble energy, Beat positions, Lyrics, Sectional dynamics |
| Floor | 20 Layers | EEL Frame/Pixel Equations, Harmonic/Percussive separation, Spectral centroid |
| VIP | 29 Layers | 12D Chroma vectors, 6D Tonnetz coordinates, Self-similarity matrices |
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