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organvm-i-theoria
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movement-notation-systems

Designs systems for encoding, analyzing, and generating choreographic movement using Laban notation and procedural animation.

Updated May 16, 2026
Search Tags:#choreography#motion capture#movement analysis#procedural animation#bvh#labanotation#laban movement analysis
One-click install
npx skills add https://github.com/organvm-i-theoria/_agent-ontology --skill movement-notation-systems-organvm-i-theoria
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: movement-notation-systems
Source: https://github.com/organvm-i-theoria/_agent-ontology/tree/main/.agents/skills/movement-notation-systems
Command: npx skills add https://github.com/organvm-i-theoria/_agent-ontology --skill movement-notation-systems-organvm-i-theoria

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve? Choreographers, animators, and movement researchers struggle to encode human movement in a structured, machine-readable way that preserves both quantitative positions and qualitative dynamics. This Skill provides frameworks for representing, analyzing, and generating movement using established notation systems and computational geometry. ## Core Features & Use Cases - Laban Movement Analysis: Implements the Body, Space, Effort, and Shape components of LMA, including the eight Basic Effort Actions and 27-direction kinesphere model. - Computational Movement Representation: Provides skeletal data structures, joint angle computation, and motion trajectory analysis with Effort feature extraction. - Procedural Choreography Generation: Generates movement phrases from rules, animates actions with specified Effort qualities, and creates spatial floor patterns like spirals and figure-eights. - Use Case: A dance researcher can convert motion capture data into Laban Effort annotations, or an animator can generate a character walk cycle with 'light, sustained, indirect' movement qualities. ## Quick Start Ask the AI to design a movement notation system that encodes a choreographic phrase using Laban Effort qualities and outputs a readable score.

Frequently Asked Questions about movement-notation-systems

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I encode choreography in a machine-readable format?▼

Use a skeletal hierarchy with joint positions and rotations per frame, then layer Laban notation for qualitative dynamics. The Skill provides Skeleton and MotionTrajectory classes that store poses at a fixed frame rate and support Effort feature extraction.

What is Laban Movement Analysis used for?▼

Laban Movement Analysis describes movement through four categories: Body, Space, Effort, and Shape. It is used in choreography, dance therapy, animation, and movement research to capture qualitative dynamics like weight, time, space, and flow that raw position data misses.

How do I parse BVH motion capture files in Python?▼

Parse the HIERARCHY section to build the joint tree with offsets and channels, then read the MOTION section for frame data. The Skill's reference documentation includes a complete BVH parser plus examples for C3D, FBX, and MediaPipe landmark formats.

Can I generate procedural animation from Effort qualities?▼

Yes. The EffortAnimator maps Laban Effort factors to animation parameters: time controls duration and easing, weight shapes acceleration curves, space selects linear or curved paths, and flow adds or removes micro-pauses.

What are the limitations of motion capture versus notation systems?▼

Motion capture records exact coordinates but loses choreographic intent and qualitative dynamics. Notation systems like Labanotation preserve intent and support interpretation, but require trained readers and are less precise for reconstruction.

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