「light」の検索結果

Build and distribute Expo development clients locally or via TestFlight

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。

Keep pnpm current: preflight the published package and pnpm/action-setup self-installer, update pnpm locally, align packageManager in package.json, and refresh CI pins. Use this when refreshing the pnpm toolchain manually or in automation.

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。

Runs rigorous visual QA across web, terminal, and paginated surfaces: an Apple HIG-based checklist, paired light/dark captures at phone and desktop widths, screenshot evidence, and a per-item PASS/FAIL verdict. Use for any UI build or change, or when asked whether a page, component, or TUI looks right or follows the platform's design guidelines.

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。

Zero-shot time series forecasting with Google's TimesFM foundation model. Use this skill when forecasting ANY univariate time series — sales, sensor readings, stock prices, energy demand, patient vitals, weather, or scientific measurements — without training a custom model. Automatically checks system RAM/GPU before loading the model, supports CSV/DataFrame/array inputs, and returns point forecasts with calibrated prediction intervals. Includes a preflight system checker script that MUST be run before first use to verify the machine can load the model. For classical statistical time series models (ARIMA, SARIMAX, VAR) use statsmodels; for time series classification/clustering use aeon.

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。

Open-ended scientific ideation partner. Use for research gaps, mechanism exploration, interdisciplinary connections, assumptions, possible research directions, and lightweight literature matrix or A+B paper-combination idea mapping. For structured testable hypotheses and validation plans, use hypothesis-generation 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。

Model interpretability and explainability using SHAP (SHapley Additive exPlanations). Use this skill when explaining machine learning model predictions, computing feature importance, generating SHAP plots (waterfall, beeswarm, bar, scatter, force, heatmap), debugging models, analyzing model bias or fairness, comparing models, or implementing explainable AI. Works with tree-based models (XGBoost, LightGBM, Random Forest), deep learning (TensorFlow, PyTorch), linear models, and any black-box model.

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Lightweight WSI tile extraction and preprocessing. Use for basic slide processing tissue detection, tile extraction, stain normalization for H&E images. Best for simple pipelines, dataset preparation, quick tile-based analysis. For advanced spatial proteomics, multiplexed imaging, or deep learning pipelines use pathml.

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。

Lightweight green/red status panel for every configured integration. No gather, no 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。

Analyzes job descriptions and generates tailored resumes that highlight relevant experience, skills, and achievements to maximize interview chances

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。

整片视频产线（原片 → 包装级成片）：九步流程 + 八件质量门 + 两道人工确认门，产出成片与全套交付物。 当用户说"用视频产线做一支整片""把这条原片做成片""整片包装""走视频产线"时使用。 全程经独立接口驱动：每步可核验、门不过不交付。 与兄弟技能分界：本 SKILL 的**输入是已有的整条原片**，做的是重包装（分场/动效/字幕/调色 + 质量门）， 过程重、需两次人工点头；从一句话主题**无中生有**出片用 auto-short-video； 手上只有一组图片用 slideshow-video；把长片**切成多条**短视频用 clipify 或 video-highlights。

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。

用自然语言指令剪辑视频：裁剪、拼接、变速、跳切去静音、文字覆盖、横竖比转换、抽帧封面、转 GIF、压缩、加 BGM/水印，基于 ffmpeg。 当用户说"剪视频""裁剪视频""拼接视频""视频变速""去静音""视频加文字""视频转GIF""压缩视频""加BGM/水印"时使用。 本 SKILL 做单条视频的通用剪辑；智能画幅转换用 video-reframe，长视频切片用 clipify/video-highlights，从 0 生成用 ai-video-gen。

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 running video data augmentation and auto-labeling workflows on OSMO: flow selection, preflight, submit-time interpolation, monitoring, and output retrieval. Trigger keywords: video data augmentation, data enrichment, auto labeling, VDA demo, OSMO workflow, pseudo labeling.

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Use when operating PAIDF Curation and Retrieval or NVIDIA Cosmos Curator pipelines (split, filter, caption, embed, dedup, shard, image annotate) or PAIDF Data Mining nearest-neighbor matching on Curator embeddings. Activate for Make or CLI pipeline config, GPU run preflight, FFmpeg sidecar, SAM3 keys, or Curator-to-TAO handoff. Do not use for generic ETL, vector-database RAG, model training, orchestration, or embeddings outside Cosmos Curator and PAIDF Data Mining.

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 existing Hugging Face Transformers Trainer or TRL SFTTrainer training code into an NVFLARE federated job using flare.patch(trainer), local validation, and job export; use when the user names Hugging Face or preliminary source inspection identifies one Hugging Face owner, and not for manual PyTorch loops, Lightning, inference-only pipelines, deployment, or experiment workflows.

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 existing plain or manual PyTorch training code into an NVFLARE federated job using Client API model exchange, local validation, and job export; use when the user names plain PyTorch or preliminary source inspection identifies one plain-PyTorch owner, and not for Lightning, other frameworks, deployment, POC/production lifecycle, or experiment workflows.

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。

Preflight and set up the root-level workflow runtime. Use for installation, missing component environments, or third-party failures; do not use for rollout 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。

Create a minimal blank Workflow scaffold with a Scene containing ground and light plus an idle run mode. Use for fast new Workflow scaffolding.

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 whenever the agent is about to recommend or apply a change that touches DPU / NIC hardware state on a live system — mlxconfig firmware-parameter write, NIC firmware burn, BFB reflash, NIC ↔ DPU mode flip, SR-IOV or device-emulation slot enable, kernel boot-parameter change (IOMMU, hugepages, VFIO), PCIe rebind / rescan / link-state flip, or BlueField cold reboot. Wraps the change in pre-flight inventory, OOB reachability, a maintenance window, the mlxconfig cold-power-cycle rule, replica rehearsal, and rollback. Trigger even when the user does not say "hardware safety" — implicit phrasings: "flip BlueField mode over SSH", "enable SR-IOV and reboot", "burned firmware but mlxconfig shows old value", "reflashed BFB and lost representors", "reflash during business hours", "vendor says this is one-way". Refuse for general DOCA orientation (doca-public-knowledge-map), install or env debug (doca-setup), and program-side debug (doca-debug, doca-programming-guide) — those belong to other skills.

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。

Golang concurrency design — goroutine lifecycle and leak prevention, channels and `select`, channel ownership and direction, `sync.Mutex`/`RWMutex`/`sync.Map`/`sync.Once`/atomics, `errgroup`, `singleflight`, worker pools, and fan-out/fan-in pipelines. Use when writing or reviewing concurrent Go code, when choosing between channels and mutexes, when protecting a shared map or counter, or when a goroutine has no clear exit. Not for defensive coding unrelated to concurrency such as nil panics, slice aliasing, or numeric overflow (→ See `samber/cc-skills-golang@golang-safety` skill), and not for debugging a specific hung, crashing, or racing program after the fact (→ See `samber/cc-skills-golang@golang-troubleshooting` skill).

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[omh] Releasing an iOS or Android app through the stores -- signing, the privacy manifest, a TestFlight or Play beta, a staged rollout, a hotfix: prepare each gate the store enforces, and plan the halt and the next build before the rollout starts, because a store release cannot be rolled back. Use when the user says: mobile-release, mobile release, mobile app release, ios release, android release, app store release, app store submission, submit to the app store.

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。

Full pre-PR merge-readiness check. Run this before opening or merging a pull request — it validates local gates (lint, format, tests), CI status, screenshot evidence, and PR metadata in one pass. Also useful for reviewing an existing PR's readiness.

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。

Visualize, analyze, and render protein and molecular structures using PyMOL. Use when the user wants to create images of protein structures, perform structural alignments or superposition, measure distances or contacts, highlight binding sites or active site residues, color by B-factor/pLDDT, or analyze protein-ligand interactions. Do not use for docking, molecular dynamics, or sequence-only analysis.

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Use the Classic preset for a lightweight adjustment within one change. Use when the user explicitly invokes /comet-tweak, selects tweak, or resumes workflow: tweak.

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Pre-deployment validation for Azure readiness. Run deep checks on configuration, infrastructure (Bicep or Terraform), RBAC role assignments, managed identity permissions, and prerequisites before deploying. WHEN: validate my app, check deployment readiness, run preflight checks, verify configuration, check if ready to deploy, validate azure.yaml, validate Bicep, test before deploying, troubleshoot deployment errors, validate Azure Functions, validate function app, validate serverless deployment, verify RBAC roles, check role assignments, review managed identity permissions, what-if analysis, validate Container Apps deployment.

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。