Audit and improve MOSFET, IGBT, and diode models in a C++ power electronics simulator.
npx skills add https://github.com/lgili/skillex --skill semiconductor-models
Please help me install this Agent Skill. Skill: semiconductor-models Source: https://github.com/lgili/skillex/tree/main/skills/semiconductor-models Command: npx skills add https://github.com/lgili/skillex --skill semiconductor-models
Audits, improves, and validates semiconductor device models (MOSFET, IGBT, diode) used in a C++ power-electronics simulator, ensuring their parameters and behavior match datasheet references and measurement data so simulations yield reliable switching and conduction predictions.
Audit the MOSFET, IGBT, and diode models in the codebase and propose validated improvements against datasheet references.
High-intent search queries and answers about installing and using this skill.
Diode reverse recovery modeling captures charge removal behavior during switching transitions to predict E_on and E_off losses accurately. It requires validating reverse recovery parameters against measurement data across hard-switched and soft-switched topologies to ensure simulator predictions align with real device behavior.
Power semiconductor model auditing requires a C++ simulator codebase containing MOSFET, IGBT, and diode models, plus accessible datasheets or measurement data for parameter extraction. No external dependencies are needed, but you must map every parameter to a datasheet page or measurement file to maintain traceability.
Coss nonlinearity affects MOSFET switching simulations by causing voltage-dependent capacitance changes that alter switching transition speeds and E_off energy loss predictions. Accurate models must capture this nonlinearity by fitting capacitance curves from datasheets and validating the behavior across different switching scenarios.
IGBT conduction loss simulations diverge from datasheet curves when model parameters lack proper temperature dependency updates or fail to trace back to accurate datasheet extraction points. Auditing the model for missing thermal coefficients and re-validating against measurement data realigns the conduction behavior predictions.
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