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概要と使いどころ
Publish a long-lived internal web app, site, or dashboard from the agent computer. Scope-bound (only the owner's scope, plus whoever you share it with, can reach it), immutable versions with rollback, a stable friendly link. No public URL.
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。
Find a time and put a meeting on the calendar through Superhuman — read what is already booked, check the participants' availability, propose slots, and create the event only once the user picks one. Use when the user asks what is on their calendar, when they are free, to find a time with someone, or to schedule, move, or set up a call.
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。
Run Maestro YAML flows against a Limrun cloud iOS simulator with `lim ios maestro`, from any environment (Linux, Windows, macOS, VM, container). Use when the user wants to run, write, or debug Maestro flows or `maestro test` on iOS, migrate an existing Maestro suite to remote simulators, or asks for UI testing with Maestro. iOS simulators only today. For Detox suites use limrun-detox-testing; for driving the simulator without a test framework use limrun-ios-simulator.
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。
Work the action items Circleback captured — find what is assigned to this user, do the ones the workspace can do, and update status only for work that is actually finished. Use when the user asks what they owe, "do my action items", "what's still open from", or wants to close, reassign, or add an action item.
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。
Work through a Superhuman inbox — sort what is there into needs a reply, waiting on someone else, worth knowing, and noise, then archive, star, label, remind, or unsubscribe only as the user directs. Use when the user asks what needs their attention, what they are waiting on, to triage or clean up their inbox, or to deal with newsletters and notifications.
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。
Finds undocumented and rotting documentation in a CARLA checkout — unreleased features, Python bindings missing from the API reference, documented API that no longer exists, sensors and catalogue actors missing from their reference pages, broken links, and pages unreachable from the nav. Static only, no build or server. Use when the user asks to "audit the docs", "find undocumented features", "what needs documenting", or is preparing a release or a new default branch.
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。
Tells you what CARLA on UE 5.5 (branch ue5-dev) cannot do that UE 5.8 (ue58-dev) can, so you do not attempt an unsupported workflow there — no Autoware integration, no ROS 2 middleware choice (FastDDS only), no DLSS or ray-traced-lens camera, no OFPA large-map mount so Town12/13/15 load black, and a few missing World/Actor methods. Also states what is identical, so the ue58 skills can be used directly for everything else. Use when working against a 5.5 checkout, when a ue58 skill's step has no counterpart, or when deciding whether a feature request is possible on 5.5 at all.
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。
Gets a custom map into CARLA on UE 5.8 — the four distinct routes and which one to pick — via Util/Tools/Import.py for FBX+OpenDRIVE assets into a source build, CARLA_MAPS_TO_COOK to ship a map inside a package, or client.generate_opendrive_world() for an OpenDRIVE-only map at runtime with no build at all. Covers where imported assets land, why an imported map cannot be loaded from a package by name, and the four capabilities the UE4 skill has that ue58 lacks — standalone asset packages, their installer, semantic-material tagging, and a pedestrian navmesh that is generated but silently skipped. Use when the user asks to "import a map", "add a custom map", "load my .xodr", "package my own town", or "create a standalone map package".
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 custom RoadRunner map (FBX geometry + XODR OpenDRIVE) into a CARLA source build as a drivable map — reads the map from the directory the user names, runs CARLA's Import.py to cook the level, Traffic Manager graph and (standard maps only) pedestrian navmesh, and verifies it loads on a server. Handles both standard maps and large tiled maps; large maps get no navmesh, matching upstream CARLA. Use when the user asks to "import a map into CARLA", "add a custom/RoadRunner map", "ingest an FBX+XODR map", "make import", bring in a "large/tiled map", or fix missing walkers / a missing pedestrian navmesh.
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。
Lists and toggles a map's static environment objects via get_environment_objects / enable_environment_objects — hide or show buildings, vegetation, poles, fences, traffic signs and other baked assets by CityObjectLabel (and optional name filter). Use when the user asks to "hide/remove the buildings", "turn off the trees/vegetation", "show only the roads", "disable the fences", or "list the environment objects". Affects rendering and collision; resets on map reload.
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。
Builds CARLA (branch ue4-dev, Unreal Engine 4.26) from source on Linux end-to-end — UE4 fork, the Carla server (editor C++ modules), the LibCarla Python client wheel, and content — then verifies them against a from-source server. Optionally builds the native ROS 2 interface in (ROS2=1 → --ros2, Fast-DDS/CycloneDDS/Zenoh), which is compile-time only and cannot be enabled later. Use when the user asks to "build CARLA from source", "compile CARLA ue4-dev", "set up CARLA on Ubuntu (incl. 24.04)", "build CARLA with ROS2 support", or "produce a CARLA server + client wheel". Cooking a distributable Dist/ package is a separate skill (package-carla-ue4); running the build is run-carla-server.
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。
Generates optimized prompts for AI tools. Activates only when the user explicitly asks to write, fix, improve, or adapt a prompt for a specific AI tool (LLM, Cursor, Midjourney, image AI, video AI, coding agents, etc.). Does not activate for general conversation, coding tasks, document writing, or other non-prompt-engineering work.
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 validating or documenting a multi-stage KrillinAI CLI output plan; the current pipeline command supports dry-run planning only, not end-to-end execution.
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 designing new high-level system architecture, reviewing existing designs, or making architectural decisions. Invoke to create architecture diagrams, write Architecture Decision Records (ADRs), evaluate technology trade-offs, design component interactions, and plan for scalability. Use for system design, architecture review, microservices structuring, ADR authoring, scalability planning, and infrastructure pattern selection — distinct from code-level design patterns or database-only design 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。
Ask users questions via the UI. Use when you need clarification, user preferences, or confirmation before proceeding. The user CANNOT see CLI output - this tool is the ONLY way to communicate with them.
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。
Takes a structured list of changes from changelog-scan and produces a clean, categorized, user-facing release notes draft. Follows project voice and conventions. Never publishes — only proposes a draft file or section.
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。
Scan open issues and discussions. Dedupe, prioritize, and propose labels. Updates issue-triage-state.md. L1 propose-only — never auto-label or close.
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。
Install Loop Engineering into a project via the unified CLI front door (@cobusgreyling/loop). Prefer this over invoking loop-init / loop-audit separately. Week-one is report-only; never enable auto-merge or unattended fixes unless the human explicitly asks and doctor is healthy.
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 changelog-scan output into polished, categorized release notes draft. Propose only.
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。
Produce the smallest possible code change that fixes a specific, well-scoped issue (CI failure, reviewer comment, typo). Use only when the fix target is explicit. Never refactor unrelated code.
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 demos and GPU compute with Gradio Spaces and Hugging Face Spaces ZeroGPU. Use when writing or reviewing code that uses `@spaces.GPU`, configuring `python_version` or `requirements.txt` for a ZeroGPU Space, or handling ZeroGPU-specific code constraints — pickle-based process isolation, `gr.State` semantics across the worker boundary, no `torch.compile` (use AoTI instead), CUDA wheel-only builds (no `nvcc` at build or runtime), large vs xlarge sizing, and dynamic duration callables. Make sure to use this skill whenever the user mentions ZeroGPU, `@spaces.GPU`, or the `spaces` Python package, or hits ZeroGPU-specific code errors like `PicklingError` across the worker boundary, `illegal duration`, or `flash-attn` wheel-build failures — even when the user does not explicitly ask for ZeroGPU coding guidance. Trigger on `import spaces` or `@spaces.GPU` in code.
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。
Pick the right serving container for a SageMaker model deployment and find its current image URI. Use this skill whenever about to deploy a model to a SageMaker endpoint and an image URI needs to be chosen — including when the user says "deploy this LLM", "host this HuggingFace model", "serve this fine-tuned model", "deploy this embedding model", "host a reranker", "serve a sentence-transformers model", or when about to hardcode any container URI in deployment code. HuggingFace-curated Deep Learning Containers are ALWAYS preferred: HuggingFace vLLM (LLMs and generative rerankers), HuggingFace vLLM-Omni (multimodal), TEI (embeddings/cross-encoder rerankers), HF Inference Toolkit (other transformers). Generic images (AWS vLLM, DJL-LMI, SGLang) are used only when no HuggingFace image is compatible — never merely because they carry a newer version. Never hardcode a container URI from memory and never default to TGI. Prevents stale-image failures and wrong-region URIs.
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 state across calls — building env vars, navigating with cd, driving REPLs (python -i, mysql, psql, node), or responding to interactive prompts (sudo password, ssh host-key confirmation, mysql connection). Teaches the prompt-sentinel exec pattern (default mode), raw I/O for REPLs (raw_send=True then read_only=True), the one-in-flight-per-session rule, and the close-or-leak-against-the-cap discipline. Bash on macOS — never zsh; explicit shell=/bin/zsh is rejected. Read before calling terminal_pty_open.
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。
Read when a terminal-tools call returned something surprising — empty stdout despite no error, exit_code is null, output_handle came back expired, "too many jobs" / "session busy" / "too many PTYs", warning was set unexpectedly, semantic_status disagrees with exit_code. Diagnostic recipes only — load on demand. Don't preload; the foundational skill covers the happy path.
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。