Field-tested Agent Skill: run CarSim 2024.0 headless - batch scenarios, parameter & per-wheel torque control, SI-unit results, Simulink co-sim (vs_sf S-Function). No MCP server, no GUI after a one-tim
View on GitHub ↗This skill follows the Agent Skills standard: a folder with a SKILL.md (YAML frontmatter + instructions), bundled scripts, and on-demand references.
A skill for operating CarSim from an AI agent: environment discovery, headless simulation, safe output/parameter discovery, structured overrides, isolated batch runs, dependency graphs, control-mode selection, SI output parsing, diagnostics, and version-aware execution. CarSim 2024.0 is the field-tested solver. A named GUI-expanded Run Control provides the execution context; the CLI writes scenario artifacts outside the read-only database. Verification results distinguish implementation, unit tests, licensed solver runs, and agent-behavior evals.
Task How ------ First task on a machine scripts/setuppaths.py — discovers and validates install paths; lists base candidates for explicit selection Run a scenario headless scripts/carsimbatch.py generates override.par + simfile.sim, calls the solver CLI, and verifies success Run a scenario from YAML (manifest + validation) scripts/scenariorunner.py — typed scenario YAML, base resolved by name + SHA256, compile-time checks, runmanifest.json Change speed / steering / friction / duration keyword overrides appended after a GUI-expanded base (CarSim parses last-write-wins) Set sprung mass, CG and yaw inertia VehicleOverrides(sprungmasskg=..., cgym=..., izzkgm2=...); SI inputs convert internally Discover/change another CarSim keyword parameterdiscovery.py + read-only database evidence, then a scalar/table/reference/raw override — never guess Switch execution context expand/bind the intende
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