905 件 ・ 関連度順
概要と使いどころ
Build production computer vision pipelines for object detection, tracking, and video analysis. Handles drone footage, wildlife monitoring, and real-time detection. Supports YOLO, Detectron2, TensorFlow, PyTorch. Use for archaeological surveys, conservation, security. Activate on "object detection", "video analysis", "YOLO", "tracking", "drone footage". NOT for simple image filters, photo editing, or face recognition APIs.
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。
Expert in 3D computer vision labeling tools, workflows, and AI-assisted annotation for LiDAR, point clouds, and sensor fusion. Covers SAM4D/Point-SAM, human-in-the-loop architectures, and vertical-specific training strategies. Activate on '3D labeling', 'point cloud annotation', 'LiDAR labeling', 'SAM 3D', 'SAM4D', 'sensor fusion annotation', '3D bounding box', 'semantic segmentation point cloud'. NOT for 2D image labeling (use clip-aware-embeddings), general ML training (use ml-engineer), video annotation without 3D (use computer-vision-pipeline), or VLM prompt engineering (use prompt-engineer).
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。
Claude Codeの記憶、定期実行、タスク待ち行列を組み合わせ、継続的な作業を設計するスキル。外部の実行環境や画面操作の連携設定も案内します。
A real-time data/particle/simulation visualizer whose heavy compute runs in a Web Worker (off the main thread), optionally sharing memory with the UI via SharedArrayBuffer, and renders to a canvas at 60fps. Produced as a single self-contained `index.html`. Use when the brief asks for a "web worker", "simulation", "particle system", "physics", "off-main-thread", "fractal", "real-time compute", or "audio/data visualizer". OpenDesign serves this in powered-preview mode so Workers and SharedArrayBuffer actually 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。
Operate Orca-managed worktrees, folder contexts, terminals, repos, automations, artifacts, skill sharing, worktree comments, and Orca's embedded browser through the `orca` CLI. Use when the user says "$orca-cli", "Orca worktree", "child worktree", "spawn codex/claude in a worktree", "read/wait/send Orca terminal", "handoff" / "handover" / "give this to another agent", "Orca browser", "orca artifacts", or "share skills". Prefer it over raw git worktree, ad hoc PTYs, or Computer Use when Orca state is involved. Use Computer Use only when a visible window needs GUI control that a CLI, filesystem, or API cannot do.
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。
Drives the GUI of a visible local app window through `orca computer`: accessibility tree, clicks, typing, menus, dialogs, and screenshots in native apps and external browser windows (Chrome, Edge, Safari) or webviews. Prefer a programmatic path (shell, filesystem, git, HTTP, existing CLIs) whenever it can complete the task. Use only when a visible window needs GUI control those cannot reach. Do not use for Orca's embedded browser (`orca-cli`).
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。
Discover available task environments and create or continue tasks when the user names an environment or computer, or refers to files or apps specifically on their computer. Excludes software engineering; use orbit:software-engineering for that.
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 to change the wallpaper on the user's dot's computer and/or the accent color used for the Dock and window titlebars
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。
构建 GUI 自动化 Agent、让 Agent 像人一样看屏幕并用鼠标键盘操作桌面或浏览器软件时使用。 覆盖感知-思考-行动循环、动作空间（GUI/bash/文件编辑）、视觉定位 Grounding 三条路线 （结构化元素索引、Set-of-Mark、纯坐标预测）、分辨率坐标缩放、关键帧观察接口、世界模型与移动端生态壁垒。 触发词：Computer Use、GUI 自动化、截图、Grounding、browser-use、Set-of-Mark、视觉定位、坐标缩放、OSWorld。
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。
Azure Batch SDK for Java. Run large-scale parallel and HPC batch jobs with pools, jobs, tasks, and compute nodes.
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。
Govern Power Automate flows and Power Apps at scale using the FlowStudio MCP cached store. Classify flows by business impact, detect orphaned resources, audit connector usage, enforce compliance standards, manage notification rules, and compute governance scores — all without Dataverse or the CoE Starter Kit. Load this skill when asked to: tag or classify flows, set business impact, assign ownership, detect orphans, audit connectors, check compliance, compute archive scores, manage notification rules, run a governance review, generate a compliance report, offboard a maker, or any task that involves writing governance metadata to flows. Requires a FlowStudio for Teams or MCP Pro+ subscription — see https://mcp.flowstudio.app
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。
Compute the optimal --mamba-full-memory-ratio (or --max-mamba-cache-size pin) for a hybrid attention + linear-attention (Mamba / GDN / KDA) model's two serving memory pools, from the workload and serving config. Use when a user asks what ratio to set, why concurrency is clamped, or how to size the state vs KV pools for a hybrid model.
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 computer_use for live Windows or macOS GUI work that structured tools cannot complete. Discover an approved app and window, act from fresh observations, and verify every requested result.
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。
Train Mixture of Experts (MoE) models using DeepSpeed or HuggingFace. Use when training large-scale models with limited compute (5× cost reduction vs dense models), implementing sparse architectures like Mixtral 8x7B or DeepSeek-V3, or scaling model capacity without proportional compute increase. Covers MoE architectures, routing mechanisms, load balancing, expert parallelism, and inference optimization.
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, use and clean up cua sandboxes (disposable Linux or macOS computers) locally or in the Cua cloud with the `cua` CLI or the cua SDK, and browse the web inside one. Use when a task needs an isolated machine to run code, test an app, drive a desktop GUI, browse or test a website, fill a web form, take a web page screenshot, or reproduce something without touching the user's own computer.
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。
Control local desktop applications through Computer Use for tasks that require reading or operating app UI. Prefer purpose-built connectors, APIs, or CLIs when available.
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。
Computer vision engineering skill for object detection, image segmentation, and visual AI systems. Covers CNN and Vision Transformer architectures, YOLO/Faster R-CNN/DETR detection, Mask R-CNN/SAM segmentation, and production deployment with ONNX/TensorRT. Includes PyTorch, torchvision, Ultralytics, Detectron2, and MMDetection frameworks. Use when building detection pipelines, training custom models, optimizing inference, or deploying vision 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。
Trigger on mention of GKE cluster autoscaler, node autoscaling, node pool auto-creation / node auto-provisioning. Provides guidance on enabling and optimizing cluster autoscaler, best practices, and troubleshooting issues such as nodes not scaling up or down, zonal stockouts, or capacity buffers. Do not use for ComputeClass-specific YAML generation or priority configuration (defer to gke-compute-classes 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。
Configures alerting policies in Terraform for Google Kubernetes Engine (GKE) clusters, workloads, and services using PromQL and Google Cloud Managed Service for Prometheus. Use when writing, analyzing, validating, or deploying Terraform alerting policies to monitor GKE service latency, traffic, error rates using Multi-Window Multi-Burn-Rate SLO alerts, memory saturation, and cluster health such as CrashLoopBackOff and Node NotReady conditions. Don't use for non-GKE compute runtimes such as standalone Compute Engine VMs or standalone Cloud Run services without GKE.
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。
Optimizes GKE costs, rightsizes workloads, and configures Spot VMs, CUDs, cost allocation, and resource quotas. Use when optimizing GKE cluster or workload costs, configuring GKE cost allocation or quotas, rightsizing CPU/memory requests, or selecting Spot VMs and machine types. Don't use for general compute class provisioning or GPU Selection (use gke-compute-classes instead).
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。
Backend-specific Windows guidance for `computer_use_remote`. Load after `status` or `start_session` reports backend_family/backend_id `windows` or Windows UI Automation features. Covers UIA structural targeting, Windows session caveats, and screenshot verification.
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。
Backend-specific macOS guidance for `computer_use_remote`. Load after `status` or `start_session` reports backend_family/backend_id `macos` or Accessibility-tree features. Covers AX structural targeting, macOS window actions, and screenshot verification.
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。
Backend-specific Linux guidance for `computer_use_remote`. Load after `status` or `start_session` reports backend_family `linux`, backend_id `wayland`, or AT-SPI features. Covers AT-SPI structural targeting, Wayland portal caveats, and screenshot verification.
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。
Real Chrome/Brave + macOS Computer Use from inside the browser — zero CDP fingerprint, real sessions; mandatory for visual deploy verification. Drive clickwork yourself. USE WHEN verify deploy, confirm UI, screenshot verification, computer use, macos automation, debug web, troubleshoot, visual check, motion/animation bug, jank, transition stutter, scrub a flow, console logs/errors, runtime/JS/react errors, mismatch warning, network traffic/log, HAR/pcapng export, hydration/blank-page debug, flash then blank/page broken, why is this not working/what's happening on the page, authenticated page, bot detection bypass, reproduce bug, drive native app, about to ask the operator to click/navigate/fill a form/log in/approve OAuth, OAuth consent flow, complete a web login, do this in your browser. NOT FOR residential-proxy crawling (BrightData) or social actor scraping (Apify).
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。