「overlay」の検索結果

NIST SP 800-53 Rev 5 compliance advisor — all 20 control families (AC, AT, AU, CA, CM, CP, IA, IR, MA, MP, PE, PL, PM, PS, PT, RA, SA, SC, SI, SR), Low/Moderate/High baseline selection, FIPS 199/200 system categorization, control tailoring and overlays, privacy controls (PT family), supply chain risk management (SR family), assessment procedures (SP 800-53A), OSCAL, RMF integration (SP 800-37), and mapping to FedRAMP, FISMA, CMMC 2.0, and ISO 27001. Use for any federal system security controls, FISMA compliance, RMF step guidance, control narrative writing, or baseline tailoring question.

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 and record scripted browser demo videos with webreel. Generates MP4, GIF, or WebM recordings with cursor animation, keystroke overlays, and sound effects from a JSON config. Use when the user wants to record a demo, create a browser video, edit a webreel config, generate a screen recording, preview a demo, or work with webreel in any way.

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。

NIST SP 800-53 Rev 5 compliance advisor — all 20 control families (AC, AT, AU, CA, CM, CP, IA, IR, MA, MP, PE, PL, PM, PS, PT, RA, SA, SC, SI, SR), Low/Moderate/High baseline selection, FIPS 199/200 system categorization, control tailoring and overlays, privacy controls (PT family), supply chain risk management (SR family), assessment procedures (SP 800-53A), OSCAL, RMF integration (SP 800-37), and mapping to FedRAMP, FISMA, CMMC 2.0, and ISO 27001. Use for any federal system security controls, FISMA compliance, RMF step guidance, control narrative writing, or baseline tailoring question.

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 zero-hallucination privacy-policy generator that takes anyone — non-lawyer founder to lawyer — from a guided intake to a publishable, jurisdiction-aware privacy policy. Jurisdiction-first: it detects which laws apply from where your users are, then drafts only the required clauses — GDPR/EU + UK, US (CCPA/CPRA, ~20 state laws, COPPA, sector overlays), and global/MENA (LGPD, Quebec Law 25, India DPDP, China PIPL, UAE/DIFC, Saudi PDPL), plus app-store, cookies, and AI/EU AI Act disclosures. Its rule: state only what you confirm; never invent a statute, citation, fine, or date — every claim is source-cited and QA-gated. Not legal advice.

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。

Assess compliance with the EU General-Purpose AI (GPAI) Code of Practice under the AI Act (Regulation (EU) 2024/1689). Use when asked to determine GPAI model provider obligations, assess Code of Practice compliance, evaluate transparency or copyright requirements for GPAI models, check systemic risk obligations, build a GPAI compliance roadmap, or prepare for AI Office requests. Covers all three Code chapters (Transparency, Copyright, Safety & Security), the 12 commitments and their measures, systemic risk classification (Article 51), provider obligations (Articles 53 and 55), open-source exemptions, enforcement timeline, Commission/AI Office supervision, and DACH-specific overlays such as BSI cybersecurity and works council implications. Triggers on phrases like "GPAI compliance", "Code of Practice assessment", "GPAI model obligations", "systemic risk evaluation", "AI Act transparency requirements", "GPAI copyright policy", "general-purpose AI compliance".

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 film-grain noise overlay — subtle animated texture over any background. 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。

Apply a Classic Easter seasonal overlay: pastel Theme/StyleBox injection, confetti/shimmer VFX, elastic juice, mesh surface_override painting, cursor/audio swaps, and a date-window activation gate with player opt-out. Use when shipping holiday skins, April events, or egg-hunt juice without mutating shared .mesh/.tres assets. Keywords: pastel, seasonal Theme, StyleBox, confetti, Easter egg, surface_override, TRANS_ELASTIC, Disable Seasonal Themes, activation gate.

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。

This R package supports interactive visualization of multi-channel images and segmentation masks generated by imaging mass cytometry and other highly multiplexed imaging techniques using shiny. The cytoviewer interface is divided into image-level (Composite and Channels) and cell-level visualization (Masks). It allows users to overlay individual images with segmentation masks, integrates well with SingleCellExperiment and SpatialExperiment objects for metadata visualization and supports image do

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 a watermark overlay system for images and video with configurable positioning, opacity, tiling, and paywall integration for automatic removal on subscription or purchase. Use when user wants to add branding, attribution, or free-tier watermarks to visual content.

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 an automated App Store screenshot pipeline with UI tests for screenshot capture, device framing, localized caption overlays, and multi-size batch export. Use when user wants automated screenshots, App Store screenshot generation, or a fastlane snapshot replacement.

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。

Search for images on Unsplash and download them. Optionally add text overlay (title/subtitle) to the image. Use when the user asks to find a stock photo, search for an image, get an Unsplash image, or download a photo with text.

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。

Build an interactive competitive-traffic report for any company and its rivals — monthly visits (SimilarWeb), organic search traffic and Domain Rating (Ahrefs) — as one self-contained HTML page with ranked charts, a three-month trend, a multi-year overlay and a data table. Use when asked to benchmark a product against competitors, size up a market, answer "how much traffic does X get compared to Y", build a competitive landscape, or produce a traffic/authority comparison deck or page.

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。

Turn a talking-head video and its transcript into motion-graphic B-roll timed to the speaker's words, in a continuous one-shape style (one element that morphs and never cuts, driven by a cursor, spring motion). Use when someone gives a video (and ideally a transcript) and asks for motion graphics, B-roll, animated cutaways or overlays.

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 an editor or videographer needs production direction — beat-by-beat storyboard, footage to shoot, b-roll, text overlays, music and pacing, transitions, CTA end frame, and per-channel export specs. This is the EDIT instruction, not the spoken script. Trigger on 'video brief', 'editor brief', 'storyboard', 'brief for the video editor', 'how should this video be cut', 'we have the footage and no direction'. Also use when raw clips exist and nobody knows what the final should look like. Not for — the words said on camera, see `04-script-video-global`; briefing a creator to film their own, see `06-ugc-egc-brief-global`; resizing a finished video, see `50-asset-resize-global`.

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 ONE static image needs a full brief for a designer or photographer — visual objective, concept and mood, composition, overlay copy, colors and fonts read from the brand guideline, per-channel dimensions, deliverables, and approval criteria. Trigger on 'image brief', 'banner brief', 'thumbnail brief', 'brief for the designer', 'how should this graphic look', 'the design came back wrong again'. Also use when the user has a post idea and needs to explain the visual. Not for — a multi-slide sequence, see `43-carousel-brief-global`; tool-level production steps in Canva or Figma, see `45-design-tool-brief-global`; generating the image with AI, see `30-design-master-global`; an urgent same-day swap, see `48-quick-visual-brief-global`.

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。

用于把 AI 生成的视频、本地素材、口播素材或产品短片剪成可发布到 TikTok 的竖屏成片。 当用户要求剪短视频、TikTok/Reels/Shorts 版本、9:16 重构图、批量短视频剪辑、AI 视频二创、 加大字字幕、BGM、音频 ducking、hook overlay、去空白、粗剪、导出 final.mp4 或根据素材文件夹 生成可发布短视频时，应使用此 skill。适合本地 FFmpeg/Whisper/Codex 工作流。

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。

Mandatory entry point: read this first for any request to make, create, edit, animate, or render a video, animation, or motion graphic, including a promo, explainer, captioned clip, title card, overlay, slideshow or interactive deck, Remotion port, or any HyperFrames HTML composition. Also use it to inspect, diagnose, validate, preview, publish, or batch-render an existing HyperFrames project. Inputs may be a website URL, GitHub PR, Figma design or URL, text or brief, existing footage, or music. It resumes project state, captures intent when applicable, selects and installs the owning workflow, and routes domain capabilities. HyperFrames is the default output framework unless the user explicitly chooses another framework for the deliverable or asks only to record a browser 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。

Elixir/Phoenix deployment patterns — Dockerfile, fly.toml, runtime.exs, mix release, rel/ overlays. Use when configuring Fly.io, Docker, CI/CD, health checks, or production migrations.

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This skill should be used when the user asks about "ralph arguments", "quick mode", "commit spec", "max iterations", "ralph state file", "prototype overlay", "execution modes", "ralph loop", "coordinator behavior", "delegate to subagent", or needs guidance on Ralph plugin arguments, state management, delegation patterns, or execution loop behavior. Core behavioral skill for all Ralph Specum operations.

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。

Prevent Kubernetes hallucinations by diagnosing and fixing failure modes: insecure workload defaults, resource starvation, network exposure, privilege sprawl, fragile rollouts, and API drift. Use when generating, reviewing, refactoring, or migrating manifests, Helm charts, Kustomize overlays, cluster policies, and platform-specific Kubernetes work for EKS, GKE, AKS, OpenShift, GitOps controllers, or observability stacks.

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。

Enhancement-overlay SOP for adding sparse (BM25 / keyword) retrieval alongside dense (embedding) retrieval. Activate when a calling agent is building, reviewing, or debugging a retrieval pipeline whose corpus contains exact-match tokens — identifiers, error codes, SKUs, API/function names, proper nouns, citations, rare jargon — that pure dense embedding silently misses. Encodes the single decision rule (**hybrid is traffic-driven, not theoretical: add sparse only when the query share that depends on exact tokens is non-trivial**), the wiring of QueryFusionRetriever-style fusion (RRF vs alpha-weighted), and per-query-type alpha tuning. Frame the work as recovering lexical identity that dense pooling destroys, not as "add keyword search for completeness". Cross-links [[llamaindex]].

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。

Cross-framework enhancement overlay for choosing a multi-agent topology BEFORE writing any agent. A binary-question rubric — is single-agent + tools enough? do agents need to know about each other? does the output need one voice? — maps the answer to single-agent / supervisor / swarm / sequential / hierarchical. Activates when a coder agent is tempted to "split the work into roles" or reaches for a multi-agent framework. Encodes the *selection rubric* that the per-framework skills assume but never surface. Search keywords: when to use multi-agent, single vs multi agent, do I need multiple agents, supervisor vs swarm, multi-agent vs single agent, agent team design.

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Enhancement-overlay skill — the DECISION + WIRING layer for LM observability that the single-backend skills [[langsmith]], [[phoenix]], [[mlflow]] do NOT cover. Each of those installs one backend; none of them help you DECIDE which backend fits your stack/scale/budget, nor give you a one-line autolog that turns it on fast. Use when starting any LM project, before the first deploy, or the moment someone asks "why did it do that?" and there are no traces to answer with. The skill picks a backend by stack (LangSmith for LangChain/LangGraph; Phoenix for OSS/local OpenTelemetry; MLflow for ML-shops already on MLflow; Langfuse for self-host), wires one-line autolog, verifies traces land, and adds eval hooks — instrumenting BEFORE you need it. Cross-links the first-debug-move skill [[agentsop-prompt-history-inspect]]. Do NOT activate to re-teach a backend you already chose (defer to its own skill), or for non-LM ML experiment tracking with no LLM calls (that is plain MLflow).

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Re-ingest-correctness SOP for production RAG. Activate when a calling agent builds, reviews, or debugs an ingestion pipeline that runs more than once over a changing corpus — scheduled re-index, incremental updates, CI re-ingest, or a "retrieval has duplicates / shows deleted docs" bug. Encodes the rule — **ingestion must be idempotent: a document's content hash decides insert/update/skip, so re-running over unchanged docs is a no-op** — plus the docstore + doc-hash upsert machinery (LlamaIndex `IngestionPipeline` + `DocstoreStrategy`), the delete-propagation problem, and cross-framework equivalents (LangChain `index()` + `RecordManager`, manual hash table). ENHANCE overlay over [[llamaindex]]: the IngestionPipeline exists in the base skill but the re-ingest-correctness contract is not surfaced.

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。