「node」の検索結果

H100やH200のGPU在庫を調べ、固定料金の見積もり依頼や調達状況を確認します。端末の認証解除と、明示的な許可に基づくノード検証にも対応します。

• H100やH200の在庫確認
• 固定料金の見積もりを依頼したいとき
• 見積もりや調達の進捗確認

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。

Monitors and troubleshoots GKE TPU workloads, nodes, and node pools using GKE system metrics and PromQL. Use when monitoring TensorCore duty cycle, TPU memory, node readiness, multi-host TPU node pool availability, host maintenance or preemption interruptions, and calculating MTTR or MTBI metrics for GKE TPUs. Don't use for general non-TPU GKE workload monitoring or non-metric TPU 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。

Convert single-node scripts to multi-node Slurm sbatch jobs and debug common multi-node failures. Covers srun-native vs uv run torch.distributed approaches, container setup, NCCL timeouts, OOM sizing for MoE models, and interactive allocation.

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 React Flow node components with TypeScript types, handles, and Zustand integration. Use when building custom nodes for React Flow canvas, creating visual workflow editors, or implementing node-based UI components.

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。

Invoke and troubleshoot OpenClaw Windows-node commands through the local winnode CLI and MCP endpoint, including command argument schemas and A2UI JSONL calls.

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 a browser/Web app (React, Vue, Next, Nuxt, static sites, SPAs, dashboards, AI chat UI, 页面, 前端, 网页) needs AI models via @cloudbase/js-sdk. Default routing for Web/frontend AI — call directly from the browser, do NOT propose a Node.js proxy. Covers generateText and streamText; models via ai.createModel with groups cloudbase, hunyuan-exp, or custom-*, model id in the `model` field. MUST run two-step preflight before code — see body. NOT for Node.js backend (use ai-model-nodejs), Mini Program (use ai-model-wechat), or image generation (Node SDK 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。

Diagnose performance issues on Amazon SageMaker HyperPod clusters — uneven NCCL bandwidth across nodes and poor filesystem throughput. Read-only. Surfaces host-side signals (Xid, ECC, NVLink, EFA reachability, FSx saturation) and routes to the appropriate sibling skill (hyperpod-node-debugger, hyperpod-nccl, hyperpod-version-checker, hyperpod-issue-report) for any remediation. Triggers on uneven NCCL across nodes, straggler node, FSx slow, checkpoint slow, dataloader slow, filesystem bottleneck, FSx throughput, cross-AZ latency, topology mismatch.

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。

Configure and author rgthree-comfy nodes — Fast Groups Bypasser/Muter (group toggles), Power Lora Loader, Context/Context Big, Seed, Any Switch. Use when a workflow contains rgthree nodes, when asked to add stage/section toggles or an A/B switch, when stacking LoRAs, or when an rgthree node needs configuring. Covers the frontend-only nodes that are absent from /object_info and the properties-not-widgets configuration 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。

On-demand procedures for the ComfyUI sidebar panel agent that are too long to sit in the system prompt. Read it when a task lands in one of these areas. subgraph boundary rails and blueprint reuse, merging or composing workflows across tabs, pinning your edits to one workflow, opening staged or downloaded workflow files, untangling Get/Set-bus and rgthree toggle-heavy graphs, authoring rgthree Fast Groups nodes, the LoRA Manager autocomplete-node limitation, the CivitAI browser flow, downloading model weights, reading hardware and runtime stats, Prompt Director audits, custom-node crash recovery, run-to-node render debugging, and multi-stage pipeline chaining. Each section is a lookup, not background reading. Reach for the one you need instead of guessing, and follow the tool's own description for parameter detail.

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。

n8n workflow automation knowledge base. Provides n8n node information, node functionality details, workflow patterns, and configuration examples. Covers triggers, data transformation, data input/output, AI integration, covering 10 nodes. Keywords: n8n, workflow, automation, node, trigger, webhook, http request, database, ai agent.

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 user asks to create/generate/edit/modify a BPM workflow, design a Flowable BPMN process, or says "创建流程", "生成流程", "新建流程", "设计流程", "画流程", "审批流程", "工作流", "BPM", "BPMN", "create flow", "create process", "new workflow", "generate workflow". Also triggers when user describes an approval chain like "先经理审批再HR审批" or mentions process nodes like "开始→审批→网关→结束". Also triggers for OA application creation: "创建OA应用", "创建审批单", "创建报销单", "创建请假单", "做一个OA表单带流程", "一键创建表单和流程", "create OA app", "create approval form with workflow". Also triggers for ANY operation on existing processes: "编辑流程", "修改流程", "删除监听器", "添加监听器", "删除节点", "添加节点", "修改节点", "配置节点", "流程中的", "edit process", "modify process", "delete listener", "add listener", "remove listener", "add node", "delete node", "configure node". Key rule: whenever user mentions a specific process name (like "网关测试") with any modification intent, this skill MUST be invoked FIRST before any manual API exploration.

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 Univer Sheets, Docs, Slides, Bases, Boards, or PDFs in Node.js without browser UI. Use for server-side or backend Univer, OSS Sheets or Docs Node presets, Pro product Facades and collaboration, JSON snapshot processing, formula or Base child-process workers, or automated unit manipulation with @univerjs/rpc-node.

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 Blender Python (bpy) scripts that programmatically create Geometry Nodes modifier trees, using the node_groups API and GeometryNodeTree interface for parametric 3D asset generation.

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。

Specialized business strategy prompt methodology for Claude. Provides 11 tested approaches (3 identity, 4 reasoning, 4 output formats) for consulting, M&A, corporate strategy, and strategic planning tasks. Use when building prompts for deal evaluation, due diligence, portfolio strategy, business model analysis, board narratives, investment cases, market entry plans, or strategic options assessments. Trigger on: "build a prompt for M&A analysis," "due diligence prompt," "board narrative," "investment case," "market entry strategy," "strategic options," "portfolio strategy prompt," "business model analysis prompt," "consulting-style analysis." Do NOT use for evaluating or scoring existing prompts (use rootnode-prompt-validation if available) or for auditing Claude Projects (use rootnode-project-audit if 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。

Specialized prompt-building methodology for content and communications tasks. Provides 11 tested approaches (3 identity, 4 reasoning, 4 output) for writing, editing, content strategy, copywriting, audience analysis, editorial evaluation, cross-channel adaptation, and persuasion design. Use when building a Claude prompt or system prompt for content creation, editorial workflows, or communications tasks. Trigger on: "build a prompt for writing," "prompt for content strategy," "prompt for copywriting," "prompt for blog posts," "prompt for email sequences," "system prompt for editorial," "prompt for messaging framework," "content brief prompt," "prompt for audience analysis," "prompt that produces better content." Do NOT use for writing content directly — this Skill builds prompts that produce better content. Do NOT use for evaluating or scoring existing prompts — use rootnode-prompt-validation if available. Do NOT use for general prompt compilation — use rootnode-prompt-compilation if 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。

Specialized research and analysis prompt methodology for Claude. Provides 11 tested approaches (3 identity, 4 reasoning, 4 output) for data analysis, policy research, systematic evidence review, investigative research, and research-specific deliverables. Use when building prompts for quantitative data interpretation, evidence-based policy recommendations, literature reviews, causal analysis, hypothesis-driven investigation, or briefing documents. Trigger on: "prompt for data analysis," "research prompt," "policy brief prompt," "literature review prompt," "prompt for investigating," "systematic review prompt," "build a prompt for evidence synthesis," "causal analysis prompt," "briefing document prompt." Also use when user describes a research or analytical task and needs a structured Claude prompt for it. Do NOT use for evaluating existing prompts (use rootnode-prompt-validation if available) or auditing Claude Projects (use rootnode-project-audit if 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。

Builds complete, optimized Claude prompts and Project scaffolds from task descriptions. Use when building, designing, creating, or structuring a Claude prompt or system prompt. Trigger on: "build me a prompt," "compile a prompt for," "create a system prompt," "write a Claude prompt," "design a prompt for," "help me write a prompt," "scaffold a Claude Project," "design a Project for." Uses a four-stage methodology (Parse, Select, Construct, Validate) and a tested 5-layer architecture (Identity, Objective, Context, Reasoning, Output + Quality Control). In Project Mode, the Compiler is global-layer-aware — checks User Preferences, installed Skills, and MCP Connectors to avoid redundancy and flag gaps. Do NOT use for auditing existing prompts (use rootnode-prompt-validation if available) or diagnosing existing Projects (use rootnode-project-audit if available). Run on Opus 5 or Sonnet 5 at `high` effort (both defaults); depth reduces on legacy models.

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。

Node.js backend conventions: project layout, configuration, logging, routing, and error handling patterns. Apply when implementing or modifying Node.js backend code in projects matching the nodejs stack profile. Use this skill to: - Pick conventional file/folder structure for a new module. - Wire configuration through env vars correctly. - Use the project's existing logger style. - Implement error handling that matches the framework (Express/Fastify/Koa). - Follow async/await patterns consistently. Do NOT use this skill for: - Frontend code (React/Vue/RN have their own conventions). - NestJS-specific patterns (decorators, DI, modules) — nest-plugin owns those. - Database schema design (call out as a sub-task; npm-patterns covers package management, not DB).

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。

Troubleshoot and resolve common issues with the ClickHouse Node.js client (@clickhouse/client). Use this skill whenever a user reports errors, unexpected behavior, or configuration questions involving the Node.js client specifically - including socket hang-up errors, Keep-Alive problems, stream handling issues, data type mismatches, read-only user restrictions, proxy/TLS setup problems, or long-running query timeouts. Trigger even when the user hasn't precisely named the issue; vague symptoms like "my inserts keep failing" or "connection drops randomly" in a Node.js context are strong signals to use this skill. Do NOT use for browser/Web client issues.

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。

Monitors and troubleshoots GKE TPU workloads, nodes, and node pools using GKE system metrics and PromQL. Use when monitoring TensorCore duty cycle, TPU memory, node readiness, multi-host TPU node pool availability, host maintenance or preemption interruptions, and calculating MTTR or MTBI metrics for GKE TPUs. Don't use for general non-TPU GKE workload monitoring or non-metric TPU 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。

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。

Orchestrate work as a cyclic directed graph of subagent-executed nodes with transition criteria on edges, budgeted cycles, and per-node gates (runtime verification, metric, or artifact). Use when the user says "workgraph", "run this as a graph", "graph this work", or when a goal has parallel branches, feedback loops, or mixed gate types that a linear loop cannot express. Every node is sized to a 64k context budget, so small goals run as a two-node do/verify graph and large goals scale out without caps. For clock-driven or metric-driven single loops, timeboxed-iterating and autoresearch remain lighter.

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。

Quicknode blockchain infrastructure for 80+ chains: Core RPC API, Streams, Webhooks, Blazar WSS, Solana gRPC, HyperCore gRPC, SQL Explorer, Metaplex DAS API, Blockbook, Ordinals & Runes API, Swap API, x402, MPP, Agent Subscriptions, CLI, and MCP. Use for any Quicknode product, blockchain data access, or wallet-paid agent access.

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。