genpark-personal-agent-oculomotor-smooth-pursuit-velocity-gain-skill

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SUMMARY

Deterministic personal agent skill for evaluating oculomotor smooth pursuit velocity gain, retinal velocity error, and saccadic substitution kinetics.

README.md

Oculomotor Smooth Pursuit Tracking Skill

License: Apache-2.0
Python 3.10+
Zero Dependencies
MCP Protocol Ready
OpenClaw Certified

Zero-dependency sovereign personal agent skill for non-invasive evaluation of Oculomotor Smooth Pursuit gain, retinal velocity error, and saccadic substitution kinetics.

flowchart TD
    TargetStimulus["Dynamic Visual Target Trajectory (Deg/s)"] --> EyeTracker["High-Speed Video Oculography (VOG)"]
    EyeTracker --> PursuitExtract["Decomposition: Smooth Pursuit vs Catch-Up Saccades"]
    PursuitExtract --> GainCalc["Velocity Gain & Phase Lag Calculations"]
    GainCalc --> PrecisionScore["Oculomotor Tracking Precision Engine"]
    PrecisionScore --> Agent["OpenClaw / Hermes Personal Agent Actions"]

Features

  • Deterministic Math: Pure Python standard library implementation with zero external dependencies.
  • Oculomotor Physiology Standards: Implements clinical eye-tracking velocity gain and phase angle calculations.
  • Visual Fatigue & Attention Biomarker: Quantifies cognitive decoupling and saccadic substitution.
  • Model Context Protocol (MCP): Native JSON-RPC tool integration for LLMs and autonomous agents.

Quickstart

from client import OculomotorSmoothPursuitClient

client = OculomotorSmoothPursuitClient()
result = client.evaluate_smooth_pursuit(
    target_velocity_deg_s=20.0,
    eye_smooth_velocity_deg_s=19.4
)
print("Pursuit Gain:", result["smooth_pursuit_gain"])
print("Classification:", result["classification"])

License

Apache-2.0

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