「step」の検索結果

Answers "can CARLA do X here, and is there a vetted procedure for it" before you promise anything, and detects client/server/tree version skew — a support matrix over the whole collection (covered by a skill / works but undocumented / present but untested / broken or removed), an index of the features that are verified BROKEN on 0.10.0 (gbuffers crash the server, map layers do nothing, Landmark.waypoint is None, standalone asset packages are gone), and a probe that reports what this machine's build flags and running server actually offer. Use when the user asks whether CARLA supports something, when a feature has no skill, when a UE4 tutorial's steps do not exist, or before improvising a procedure.

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。

Writes an autonomous agent for the CARLA Leaderboard — an AutonomousAgent subclass with setup/sensors/run_step/destroy, a sensor suite inside the track's budget, and the right constructor signature for the leaderboard version (1.0 and 2.x differ). Generates a working skeleton, validates the sensor configuration against the real per-track limits offline, and covers the ROS1/ROS2 agent base classes. Use when the user asks to "write a leaderboard agent", "make my agent work with the leaderboard", "add sensors to my agent", "port my agent to 2.1", or hits SensorConfigurationInvalid.

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。

Adds a new ROS 2 message type to CARLA's native ROS 2 interface — the hand-written POD struct under LibCarla/source/carla/ros2/types/msg, its Fast-CDR serialize/deserialize pair, the RIHS01 type hash computed in Docker, the CdrTopicInfo registration and the type-hash test. Use when the user asks to "add a ROS message type", "support sensor_msgs/X in CARLA", "add a carla_msgs type", "why is my type hash warning appearing", or needs a message CARLA does not publish yet. There is no IDL codegen — every step is manual.

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。

Autonomous AI agent platform for building and deploying continuous agents. Use when creating visual workflow agents, deploying persistent autonomous agents, or building complex multi-step AI automation systems.

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。

Parse CI failures, identify failing job/step, classify as flake, regression, env, or config. Use in CI sweeper loops before any fix attempt.

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。

Build and publish a Gradio demo on Hugging Face Spaces for a user-provided LoRA. Use when someone asks to create, generate, ship, or publish a Space, demo, Gradio app, or playground for a LoRA — including LoRAs for Qwen-Image, Qwen-Image-Edit, LTX-Video, Wan, FLUX, SDXL, or other diffusion base models. Also triggers when someone describes a LoRA they trained or hosts on the Hub and wants to share it. Covers picking the right base pipeline and `diffusers` inference recipe, designing a UI tailored to the LoRA's task and inputs (Union/multi-task control, edit, video, image, etc.), respecting model-card recommendations (trigger words, steps, guidance, LoRA scale, example inputs), and shipping to ZeroGPU hardware as a private Space by default.

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。

Create a SageMaker endpoint (real-time, real-time scale-to-zero, or async) with autoscaling, CloudWatch alarms, and tagging enabled by default. Use this skill whenever about to create a SageMaker endpoint, write deployment code that calls `create_endpoint`, or finalize a deployment after the image URI and IAM role are known. Provides deploy.py for real-time endpoints, deploy_ic.py for real-time endpoints that scale to zero instances via inference components, and deploy_async.py for async endpoints (also scale-to-zero). This is the last step in the SageMaker deployment workflow. Never generate a bare `create_endpoint` call without these defaults — endpoints without autoscaling or alarms are demos, not deployments.

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。

Set up a Telegram notification channel (Sentinel) for a colony by driving the browser — create a bot via @BotFather in Telegram Web, store its token, detect the chat to notify, and turn Sentinel on so the colony can ping the user on Telegram and accept replies. Use when the user asks to "set up Telegram notifications", "get pinged on Telegram", "connect Telegram for alerts", "set up Sentinel on Telegram", or clicks the in-app "Set this up with the agent" button on the Telegram step. Requires hive.browser-automation.

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。

Claim tasks, record step progress, and verify SOP gates in the colony SQLite queue. Applies when your spawn message includes a db_path field.

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。

Set up a Slack notification channel (Sentinel) for a colony by driving the browser — reuse or create the "Hive Sentinel" Slack app from a JSON manifest, install it, capture the bot + app tokens, create/select the channel via the Slack API, and turn Sentinel on so the colony can ping the user on Slack and accept replies. Use when the user asks to "set up Slack notifications", "get pinged on Slack", "connect Slack for alerts", "set up Sentinel on Slack", or clicks the in-app "Set this up with the agent" button on the Slack channel step. Requires hive.browser-automation.

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。

Turn a breaking change (an API rename, removed flag, changed default, or moved module) into concrete upgrade steps with before/after examples. Use when the user asks how to migrate, upgrade, or adapt to a breaking change.

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。

Quickly determine whether a processing activity needs a PIA, a mandatory GDPR DPIA, or can proceed — surfaces privacy policy conflicts and routes to the right next step. Use when the user asks "does this need a PIA", "triage this feature", "privacy check on X", "is this okay from a privacy perspective", or describes a new data processing activity, product feature, or vendor relationship.

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。

Classify a proposed AI use case against your registry — approved, conditional, or not approved — and produce required conditions and next steps. Flags cross-plugin handoffs to privacy or product counsel. Use when user says "triage this use case", "can we use AI for X", "is this approved", "what do we need to do to use AI for X".

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。

Review and approve (or reject) pending playbook update proposals from the playbook-monitor agent and apply approved changes to the practice profile. Use when the playbook-monitor agent has surfaced proposals, when the user says "review playbook proposals", "what playbook updates are pending", or wants to step through deviation-driven playbook changes.

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。

Munder Difflin hive sync — runs the start-of-task hive protocol steps: reads memory.md, checks inbox/ for new messages, and reminds you to record durable facts in memory.md and write coordination files before ending. Use when asked to "sync with the hive", "check my inbox", "hive status", or "hive sync". Proactively suggest at the start of a new task if you haven't checked your hive inbox in this conversation. (munder-difflin)

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。

Trigger: a mapping need, parallel units, context backstop, high-risk verify, fresh review, or multi-step debug. Delegate via delegate_task for a named reason.

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。

Workaround for write_file failures caused by special characters (apostrophes, backticks, template literals) using a Python heredoc script, plus a reliable TypeScript type-checking step that filters output to only the newly-written files to avoid being misled by pre-existing errors.

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。

Reliable fallback technique for writing large file contents when write_file and shell_agent both fail with 'unknown error' due to payload size limits — uses run_shell with a Python heredoc to bypass tool constraints, with an explicit mkdir -p step to ensure the target directory exists before writing.

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。

Ensures codebase deliverables are properly exported as ZIP archives through a dedicated finalization step

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。

Ensure codebase deliverables are properly packaged as ZIP exports in a dedicated final step

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。

Four-step recovery workflow for code execution failures when inline Python fails

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 run_shell with cd and heredoc syntax for complex Excel operations when read_file fails on XLSX or execute_code_sandbox fails on multi-step workbook creation

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。

Delegate web search tasks to shell_agent when direct search_web tool fails, using its retry mechanism and multi-step capabilities for resilient data gathering

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。

Build complex Excel files through staged, verifiable steps with intermediate CSV outputs for debugging

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。