A
licenseNot graded
qualityB
maintenanceEnables agents to compute deterministic constriction kinetics, autonomic tone, and alertness metrics from pupillometry inputs through native JSON-RPC tool integration.
7
MIT
Oculomotor Smooth Pursuit Tracking Skill
Officialby alphaparkinc
This server does not currently have a deployable release on Glama. Follow the repository's setup instructions to install and configure it for your MCP client, including any required credentials and local dependencies.
Oculomotor Smooth Pursuit Tracking Skill
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.
Related MCP server: Quantitative Pupillary Light Reflex (PLR) Skill
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
This server cannot be deployed
Maintenance
ActivityMaintained
ResponsivenessNo issues
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