「background」の検索結果

A distinctive GPU shader background rendered in dependency-free WebGL — a marketing-grade hero backdrop. One per view; scrim behind text. Renders a static frame under prefers-reduced-motion.

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。

A distinctive GPU shader background rendered in dependency-free WebGL — a marketing-grade hero backdrop. One per view; scrim behind text. Renders a static frame under prefers-reduced-motion.

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。

A distinctive GPU shader background rendered in dependency-free WebGL — a marketing-grade hero backdrop. One per view; scrim behind text. Renders a static frame under prefers-reduced-motion.

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。

A distinctive GPU shader background rendered in dependency-free WebGL — a marketing-grade hero backdrop. One per view; scrim behind text. Renders a static frame under prefers-reduced-motion.

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。

A living GPU shader background rendered in dependency-free WebGL — a distinctive, marketing-grade hero backdrop. One per view; put a scrim behind text. Renders a static frame under prefers-reduced-motion.

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。

A distinctive GPU shader background rendered in dependency-free WebGL — a marketing-grade hero backdrop. One per view; scrim behind text. Renders a static frame under prefers-reduced-motion.

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。

Redesign a lesson to centre students' cultural backgrounds, community knowledge, and lived experience. Use when making curriculum relevant and inclusive for diverse student populations.

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 blueprint for scene loading, transitions, async (background) loading, instance management, and caching. Covers fade transitions, loading screens, dynamic spawning, and scene persistence. Use when implementing level changes OR dynamic content loading. Keywords scene, loading, transition, async, ResourceLoader, change_scene, preload, PackedScene, fade.

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。

Bevy ECS skill for UI theming. Keywords: Bevy, UI theme, BackgroundColor, BorderColor, feathers.

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。

Bevy ECS skill for UI layout. Keywords: Bevy, UI, Node, Flexbox, BackgroundColor, Button.

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。

Interactive QA session where user reports bugs or issues conversationally, and the agent files GitHub issues. Explores the codebase in the background for context and domain language. Use when user wants to report bugs, do QA, file issues conversationally, or mentions "QA session".

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。

Swift 6.2 concurrency updates including default MainActor inference, @concurrent for background work, isolated conformances, and approachable concurrency migration. Use when adopting Swift 6.2 concurrency features or fixing data-race errors.

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 guidance on macOS platform capabilities. Covers sandboxing, app extensions, menu bar apps, and background execution. Use when implementing system integration features.

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 state preservation and restoration infrastructure for navigation paths, tab selection, scroll positions, and form data across app launches and background termination. Use when user wants to save/restore app state, remember where the user left off, or persist UI state.

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。

Code generator skills that produce production-ready Swift code for common app components. Use when user wants to add logging, analytics, onboarding, review prompts, networking, authentication, paywalls, settings, persistence, error monitoring, CI/CD pipelines, localization, push notifications, deep linking, testing, accessibility, widgets, feature flags, app icons, image caching, pagination, HTTP caching, share cards, social export, subscription lifecycle, referral systems, watermarks, streak tracking, milestone celebrations, what's new screens, lapsed user re-engagement, usage insights, variable rewards, consent flows, account deletion, permission priming, force updates, state restoration, debug menus, offline queues, feedback forms, announcement banners, quick win sessions, Spotlight indexing, App Clips, screenshot automation, background processing, app extensions, data export, or SwiftUI preview sample data and variant matrices.

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。

mastR is an R package designed for automated screening of signatures of interest for specific research questions. The package is developed for generating refined lists of signature genes from multiple group comparisons based on the results from edgeR and limma differential expression (DE) analysis workflow. It also takes into account the background noise of tissue-specificity, which is often ignored by other marker generation tools. This package is particularly useful for the identification of g

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。

We propose an Asymmetric Within-Sample Transformation (AWST) to regularize RNA-seq read counts and reduce the effect of noise on the classification of samples. AWST comprises two main steps: standardization and smoothing. These steps transform gene expression data to reduce the noise of the lowly expressed features, which suffer from background effects and low signal-to-noise ratio, and the influence of the highly expressed features, which may be the result of amplification bias and other experi

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。

MHC (major histocompatibility complex) molecules are cell surface complexes that present antigens to T cells. The repertoire of antigens presented in a given genetic background largely depends on the sequence of the encoded MHC molecules, and thus, in humans, on the highly variable HLA (human leukocyte antigen) genes of the hyperpolymorphic HLA locus. More than 28,000 different HLA alleles have been reported, with significant differences in allele frequencies between human populations worldwide

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 knowledge for Azure AI Vision development including decision making, limits & quotas, configuration, integrations & coding patterns, and deployment. Use when using Image Analysis, Read OCR containers, smart-crop thumbnails, background removal, or video frame analysis, and other Azure AI Vision related development tasks. Not for Azure AI Custom Vision (use azure-custom-vision), Azure AI Video Indexer (use azure-video-indexer), Azure AI Document Intelligence (use azure-document-intelligence), Azure AI Immersive Reader (use azure-immersive-reader).

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。

Always-on spam and deliverability guardrails for cold email copy. Apply these rules whenever writing, reviewing, or QA-ing subject lines, openers, follow-ups, CTAs, or any cold outreach copy. Trigger on phrases like "check this for spam", "spam word check", "deliverability review", "QA this copy", "flag banned words", "check subject line", "is this safe to send", "scan for spam triggers", or any time cold email copy is being generated or reviewed. Also trigger automatically as a background rule whenever the campaign-copywriting or cold-email-copywriting skill is active -- these guardrails apply to EVERY piece of outbound copy generated, even if the user does not explicitly ask for a check.

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 gives or references a webpage URL and wants to extract, reproduce, port, or locally replay a WebGL, WebGPU, Canvas, shader-like, animated background, interactive 3D, or web visual effect. Locks the target rendering surface group, records shader/resource/render graph/timing/input evidence, builds an evidence-matched local baseline, then projectizes it. Do not use for ordinary DOM/CSS page cloning.

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 AUTOMATICALLY before writing any Higgsfield prompt. Query the memory databases for relevant past failures and pre-apply known fixes before the user even hits generate. Triggers include: any request to write a Higgsfield prompt, any use of the higgsfield-prompt skill, any mention of generating a video or image on Higgsfield, any MCSLA prompt construction. This skill should run SILENTLY in the background — don't announce it, just apply what's known. If the databases are empty, skip silently and proceed with normal prompt 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。

Separate a person, product or hand from the background in a video, on the user's own computer with SAM 2. Scans the machine first to pick the right model size, then outputs the same video with the separated subject highlighted in green, plus frame-by-frame masks. Use when someone wants to separate, isolate, cut out or rotoscope something in a video.

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。