スキルを探す

钉钉消息发送技能。支持企业内部机器人（批量单聊/群聊）和 Webhook 自定义机器人两种接入方式，支持多机器人管理，支持文本、Markdown、链接、ActionCard、FeedCard等多种消息类型。

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。

Connect the Newton physics engine to an Omniverse simulation or ovstage with ovnewton. Use when importing a populated ovstage into Newton, stepping its physics, synchronizing stage edits, or publishing simulation state for Omniverse rendering.

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 AI agent's ethos.md — a durable snapshot of its purpose, intended behavior, boundaries, and success criteria — from the answers gathered by ethos-explore. Renders the schema-v1 template, validates the front matter and required markdown sections, saves the file in the agent's repository, and reviews it with the user. Also edits an existing ethos.md. Use over generic planning skills for an agent's ethos.md.

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。

Entry point for an AI agent's ethos.md: a durable snapshot of what the agent is for, what it must not do, and what success looks like. Routes a request to explore and capture intent (ethos-explore), or write or edit the file (ethos-write), including renaming an Eval Author 0.1.0 ETHOS.md. Owns the standard every ethos sub-flow follows. Use when the user asks "help me write an ethos.md", "does my agent have an ethos.md?", "update my agent's ethos.md", or when you need to pick between the ethos sub-flows.

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。

Plan NVIDIA Parabricks pbrun commands, translate CPU options and check runtime readiness. Use for individual tools, including planning-only questions.

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 a user asks to build, optimize, backtest, rebalance, or analyze a stock portfolio with Mean-CVaR, Mean-Variance/SOCP variance caps, efficient frontiers, scenario generation, or NVIDIA cuOpt.

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 only to generate or update a governance skill card for a specified existing agent skill directory. Do not use for explaining, listing, comparing, or discussing skill capabilities.

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。

Used for running NV-Segment-CT VISTA3D on CT NIfTI volumes and recording label-map evidence.

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 as the top-level router for Omniverse Realtime Viewer USD app requests and focused viewer reference documents.

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 the user asks why a reported NVFLARE job failure signal occurred: the job failed, stalled, timed out, lost clients, ended with EXECUTION_EXCEPTION, or produced suspicious errors. Diagnose in simulation, POC, or production by collecting bounded evidence and mapping failure patterns to recovery actions.

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。

Used for running NV-Segment-CTMR on CT or MRI NIfTI volumes and recording label-map evidence. Not for clinical interpretation.

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。

Route open-ended NVFLARE advice and only conversion requests whose preliminary source inspection reports unresolved or conflicting ownership; never load this skill merely to inspect a concrete conversion request before selecting its detected framework converter.

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。

Techniques for reducing peak GPU memory in Megatron Bridge — expandable segments, PEFT + SP input re-gather, parallelism resizing, activation recompute, CPU offloading constraints, and common OOM fixes.

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。

Operational guide for choosing and combining parallelism strategies in Megatron Bridge, including sizing rules, hardware topology mapping, and combined parallelism configuration.

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。

Practical guidance for training MoE VLMs in Megatron Bridge. Compares FSDP and 3D-parallel approaches, using rounded lessons from Qwen3-VL, Qwen3-Next, and other multimodal experiments.

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。

Guide for selecting and configuring distributed training strategies in NeMo AutoModel, including FSDP2, Megatron FSDP, DDP, and parallelism settings.

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。

Validate and use selective and full activation recompute in Megatron Bridge to reduce GPU memory usage at the cost of extra compute. Use for activation memory OOMs or regressions involving recompute_granularity, recompute_num_layers, recompute_modules, recompute_method, selective recompute, full recompute, or activation checkpointing.

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。

Choose the right MoE token dispatcher (`alltoall`, DeepEP, or HybridEP) for the hardware, EP degree, and optimization stage. Summarizes patterns from DSV3, Qwen3, Qwen3-Next, and VLM bring-up 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。

Representative, point-in-time MoE training playbooks by hardware and model family. Use them as candidate seeds, then revalidate the exact runtime, semantics, topology, and steady-state throughput.

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。

Exports sanitized metadata, parameters, reproducibility details, quality metrics, and optional review artifacts from Medical AI inference runs or evidence packs to MLflow. Use after inference, including NV-Generate runs; not for live training tracking, model registration, or clinical use.

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。

Long-context MoE training guidance for Megatron Bridge. Covers CP sizing, selective recompute, dispatcher choices, and practical patterns from DSV3, Qwen3, and Qwen3-Next long-context experiments.

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 baseline NeMo Relay instrumentation exists and the user wants to configure or evaluate adaptive plugin behavior, including telemetry, state, adaptive_hints, tool_parallelism, ACG, hint consumption, or measured rollout.

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。

Guide for onboarding new model architectures into NeMo AutoModel, including architecture discovery, implementation patterns, registration, and validation.

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。

Validate and use CPU offloading in Megatron Bridge, including layer-level activation offloading and fractional optimizer state offloading with HybridDeviceOptimizer.

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。