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Write AI disclosure policies for transparency with customers and stakeholders. TRIGGERS - Use when user needs help with ai-disclosure-policy related tasks.

日本語の概要は準備中です。原文の説明を表示しています。

Curate and prepare datasets for AI training. TRIGGERS - Use when user needs help with ai-dataset-curator related tasks.

日本語の概要は準備中です。原文の説明を表示しています。

Prepare for educational accreditation. TRIGGERS - Use when user needs help with accreditation-prep related tasks.

日本語の概要は準備中です。原文の説明を表示しています。

Prepare for educational accreditation. TRIGGERS - Use when user needs help with accreditation-preparation related tasks.

日本語の概要は準備中です。原文の説明を表示しています。

Prepare for acquisition. TRIGGERS - Use when user needs help with acquisition-prep related tasks.

日本語の概要は準備中です。原文の説明を表示しています。

AI-powered subject-aware crop that centers on the product regardless of its position in the frame.

日本語の概要は準備中です。原文の説明を表示しています。

Smart crop a product photo to Etsy's recommended 2000×2000px square format and export as JPEG.

日本語の概要は準備中です。原文の説明を表示しています。

Integrate Iteration Layer APIs for document, website, image, and sheet processing. Composable APIs — Document Extraction, Website Extraction, Document to Markdown, Image Transformation, Image Generation, Document Generation, and Sheet Generation — share one credit pool and chain together into workflows.

日本語の概要は準備中です。原文の説明を表示しています。

Resize a product photo to Shopify's recommended 2048×2048px square format, sharpen, and convert to WebP for fast storefront load times.

日本語の概要は準備中です。原文の説明を表示しています。

Generate a visual KPI report card with a headline metric, secondary stats, and branding — shareable as an image.

日本語の概要は準備中です。原文の説明を表示しています。

Extract shipment details, item quantities, and delivery confirmation data from warehouse delivery notes and goods received notes.

日本語の概要は準備中です。原文の説明を表示しています。

Extract HS codes, declared values, and duty amounts from customs declaration documents, then generate an XLSX import duty log.

日本語の概要は準備中です。原文の説明を表示しています。

Imports a pedestrian into CARLA on UE 5.8 as a spawnable, animating walker — imports the skinned FBX bound to CARLA's shared pedestrian skeleton, duplicates a donor walker blueprint and repoints it at the new mesh, and registers it in WalkerParameters.json as walker.pedestrian.<id>. Can also export a shipped walker to FBX, which is how you obtain a rig-conforming mesh to start from. Use when the user asks to "import a walker/pedestrian", "add a custom character", "clone a walker", or has a walker that spawns but stands still.

日本語の概要は準備中です。原文の説明を表示しています。

Explores a CARLA map's road network through the Map/Waypoint API — a natural-language rundown of the whole map ("give me the topology"), drawing the topology to confirm the modelled map matches the visible one, enumerating junctions to resolve phrases like "the 4-way junction in the middle", nearest-waypoint lane facts, and next/previous lane navigation. Use when the user asks about the map's layout, lanes, junctions, topology, or to locate/navigate specific map elements.

日本語の概要は準備中です。原文の説明を表示しています。

Imports FBX meshes into a CARLA source build as spawnable static.prop.* blueprints — pass a directory whose subdirectories name the semantic tags, or a single FBX with its tag, and the props are imported into content, registered, added to the prop factory and verified by spawning. Use when the user asks to "add a prop to CARLA", "import an FBX as a prop", "add street furniture/benches/signs", or needs a custom mesh spawnable from the Python API.

日本語の概要は準備中です。原文の説明を表示しています。

Controls the traffic-light actors on a running CARLA server — set a light green/red/yellow/off, freeze all lights (optionally all-green), set phase timing (green/yellow/red durations), and reset to the normal cycle. Target all lights, one by id, all in a junction, or the nearest. Use when the user asks to "make the light red", "freeze all lights green", "change the light timing", "turn the traffic lights off", or "reset the lights". This is the light actors, not the Traffic Manager (that's control-traffic).

日本語の概要は準備中です。原文の説明を表示しています。

Reads and summarises ScenarioRunner output — the criteria pass/fail tables from --output/--file, the machine-readable --json and --junit result files, and the criteria JSON written alongside a --record recording — and runs the metrics module (metrics_manager.py) to compute custom measurements over a recorded run offline, without the simulator. Use when the user asks "did the scenario pass", "why did it fail", "summarise these results", "compare these runs", or wants distance/speed/lane metrics from a recording.

日本語の概要は準備中です。原文の説明を表示しています。

Reads a specific actor's live telemetry from a running CARLA server — location and rotation, velocity and speed, acceleration, angular velocity, and for vehicles the control input (throttle/steer/brake), front-wheel steer angle and mass. All kinematics come from one world snapshot so they are frame-consistent. Use when the user asks "how fast is it going", "where/what heading is the car", "show the ego's telemetry", or "stream a vehicle's state". Resolve ambiguous targets with the world-data skill.

日本語の概要は準備中です。原文の説明を表示しています。

Inspects and visualises a ROS-2-enabled CARLA server from Docker containers, with no local ROS 2 install — list/echo/rate-check topics (the "are the topics actually on the wire" check), run the bundled map-and-lidar demo stack (hero vehicle with camera/lidar/GNSS/IMU on autopilot), and open RViz2 with the lane-network preset. Use when the user asks to "check the ROS topics", "echo /carla/map", "is CARLA publishing to ROS", "visualise the lidar in RViz", "open rviz", or "run the ROS 2 demo".

日本語の概要は準備中です。原文の説明を表示しています。

Draws debug overlays in a running CARLA world via world.debug — points, lines, arrows, bounding boxes, and text strings, each with a colour and lifetime. Use when the user asks to "draw/mark/highlight a point or box in the world", "visualise a path/waypoints", "label a location", or "show something on the map for debugging". Shapes are an overlay, not actors.

日本語の概要は準備中です。原文の説明を表示しています。

Visualises and validates a map's pedestrian navigation mesh by sampling walkable locations (get_random_location_from_navigation) and drawing them as debug points. Use when the user asks to "show/visualise the pedestrian navmesh", "check walkable areas", "validate pedestrian navigation after a map import", or "why won't my walkers move". Confirms the navmesh loaded and how far it covers.

日本語の概要は準備中です。原文の説明を表示しています。

Use when you need to run security checks for MCP and agent workflows before merging changes in shared repositories.

日本語の概要は準備中です。原文の説明を表示しています。

Repository-specific AGENTS.md authoring and maintenance workflow. Use when creating, refreshing, or expanding AGENTS.md coverage for a codebase, including deciding whether nested AGENTS.md files are needed for subsystems with distinct rules, workflows, or AI-sensitive constraints.

日本語の概要は準備中です。原文の説明を表示しています。

Guide for authoring specialized AI agents. Use when creating, updating, or improving agents, choosing models, defining focus areas, configuring tools, or learning agent best practices.

日本語の概要は準備中です。原文の説明を表示しています。