'simulation' 검색 결과

Validate and run OpenSCENARIO traffic scenarios through ScenarioRunner. Check file structure, maps, vehicle definitions, and supported features before simulation. Run both .xosc and .osc files, with global parameter overrides available for OpenSCENARIO 1.x.

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• Validate and run an .xosc file from another tool
• Compare runs with different speed parameters
• Explore bundled OpenSCENARIO 2.0 examples

Run Scenic traffic scenarios on a CARLA server. First test whether a valid scene can be generated, then simulate with limits on runs and steps, record the outcome, and restore the world's asynchronous setting afterward.

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• Check a CARLA setup with carlaChallenge1
• Diagnose scenarios that reject every generated scene
• Check which challenge scenarios can run

Guide an AI agent through ClawWork, an economic survival simulation where task rewards must cover API token costs. Defines a daily choice between work and learning, artifact submission, knowledge storage, and priorities based on the remaining balance.

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• Choose work or learning based on the remaining balance
• Create and submit task artifacts for evaluation
• Save research findings to persistent memory

Guides development of Native SDK TypeScript services for file access, network requests, and other work outside the deterministic core. Covers typed calls, pinned npm dependencies, streaming, cancellation, replay, and process or thread execution.

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• Adding or updating Native SDK TypeScript services
• Moving file access or JSON processing out of the core
• Investigating NS1065–NS1067 errors

Organize red team exercises that simulate adversaries to assess an organization's detection and response. Covers engagement rules, infrastructure, operational controls, evidence handling, and reports that help security teams review gaps and prioritize improvements.

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• Defining red team objectives and engagement rules
• Designing an exercise that assumes an existing breach
• Organizing evidence and activity logs

Plan a browser game's architecture before coding. Choose an engine, separate game rules from rendering, and define input, assets, menus, saves, debugging, and performance checks within a shared design.

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• Choose an engine for a 2D or 3D browser game
• Separate game state from rendering responsibilities
• Plan input mappings and asset organization

Develop Starknet apps with starknet.js v9.x using examples for contract calls, account management, wallet connections, and transaction handling. It also covers transaction simulation, fee estimates, and paymasters, services that sponsor fees or accept alternative tokens.

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• Reading and updating Starknet smart contracts
• Connecting ArgentX or Braavos wallets
• Simulating transactions and estimating fees before submission

Routes security work to specialized guides for web and API testing, code review, threat intelligence, and incident response. Helps organize assessments from both attack and defense perspectives, including coordinated testing of detection capabilities.

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• Planning a web or API security assessment
• Reviewing code for potential vulnerabilities
• Organizing detection rules and incident response

Analyze relationships between people, objects, or papers as networks in Python. Use NetworkX to find shortest paths, identify important nodes and communities, visualize connections, exchange graph data, and generate sample networks for testing.

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• Visualizing social relationships or paper citation networks
• Finding shortest paths through a network
• Identifying important nodes and closely connected groups

Build Python code for economic models and policy evaluation, with guidance on choosing libraries, estimating effects, plotting results, and checking robustness. Covers macroeconomic simulation and causal inference, the analysis of cause and effect.

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• Implementing macroeconomic models in Python
• Estimating policy effects with difference-in-differences
• Checking assumptions and robustness in causal analysis

Estimate study sample sizes and statistical power, the chance of detecting a specified effect. Calculate detectable effects for a fixed sample, compare assumptions with power curves, and document adjustments for dropout, multiple comparisons, or clustered observations.

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• Estimate participant or sample counts before data collection
• Find the smallest effect detectable with a fixed sample
• Compare sample requirements across plausible effect sizes

Analyze relationships between people, objects, or places using Python networks. Build connection diagrams, find shortest paths, identify important nodes and closely connected groups, visualize network structure, and save the resulting data and measurements.

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• Visualize social or citation networks
• Find shortest routes through a network
• Identify important nodes and connected groups

Use for agent-assisted Auto-FL optimization of an existing NVFLARE job in simulation, POC, or production. Do not use for code conversion, diagnosis-only work, or deployment setup.

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Use when the user asks why a reported NVFLARE job failure signal occurred: the job failed, stalled, timed out, lost clients, ended with EXECUTION_EXCEPTION, or produced suspicious errors. Diagnose in simulation, POC, or production by collecting bounded evidence and mapping failure patterns to recovery actions.

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Build apps for viewing USD, a 3D scene format, using guidance matched to browser streaming or local execution. The skill selects implementation references for rendering, camera controls, selection, interface layout, and validation.

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• Build a browser viewer for USD scenes
• Create a local workstation viewer
• Add camera controls and object selection

Coordinate the end-to-end CAD/source-asset to SimReady workflow. Use for broad requests such as CAD to SimReady, source asset to simulation-ready USD, or prop packaging that require conversion, material/physics assignment, SimReady conformance, validation, and optional package creation; deploy or verify Content Agents services first when property assignment is enabled; route single-stage work through nested references.

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Run the root-level workflow runtime policy or rule-based rollouts and verify simulator success. Use for evaluation, checkpoints, or local controllers; do not use for replay or dataset annotation.

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Use for CUDA-Q setup, simulation targets, QPU access, and @cudaq.kernel authoring guidance.

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Build responsive, accessible calculators, comparisons, charts, and simulations with the sandbox contract.

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Payoff Simulation reference method. Use when studying this focused method, not to run an API request. External API-key access for its legacy endpoints is not published in this release.

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Azure VM/VMSS router. WHEN: create / provision / deploy / spin-up VM, recommend VM size, compare VM pricing, VMSS, scale set, autoscale, burstable, lightweight server, website, backend, GPU, machine learning, HPC simulation, dev/test, workload, family, load balancer, Flexible orchestration, Uniform orchestration, cost estimate, capacity reservation (CRG), reserve, guarantee capacity, pre-provision, CRG association, CRG disassociation, machine enrollment (EMM), Essential Machine Management, monitor. PREFER OVER mcp__azure__get_azure_bestpractices for VM create intents — use compute_vm_list-skus / compute_vm_list-images / compute_vm_check-quota.

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Runs quantum computing workflows on AWS through Amazon Braket — discovering devices (QPUs and simulators) and their availability, building gate-model circuits and analog Hamiltonian programs, submitting quantum tasks, program sets and hybrid jobs, looking up prices, and capping spend with spending limits. Applies to any request about quantum computing, quantum hardware, quantum simulation, AHS, OpenQASM, or running a quantum algorithm on AWS.

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Builds, runs, debugs, and operates applications on AWS Lambda MicroVMs — Firecracker-isolated, snapshot-resumable serverless compute environments running inside a container with up to 8 hr lifetimes. Applicable when workloads need strong isolation between tenants, isolated serverless compute, sandbox compute, or secure multi-tenant execution. Also suited for AI/agent code-execution sandboxes, interactive code playgrounds and notebooks (Jupyter, REPLs, dev environments running user-supplied code), reinforcement-learning environments, multi-tenant CI executors and build runners, sessionful game or simulation servers, or isolated security scanners. Also applicable when the workload needs long-lived sessions, a real port-listening server (gRPC, WebSocket, custom TCP protocols), state preserved across periods of inactivity (suspend/resume), container-level access (FUSE, eBPF, custom syscalls), or session-affine routing.

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Lead workflow for developing vgpu features and Blender assets with the repository's subharness specialists. Use for agent-based research, API design, implementation, shaders, materials, simulations, or ImageGen-guided asset iteration, and when the human mentions the agent flow or subharness team.

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