Coding standards, frameworks, refactoring, and code review (16.7K skills)
Inspect GitHub and GitLab repositories, issues, and proposed code changes, then prepare small fixes with tests and review summaries. Uses existing command-line authentication and requires approval before publishing changes or modifying repository records.
Index reference libraries locally with XERJ and retrieve relevant implementations before coding. Use the retrieved source to understand unfamiliar API behavior, port algorithms, or investigate errors that repeated attempts have not resolved.
Find the development workflow for updating the Playwright dependency or preparing a playwright-cli release. This skill points the agent to a separate guide for each task, providing an entry point for these two maintenance activities.
Prepare and launch Autoware with CARLA on UE 5.8 through its native ROS 2 interface. Check communication topics and sensors, generate required maps, and configure either the classical stack or camera-only VAD model.
Import a custom pedestrian into CARLA on UE 5.8, connect it to the shared animation skeleton, and verify that it moves. Export an existing walker as an FBX starting point or investigate a walker that stands still.
Import FBX 3D models into CARLA on UE 5.8 and register them as props that can be placed in a simulation. Check measured dimensions, adjust scale, verify placement on a running server, and undo imports when needed.
Guides custom map imports into CARLA on UE 5.8, from FBX geometry and OpenDRIVE road data to packaging or temporary road generation. Helps choose a supported route and verify map files and loading.
Build CARLA for Unreal Engine 5.8 using CMake, the tool that configures its build process. The skill guides engine setup, optional ROS 2 support, Python API rebuilding, editor builds, and checks that the expected files were actually produced.
Explore roads, lanes, and junctions in a CARLA simulation map through text reports and visual overlays. Locate places described in everyday language, inspect nearby lane details, and trace a path forward or backward along a lane.
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.
Create and edit CARLA driving routes by placing waypoints and scenario triggers in the simulator. Inspect routes visually, review summary tables, check XML structure and trigger positions, and convert older route files to the 2.x format.
Inspect data published by a ROS 2-enabled CARLA server using Docker, without installing ROS 2 locally. Check topic contents and rates, run a sensor-equipped vehicle demo, and view lidar data and supported map markers in RViz2.
Import a four-wheel FBX vehicle into a CARLA source build, configure its physics, wheels, and animation, and register it for the Python API. Verify that the resulting vehicle can spawn, drive, and steer.
Control CARLA’s on-screen camera to view a vehicle from behind, above, the driver’s seat, or the front. Select vehicles by name, role, or ID, frame them once, or follow them as they move.
Control street, building, and vehicle light sources in CARLA. Switch groups on or off, adjust their color and brightness, and enable automatic nighttime lighting to prepare illuminated city scenes or blackouts.
Install the CARLA Python client into a chosen Python environment using bundled packages or PyPI. The skill checks existing installations, selects a suitable package, and verifies imports and version compatibility with a reachable simulator.
Record a running CARLA simulation to a .log file for later replay or inspection. Capture object creation, movement, and traffic-light states, optionally include controls and velocities, and verify that the recording contains simulation frames.
Creates a Python agent skeleton for CARLA Leaderboard and checks its sensor configuration offline against the installed version’s rules. Covers version-specific initialization, driving logic, and ROS1 or ROS2 agent integration.
Install Scenic, a language for describing scenarios with randomized conditions, into the Python environment used by CARLA. Check command-line tools and version compatibility, optionally download example scenarios and map assets, and save the installation paths.
Inspect a running CARLA simulation and identify vehicles or pedestrians by type, role, color, or proximity. Retrieve their IDs, scene snapshots, static object bounds, ground positions, and expected ROS 2 topics with reasons for missing publications.
Import RoadRunner geometry and road data into a CARLA source build, then verify the map loads and supports driving. Handle tiled maps and generate and inspect pedestrian navigation areas for standard maps.
Add a message type to CARLA's native ROS 2 interface. Create its C++ data structure, serialization code for transmission, type identity hash, registration, and tests, with Docker-based hash calculation and build checks.
Draws points, lines, arrows, boxes, and text over a running CARLA world to visually check positions, paths, and objects. Sets colors and display lifetimes for temporary debugging overlays, without creating persistent actors or output files.
Add pedestrians to a CARLA map and let them walk autonomously. Adjust crowd size, walking speed, road-crossing frequency, and placement seeds, create stationary crowds, and remove pedestrians together with their movement controllers in the correct order.