executing-plans
executing-plans
Use when executing an implementation plan in the current session as the implementer yourself — your human partner chose inline execution, or no subagent tool is available
scene-simulation-assetsCompose mjlab scenes, entities, variants, simulation configs, asset-zoo robots, and exportable MuJoCo packages.
Independent GitHub snapshot · Automated classification · Original documentation governs
Use this sub-skill when the task is about mjlab scene assembly or the MuJoCo
asset layer: EntityCfg, VariantEntityCfg, SceneCfg, TerrainEntityCfg,
entity-level actuator attachment, SimulationCfg / MujocoCfg, asset-zoo
robots, scene export, or direct mujoco.MjSpec / MuJoCo Warp model-data access.
Scene.write(...), and
heterogeneous mesh variants, read
assets-export-variants.md.When manually constructing the runtime, follow the same order used by mjlab's manager-based environment:
from mjlab.scene import Scene
from mjlab.sim import Simulation, SimulationCfg
scene = Scene(scene_cfg, device="cpu")
sim = Simulation(
scene.num_envs,
SimulationCfg(),
spec=scene.spec,
variant_info=scene.collect_variant_info(),
device="cpu",
)
scene.initialize(sim.mj_model, sim.model, sim.data)
if scene.sensor_context is not None:
sim.set_sensor_context(scene.sensor_context)
Use scene.write(output_dir, zip=False) or scene.write(output_dir, zip=True)
for exportable packages. For direct runtime reads, prefer entity.data and use
sim.model / sim.data only when a task needs global MuJoCo IDs or fields.
SceneCfg and
Simulation.export-scene CLI usage, task-registry command workflows,
training, playback, and debug CLI flags to
../training-evaluation-cli/. This sub-skill is
the API-internals reference for what that export command builds.