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概要と使いどころ
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。
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。
Use this skill when building, debugging, or optimizing CRM Analytics dataflows — defining node types (sfdcDigest, Append, Augment, computeExpression, computeRelative, Flatten, dim2mea, sfdcRegister), scheduling runs, handling run failures, and tuning performance. NOT for doing the same transformation in a Data Prep recipe — use admin/analytics-recipe-design. NOT for dataset field types, refresh schedules, or row limits — use admin/analytics-dataset-management.
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。
Give an AI agent its own real browser over MCP tool calls - launch, navigate, click, fill, screenshot, extract text, run JS - with a kernel-level real-device fingerprint and a persistent profile, so the session stays logged in between runs and pages see one coherent device instead of a headless build. No Playwright or SDK code to write. Use when an agent should operate a site itself, when a computer-use / browser-use setup needs a captured real fingerprint rather than a synthetic one, when agent sessions keep losing their login, or when comparing hosted agent-browser services. Also for 'MCP browser', 'browser MCP server', 'let my agent browse the web', 'agent browser control', 'browser-use MCP', 'computer use browser', 'Browserbase alternative', 'Steel browser alternative', 'headless browser detected'. Node (npx) or Python; Windows x64, macOS Intel + Apple Silicon, Linux x64 / arm64. SDK and REST reference is anti-detect-browser; account isolation is multi-account-isolation.
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 cheap, TEE-verified AI models from the 0G Compute Network as OpenClaw providers. Discover available models and compare pricing vs OpenRouter, verify provider integrity via hardware attestation (Intel TDX), manage your 0G wallet and sub-accounts, and configure models in OpenClaw with one workflow. Supports DeepSeek, GLM-5, Qwen, and other models available on the 0G marketplace.
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。
Select and implement the right Azure compute platform for a workload. Decision tree guides choice based on workload characteristics, scaling needs, and operational maturity. Covers serverless, PaaS, containers, Kubernetes, and IaaS patterns.
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。
Ground GEODE design and evaluation work in Stanford CS329A Parts 2 and 5. Use for test-time compute, planning, multi-step reasoning, deep research, LATS, independent subplans, best-of-N, repair depth, measurement replication, verifier authority, and GEODE/Eco²/SIL/Crucible comparisons.
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。
GOMS cognitive modeling methodology for analyzing human-computer interaction task performance
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 drone systems, computer vision, and autonomous navigation. Specializes in flight control, SLAM, object detection, sensor fusion, and path planning. Activate on "drone", "UAV", "SLAM", "visual odometry", "PID control", "MAVLink", "Pixhawk", "path planning", "A*", "RRT", "EKF", "sensor fusion", "optical flow", "ByteTrack". NOT for domain-specific inspection tasks like fire detection, roof damage assessment, or thermal analysis (use drone-inspection-specialist), GPU shader optimization (use metal-shader-expert), or general image classification without drone context (use clip-aware-embeddings).
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。
Expert-level computer networks covering OSI model, TCP/IP stack, routing protocols, transport layer, application protocols, network security, and software-defined networking.
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 and integrate the machine-level core of a new EMULATORS emulator, including Computer and Emulator derivatives, memory and port maps, chip ownership and wiring, clock coordination, model configuration, commands, formatters, executables, and Visual Studio projects. Use when scaffolding a machine namespace, adding a model or regional variant, assembling existing components into a computer, or making a new emulator build and start.
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. Triggers: "BatchClient java", "azure batch java", "batch pool java", "batch job java", "HPC java", "parallel computing java".
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 AI Vision Image Analysis SDK for captions, tags, objects, OCR, people detection, and smart cropping. Use for computer vision and image understanding tasks. Triggers: "image analysis", "computer vision", "OCR", "object detection", "ImageAnalysisClient", "image caption".
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。
Turns sustainability or ESG reports the user provides into a DRAFT question pack for reviewers: every quantitative disclosure as stated with unit, period, boundary and page, year-over-year changes computed only from the stated figures with the arithmetic shown, targets with stated and computed progress, restatements and inconsistencies, gaps and specific questions to the report owner. No assurance, accuracy or conformance verdict. Use when the user asks to "prepare review questions on our sustainability report", "what changed year on year in this ESG report", "check the figures in this report before it goes to the board", "find the gaps in this ESG disclosure" or "build a question pack for the report owner". Do not use for reviewing marketing text against environmental-claim rules, use green-claims-review instead. Drafts for human review; never approves, authorises or signs off.
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。
SOTA Computer Vision Expert (2026). Specialized in YOLO26, Segment Anything 3 (SAM 3), Vision Language Models, and real-time spatial analysis.
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。
Use when working with Aws Batch — aWS Batch compute environment management, job queue analysis, scheduling policy review, and array job monitoring. Covers compute resource utilization, job status tracking, failure investigation, Fargate vs EC2 comparison, and queue priority analysis.
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。
Drive the user's desktop in the background — clicking, typing, scrolling, dragging — without stealing the cursor, keyboard focus, or switching virtual desktops / Spaces. Cross-platform: macOS, Windows, Linux. Works with any tool-capable model. Load this skill whenever the `computer_use` tool is 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。
Search arXiv physics, math, and computer science preprints using natural language queries. Powered by Valyu semantic search.
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。
Orientation for custom (non-OpenAI) models running in the Codex app through the codex-router proxy. Explains that the app's native tools arrive as flattened codex_app__ and mcp__ names, that the router restores them so the app executes them, which companion skills to read before threads, browser, or computer-use work, and that a turn with no tool call ends the task. Use when the session uses a custom (non-OpenAI) model, for example deepseek-v4-flash or mimo-v2.5, when codex_app__ or mcp__ tool names appear in the tool list, when a tool result just arrived and more work remains, or when thread, browser, or computer-use work is requested.
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。
Utilizes native Rust/Node and Python-based computer-use-mcp servers to control macOS, Windows, and Linux desktops.
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。
This skill should be used when the user asks to "calculate inheritance shares", "法定相続分を計算", "相続分を出して", "相続割合", "誰がどれだけ相続する", "遺留分", or wants to compute statutory inheritance shares from a family tree.
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。