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Run batch processing, scientific simulations, and machine learning training on Google Kubernetes Engine. Configure parallel jobs, queues that manage resource access, and distributed computation, with guidance on scaling idle capacity down and recovering from interruptions.
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Create a small interactive HTML playground to explore how something works. It packages an explanation, simulation, or simple game into one screen, with clearly labeled keyboard controls and no network requests or external assets.
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Replay a recorded CARLA simulation in full or over a selected time range. Change playback speed, follow a particular vehicle, restore recorded weather, or regenerate sensor data to examine the run again.
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Read and change a running CARLA server’s timing, physics substeps, and rendering settings. Switch between client-controlled steps and automatic progression, keep Traffic Manager synchronization aligned, and read back the settings to verify each change.
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Populate a running CARLA simulation with autonomous traffic, evenly spaced vehicles in one lane, or a single vehicle for control and sensors. Filter vehicle models, keep automated driving active, and remove vehicles when the scene is finished.
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Inspect CARLA driving simulation logs without replaying them. Get recording details, find collisions by participant category, and identify vehicles that barely moved, with times and object IDs to locate moments worth reviewing.
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List, switch, or reload maps on a running CARLA server, or generate a world from road files. Preserve simulation settings when needed and verify the resulting map name, timing settings, and rendering mode.
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Inspect a vehicle or another object in a running CARLA simulation. Read position, speed, and acceleration from the same simulation frame, check vehicle controls and steering angle, or stream the object's state over time.
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Guides fine-tuning and evaluation of OpenVLA-OFT robot models that turn images and language into actions. Covers LIBERO simulation, ALOHA deployment, LoRA adaptation that updates a small set of parameters, and troubleshooting action scaling or configuration mismatches.
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Coordinate CAD and source assets through conversion to USD, a 3D scene format, material and physics assignment, and SimReady checks. Produce stage reports, with preview rendering and validated delivery packages when requested.
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Simulate quantum systems in Python with QuTiP, including open systems that interact with their surroundings. Calculate time evolution, energy loss, and entanglement, then visualize states and results for physics research or educational experiments.
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Turn regression results, summary statistics, and simulation output into tables and figures for academic papers. Apply consistent number formatting, labels, and accessible visual styles, then produce files ready to include in LaTeX documents.
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Investigate failed, stalled, or timed-out NVFLARE federated learning jobs, which coordinate training across sites. Collect limited logs and job artifacts, match known failure patterns, and report likely causes, confidence, and concrete recovery steps.
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Use this skill when calculating control system performance metrics such as rise time, overshoot percentage, steady-state error, or settling time for evaluating simulation results.
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Generate a browser-based p5.js curtain simulation that weaves user-supplied PNG, JPEG, or WebP artwork and optional text into individually moving strands. Use when users ask for an interactive curtain or thread simulation, artwork woven into cloth or ropes, image-bearing fabric motion, 可交互线帘/幕布, 图片织入布料, printed curtain effects, or a customizable browser-based fabric reveal.
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Build Simulink models that co-simulate with Eclipse SUMO traffic simulator. Use when creating SUMO-Simulink co-simulation, traffic simulation, TraCI connection, vehicle-in-the-loop testing, or ADAS scenario validation with SUMO. Covers Server/Client setup, Reader/Writer/Actor block configuration, random traffic generation, ego vehicle control, and SUMO file creation. Also use when the user mentions SumoInterfaceLibrary, .sumocfg files, or wants to connect Simulink to an external traffic simulator.
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Use when building or debugging Geometry Nodes, headless or live: procedural modeling, scattering objects on a surface, arrays along a curve, repeat or for-each loops, simulation zones, 5.2 physics (cloth, colliders, forces, effectors), closures, bundles, matrices, node-group assets with gizmos. Also when a GN script links nothing, shows red links, ignores modifier inputs, loses instances in a bounding box, or a simulation resets or jumps.
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This skill should be used when the user asks to "plan a red team engagement", "scope a penetration test", "design a security assessment methodology", "create rules of engagement", or "plan an adversary simulation".
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Structure a direct experience into a full learning cycle with concrete experience, reflection, and conceptual transfer. Use when planning field trips, simulations, or practical tasks.
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Framework for computational fluid dynamics simulations using Python. Use when running fluid dynamics simulations including Navier-Stokes equations (2D/3D), shallow water equations, stratified flows, or when analyzing turbulence, vortex dynamics, or geophysical flows. Provides pseudospectral methods with FFT, HPC support, and comprehensive output analysis.
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Check reported lighting quantities and results in supplied simulation PDFs without adding repeated calculation surfaces. Use for scoped luminaire inventories and illuminance results; not simulation or compliance.
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Analyze MD trajectories from GROMACS, AMBER, NAMD, CHARMM, LAMMPS. Reads topology/trajectory into Universe objects; supports RMSD, RMSF, radius of gyration, contact maps, H-bonds, PCA, and custom distance/angle calculations. Use for post-simulation structural analysis; use OpenMM/GROMACS for running simulations.
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Process-based discrete-event simulation. Model queues, shared resources, timed events: manufacturing, service ops, network traffic, logistics. Processes are Python generators yielding events. Resources: capacity-limited (Resource/Priority/Preemptive), bulk (Container), objects (Store, FilterStore). For continuous use SciPy ODEs; for agent-based use Mesa.
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Testing strategies, patterns, and tools for robotics software. Use this skill when writing unit tests, integration tests, simulation tests, or hardware-in-the-loop tests for robot systems. Trigger whenever the user mentions testing ROS nodes, pytest with ROS, launch_testing, simulation testing, CI/CD for robotics, test fixtures for sensors, mock hardware, deterministic replay, regression testing for robot behaviors, or validating perception/planning/control pipelines. Also covers property-based testing for kinematics, fuzz testing for message handlers, and golden-file testing for trajectories.
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