在Orca内通过adb操作Android真机或模拟器,查看屏幕并执行点击、滑动和文字输入,安装、启动应用,检查界面结构与设备日志。
- 在Android真机或模拟器上检查运行情况
- 重现点击、滑动和文字输入
- 安装应用并检查运行时权限
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在Orca内通过adb操作Android真机或模拟器,查看屏幕并执行点击、滑动和文字输入,安装、启动应用,检查界面结构与设备日志。
Automate EEZ Studio projects from the command line. Create and edit project files, add LVGL interface widgets, manage SCPI instrument command definitions, and use the actual EEZ Studio backend to build a simulator.
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Create Appium tests that automate Android and iOS app interactions in Java, Python, or JavaScript. Configure local devices, emulators, simulators, or TestMu AI cloud devices, with guidance on element selection, waits, and gestures.
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Build end-to-end tests that follow user journeys in browsers and mobile simulators or emulators. Guides e2e configuration, AI-driven actions, precise outcome checks, failure investigation, and exploratory bug hunts backed by reproducible tests.
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Run the mobile app against local services in an iOS simulator, automate interactions with Maestro, and capture recordings or screenshots. Prepare demo data, inspect footage for private information, and restore the environment after recording.
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Check mobile app changes in an iOS simulator using real local services. It covers user interactions, service failures and recovery, language and text size checks, with screenshots and recordings as evidence.
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Checks a local CARLA checkout for missing API documentation, outdated references, broken links, and pages absent from navigation. Produces a grouped Markdown report without building the project or running the simulator.
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Start, inspect, and stop a CARLA UE 5.8 server. Choose an editor-based server, packaged build, or full editor; configure headless operation and ports; and enable native ROS 2 integration when the build supports it.
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Start a CARLA simulator server from a downloaded release, packaged build, or source checkout. It selects the launch mode, supports headless or windowed operation, checks client connectivity, and guides clean shutdown.
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Summarize ScenarioRunner results to see which scenarios passed and which checks failed. Compare runs before and after changes, or analyze recorded driving to produce measurements and plots of vehicle distance and deviation from the lane center.
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Turn a written traffic situation into a Scenic file, which describes possible scenes using placement rules and constraints. Select suitable maps and actors, then check that the file compiles and can generate at least one scene.
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Build distributable CARLA releases or asset packages containing selected maps and props. It prepares content for runtime use, checks output files, supports destination changes, and preserves native ROS 2 support when explicitly enabled.
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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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Run individual or grouped traffic scenarios in CARLA, a driving simulator, and collect pass/fail reports or recordings. Check which scenarios match the current branch and map, and investigate runs that fail or show no movement.
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Set up ScenarioRunner, CARLA’s scenario engine, with a branch matched to your simulator version. Install dependencies, configure Python search paths, and verify imports to address setup failures before running scenarios.
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Set up a CARLA driving evaluation environment for Leaderboard 1.0, 2.0, or 2.1. Match the simulator and scenario_runner versions, configure Python paths, and verify that the required components load correctly.
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Scaffold a native iOS app (SwiftUI, no dependencies) from nothing, run it in the Simulator, and archive + upload it to TestFlight entirely from the command line. Bundles working scripts for icon generation, simulator run, and signed upload via the App Store Connect API. Use this whenever the user wants to build, run, ship, or debug an iOS/iPhone/iPad app, mentions Xcode, SwiftUI, .xcodeproj, xcodebuild, simctl, provisioning, code signing, App Store Connect, TestFlight, archiving, or "get this on my phone" — and also when an existing iOS build is failing on icons, signing, teams, destinations, or duplicate build numbers.
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Create and simulate UAV scenarios with terrain, buildings, platforms, and sensors using uavScenario. Use when building a UAV simulation, UAV simulator, or UAV scenario in MATLAB. Covers addMesh for terrain/building import, uavPlatform with updateMesh, uavSensor adaptor pattern for GPS/IMU, and the setup/advance simulation loop. Triggers on: uavScenario, UAV simulation, UAV simulator, multirotor simulation, quadrotor scenario, terrain import, building import, GPS sensor simulation.
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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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Build, install, launch, and screenshot an iOS app in the Simulator to verify a change visually. Use when the user wants to run the app, see a change live, screenshot the running app, or confirm a UI fix actually works (not just that it compiles).
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Use when automating iOS Simulator interactions, capturing screenshots/video, or inspecting accessibility via AXe CLI.
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21 production-ready scripts for iOS app testing, building, and automation. Provides semantic UI navigation, build automation, accessibility testing, and simulator lifecycle management. Optimized for AI agents with minimal token output.
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Use XcodeBuildMCP to build, run, launch, and debug the current iOS project on a booted simulator. Trigger when asked to run an iOS app, interact with the simulator UI, inspect on-screen state, capture logs/console output, or diagnose runtime behavior using XcodeBuildMCP tools.
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Use when users want to practice or simulate Java, C++, Go, Golang, mixed-stack, or general backend technical interviews, including resume-based and job-description-based interview preparation.
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