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genpark-personal-agent-indoor-co2-cognitive-ventilation-skill

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Python
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genpark-personal-agent-indoor-co2-cognitive-ventilation-skill

Tests License Python Dependencies Coverage

GenPark Personal Agent Indoor CO2 Cognitive Ventilation models indoor air quality mass balances and Satish cognitive impairment curves to ensure workspaces maintain peak oxygenation.

flowchart TD
    A[Room Volume, Occupants & Ventilation ACH] --> B[Mass Balance CO2 Differential Equation Solver]
    B --> C[Predicted Indoor CO2 ppm & Time-to-1000ppm]
    C --> D[Satish Cognitive Impairment Function]
    D --> E[Strategic Decision-Making Degradation %]
    E --> F[Timed Fresh Air Purge Interval Scheduler]

Features

  • Differential Mass Balance Engine: Models dynamic CO2 accumulation curves based on human metabolic exhalation.

  • Cognitive Impairment Mapping: Translates raw ppm numbers into executive function and decision-making penalties.

  • Smart Purge Prescriptions: Calculates exact window-opening minutes needed to flush spaces back to outdoor baselines.

Related MCP server: Eco-Loop Building Agents

Quickstart

from client import PersonalAgentIndoorCO2CognitiveVentilation

vent = PersonalAgentIndoorCO2CognitiveVentilation()
res = vent.forecast_indoor_co2_accumulation(30.0, 1, 0.4)
print("Predicted CO2:", res["predicted_terminal_co2_ppm"], "ppm")

FastMCP Integration

python -m genpark-personal-agent-indoor-co2-cognitive-ventilation-skill.mcp_server

License

MIT

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Maintenance

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