「nemo」の検索結果

Guide installing Earth2Studio via uv or pip, selecting model extras, and configuring the environment. Do NOT use for writing inference code, choosing models, or PhysicsNeMo questions.

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NOTE: your protein sequence and the retrieved MSA alignment are transmitted to external NVIDIA-hosted APIs (health.api.nvidia.com) on every call. Use local NIM containers for confidential or proprietary sequences. Run a complete protein structure prediction pipeline using NVIDIA BioNeMo NIMs: search for MSA alignments with MSA-Search (ColabFold), then predict the structure with OpenFold3 using the retrieved alignments. Use this skill whenever the user wants to predict a protein structure with maximum accuracy using MSA context, run the full AlphaFold3-style pipeline, generate MSA-informed structure predictions, or improve structure prediction accuracy by providing evolutionary information. Triggers on: MSA structure prediction pipeline, structure prediction pipeline, MSA-informed prediction, OpenFold3, ColabFold MSA, AlphaFold3 pipeline, protein structure, homology search, a3m alignment, UniRef30, NIM microservice. This pipeline chains MSA-Search and OpenFold3.

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Use this skill for OpenFold3, NVIDIA's BioNeMo NIM microservice for biomolecular structure prediction. Invoke whenever the user mentions OpenFold3 or needs protein, protein-ligand, protein-DNA/RNA, or multi-chain complex prediction with the hosted NVIDIA API or local Docker NIM. Covers endpoint choice, auth, request payloads, output artifacts, confidence scores, and local container setup.

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Stage 3 of Clinical ASR Flywheel. Score a NeMo manifest, produce the five-section KER leaderboard (by-ipa_source diagnostic). Not for ASR auth (/riva-asr).

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Use this skill for OpenFold2, NVIDIA's BioNeMo NIM microservice for monomer protein structure prediction. Invoke whenever the user mentions OpenFold2, AlphaFold2-like monomer folding, protein sequence-to-structure prediction, A3M MSAs, mmCIF templates, hosted NVIDIA API calls, or local Docker deployment.

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Use this skill for MolMIM, NVIDIA's BioNeMo NIM microservice for small-molecule latent-space generation and optimization. Invoke for MolMIM, molecular embeddings, hidden states, latent decoding, sampling around a seed SMILES, CMA-ES guided molecule generation, QED or plogP optimization, hosted NVIDIA API calls, or local Docker deployment.

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FAIR-aligned exposure quantification: turns a pile of recon findings into a defensible 0-100 + A-F org risk score (Likelihood x Impact, three ownership-aware factors: exposure/threat/impact), an ownership + proof demotion cap so unproven or weakly-owned findings can't inflate the number, a $-denominated FAIR loss-magnitude estimate (IBM/Ponemon per-record cost bands, cross-source record dedup, threat-factor annualization), attack-path amplification (curated red-team chain catalog + generic graph-walk engine, with a kill-chain vs shared-fate honesty gate), and a board-ready one-pager deliverable (hero $ + letter grade + top-3 findings + top attack path + the ask). Extends osint-methodology's severity rubric and client deliverable templates with quantification. Passive analysis only -- operates on findings already collected, no target traffic, no API keys. Use when asked to score risk, quantify exposure, estimate breach cost, build a board report, translate technical findings to dollars, or explain why a grade or dollar figure came out the way it did.

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Create study sessions using recall questions, chunking, mnemonics and explicit review intervals. Use for this Bot Generator Bot template; creates drafts or simulations within its declared scope.

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Designs memory palace structures with spatial layouts and domain organization. Use when creating a new palace or planning knowledge architecture by hand.

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CesiumJS viewer setup - Viewer, CesiumWidget, widgets, Ion token, Scene configuration, SceneMode, factory helpers, geocoders, platform services. Use when initializing a CesiumJS application, configuring viewer widgets, setting Ion access tokens, creating default terrain or imagery, or bootstrapping a 3D globe.

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Implement comprehensive safety guardrails for LLM applications including content moderation (OpenAI Moderation API), jailbreak prevention, prompt injection defense, PII detection, topic guardrails, and output validation. Essential for production AI applications handling user-generated content. Use when ", guardrails, content-moderation, prompt-injection, jailbreak-prevention, pii-detection, nemo-guardrails, openai-moderation, llama-guard, safety" mentioned.

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把任意主题做成「讲解/科普视频」并渲染成 MP4：调研→审查→解说词→字幕→配音（edge-tts，或火山引擎语音合成 2.0）→**主题驱动风格编排**→并行构建 HTML 场景→确定性逐帧渲染→成片后出多画幅封面。**画面语言内置 23 个模板风格 / 8 个类别**（大胆信号卡、奢华极简、NYT 数据图表、瑞士网格、故障艺术、胶片漏光、流体 Hero、Logo 收尾、东方柔和有机、VFX 文字光标…共 23 种风格，含每种的画布/配色/字体/时间轴规范，见 references/style-catalog.md）。**v2.0 新增**：①**动效库**（`assets/motion.js`，5 组共 40+ 动作词汇：弹簧/进出场/承接/接触/运镜/环境光）；②**4K60 + 快门运动模糊**（`--quality / --fps / --profile`，线性光积分 + 三级快门闸门 `--shutter-only` / `--motion-hold`）；③**渲染提速**（多浏览器进程级并行 / `--png-fast` / `--jpeg` / `--resume` 断点续渲），且**渲染前必须先问用户选哪条中间帧通道**（`png-fast` / `jpeg q95` / `png` / `jpeg q82`，**默认推荐 `png-fast`**，四条都要列全并附速度对比与描述；未拍板由 `gate_check.py --phase render` 挡下）；④**主题驱动模板编排**（`scripts/style_director.py`，按内容类型/情绪/节奏/受众挑风格、混用与局部替换、动态开头与转场，不再一片一模板）。流程规范与音画同步体系承自 anything2explainer（词边界字幕、两级时钟、语速标定、多 agent 分工与 QC 判据），渲染层为自研 seek 式渲染器。**封面默认 16:9 + 3:4 两张**：抖音主封面 1920×1080 + 兼容主页栅格 3:4 的 1440×1080（独立重排，防切字）；**竖版投放再加 9:16 的 1080×1920**（左右并置必须改上下堆叠、上下边距让开平台 UI 层）。独立可移植：GSAP 内置、playwright-core 随包、ffmpeg 走 imageio-ffmpeg 回退、浏览器自动探测 Chrome/Edge；**不依赖 html-video / anything2explainer 任何代码或目录**。触发场景：要做科普/讲解/教学/知识/产品类视频、"讲一下 X 做成视频"、要用 html-video 那种模板化画面但更稳的音画同步、要挑某种视觉风格（极简/数据/赛博/电影感/品牌）出片、要出抖音封面/竖版封面/9:16 封面、anything2explainer 换 HTML 渲染、或提到 html-explainer / HTML 讲解视频 / explainer video / MG 视频。

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Expert guide for AI Safety, Governance, and Responsible AI in production — Constitutional AI enforcement, runtime guardrails (NeMo Guardrails 2.0, Llama Guard 3), bias auditing, hallucination detection, EU AI Act compliance, model cards, and content provenance / Panduan ahli untuk Keamanan AI, Tata Kelola, dan AI Bertanggung Jawab di produksi.

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Local speech-to-text on Apple Silicon with two cross-checking MLX engines: NVIDIA Parakeet TDT v2 (parakeet-mlx) and Qwen3-ASR 1.7B (mlx-audio, hotword biasing). Batch JSONL with word timestamps, confidence, provenance, and an agreement verdict. No NeMo or PyTorch. Use when transcribing audio files, identifying game or legacy voice clips by content, telling speech from SFX, serving an OpenAI-compatible STT endpoint, or dictating. For push-to-talk into any text field, use the Handy app.

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Contribute to OpenClaw ecosystem projects (OpenClaw, NemoClaw, NanoClaw) through a structured 9-step workflow: target verification, codebase exploration, parallel audit, finding cross-reference, and pull request creation. Emphasizes false positive prevention and project convention adherence.

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Vue 3 SFC structure, Composition API + <script setup>, file naming, project layout, props/emits/slots typing, defineModel, composables, lifecycle, watchers, UI library detection. Vue 2 fallback notes. Use this skill to: - Structure a new SFC with `<script setup>`, template, scoped styles. - Type props/emits/slots correctly via macros. - Pick `ref` vs `reactive` (prefer `ref`). - Build composables that compose cleanly. - Apply lifecycle hooks correctly. - Detect UI library (Vuetify/Quasar/PrimeVue/Naive UI/Element Plus/shadcn-vue) and mirror its patterns. Do NOT use this skill for: - State management lib choice (see vue-state-management). - Routing (see vue-routing). - Forms (see vue-forms). - Testing (see vue-testing).

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Use when building autonomous LLM agents operating in live environments. Protects against prompt injection, tool abuse, data exfiltration, and runaway loops. Keywords: guardrails, prompt injection, IPI, safety, sandboxing, NeMo, LLM Guard.

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Audit whether all score-deciding logic in sgl-eval is vendored from NeMo-Skills, or whether some has crept into SE code. Use when the user asks "are we vendoring enough", "review vendor coverage", "audit vendoring", "is our vendoring complete", or before a release.

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Add a new benchmark to sgl-eval by vendoring its NeMo-Skills dataset module and registering it in `_TABLE`. Use when the user asks to "add <benchmark>" / "support <benchmark>" / "register <benchmark>" inside the sgl-eval repo.

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Kanji specialist covering character origins, radical systems, mnemonic techniques, and historical development

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Vue 3 Composition API, script setup macros, reactivity system, and built-in components. Use when writing Vue SFCs, defineProps/defineEmits/defineModel, watchers, or using Transition/Teleport/Suspense/KeepAlive.

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Use when writing, reviewing, or refactoring Vue 3 code — components, composables, reactivity, Pinia state, Pinia Colada data-fetching, file-based routing, and forms. Targets Composition API + <script setup> + TypeScript. Auto-activates on .vue files. For deep best-practices review/recommendations or the full ordered workflow (built-in components, animations, performance pass), use mk:vue-best-practices.

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Setup Obsidian mobile sync via GitHub (free) or Obsidian Sync (paid). Includes GitHub CLI automation, MGit/Working Copy setup, and Mnemosyne integration patterns.

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Modelo de moderação especializado 7-8B do Meta para filtragem de entrada/saída de LLM. 6 categorias de segurança - violência/ódio, conteúdo sexual, armas, substâncias, automutilação, planejamento criminal. Precisão de 94-95%. Deploy com vLLM, HuggingFace, Sagemaker. Integra com NeMo Guardrails.

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