「visual」の検索結果

Create an offline HTML explanation of a code change (diff, PR, branch) or of a topic, system, or question. Use when a visual walkthrough document is requested.

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 publication-quality figures for ML papers from research context. Given a paper section or description, extracts system components and relationships to generate architecture diagrams via Gemini. Given experiment results or data, auto-selects chart type and generates data-driven figures via matplotlib/seaborn. Use when creating any figure for a conference paper.

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。

Records visual proof while testing UI behavior — the agent tests the app hands-on via computer use while a screen recording with structured test/assertion annotations captures the session — then posts the video and a results summary to the PR and tracker issue. Use whenever a change needs verifiable evidence that it works, instead of prose claims — including headless environments (scripted screenshots and probes) and non-UI changes (measured numbers, output pairs).

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 to create, convert, style, animate, export, share, or deploy HTML slide decks, presentations, pitch decks, talks, or keynotes; including zero-dependency or single-file output, PowerPoint/PPT/PPTX input, PDF export, browser delivery, visual style exploration, or "loopy slides". Not for general web pages or landing pages.

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。

Visually validate an interactive CLI or terminal UI in Codex's in-app browser using a real local PTY, fixed rendering, keyboard interaction, screenshots, and session evidence. Use for TUI layout changes, terminal menus, colors, prompts, resize behavior, keyboard flows, or proof-of-work where Codex would otherwise reach for Computer Use or a desktop terminal. Do not use for desktop-terminal-emulator-specific behavior or ordinary non-interactive command output.

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 for Superloopy clone or asks to clone, rebuild, reverse-engineer, replicate, or copy a website or page into a Superloopy-governed implementation. Triggers on "loopy clone", target URLs plus requests such as "clone this site", "rebuild this page", "make a copy of this website", "pixel-perfect clone", or "AI website clone". Requires browser automation and records component specs, assets, implementation, build output, and visual QA as Superloopy 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。

SERP-based semantic topic clustering for content architecture planning. Groups keywords by actual Google SERP overlap (not text similarity), designs hub-and-spoke content clusters with internal link matrices, and generates interactive visualizations. Optionally executes content creation if claude-blog is installed. Use when user says "topic cluster", "content cluster", "semantic clustering", "pillar page", "hub and spoke", "content architecture", "keyword grouping", or "cluster plan".

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。

Context-led frontend design: art direction derived from product, audience, brand and the existing system, not generic defaults. Named mood variants listed inside. Triggers: frontend, UI, CSS, styling, visual, theme, component, layout, responsive, accessibility, animation.

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 for AI-assisted Unity work that needs live repo discovery, project-derived routing, runtime-owner proof, runtime-visible output hard stops, runtime numeric proof for repeated visible-output failures, state-step guards, multi-agent scope ownership, modular C#/asmdef safety, UI/scene/visual asset gates, data-first content changes, validation, cleanup proof, or durable workflow rules. Best when agents must prove the actual folder/module/scene/prefab/runtime owner before editing, especially runtime UI, generated assets, code graphs, tutorial/state flows, guided selection/action flows, overlay/dim source-bound mistakes, coordinate conversions, focus/highlight/marker/HUD alignment, predictive lead aiming, ballistic trajectories, spatial IoU verification, or repeated "fix still not visible" failures.

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。

Creates data visualizations (charts, graphs, tables) optimized for AI engine parsing and citation. Produces inline SVG/HTML with text summaries, data tables, and JSON-LD so AI engines can quote the data.

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 animated flow diagrams from articles, workflow notes, architecture sketches, or process descriptions using a JSON specification and a local Python/Pillow renderer. Use when the user wants to turn source material into a static PNG + animated GIF diagram.

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。

Source authentic, high-res PUBLIC-DOMAIN artwork from museum open-access APIs (Met, Cleveland, SMK, Rijksmuseum, NGA, Art Institute of Chicago, Getty, Smithsonian) instead of AI-generated or generic-stock imagery. The default move whenever a visual needs an aesthetic, credible image (blog heroes, decks, social cards, essay/spec figures). Verified keyless recipes + licensing rules inside.

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。

Generate meaning-carrying editorial data-illustrations in the monotykamary / Linear aesthetic (near-black grayscale, Inter display + mono labels, hairline framed figures) with a single coral accent. This is a GENERATIVE GUIDE, not a template gallery: it teaches the "claim -> geometry" method so any session can invent the RIGHT figure for what a post actually argues, then render it as a self-contained, theme-aware, reduced-motion-safe HTML/SVG figure. Use when writing/illustrating a blog post, essay, spec, or slide and you want a chart, diagram, or illustration that carries a thesis (not decoration) in the house style. Triggers: "make a chart/diagram/illustration for this", "visualize this argument", "monotykamary/Linear style figure", "editorial graphic".

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。

WCAG 2.2 Accessibility Audit skill that systematically evaluates web pages against 8 core Success Criteria (1.1.1, 1.4.3, 1.4.11, 2.1.1, 2.1.2, 2.4.3, 2.4.7, 4.1.2) using accessibility tree inspection and visual analysis. Use this skill when you need to perform accessibility testing/auditing on a live webpage.

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 designing or auditing figures for an Advanced Materials manuscript, including the lead figure, multi-panel characterization figures, and the Table-of-Contents graphic, where visual quality is a house expectation. Designs figure strategy; does not run the analysis or write prose.

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 stunning, animation-rich HTML presentations from scratch or by converting PowerPoint files. Use when the user wants to build a presentation, convert a PPT/PPTX to web, or create slides for a talk/pitch. Helps non-designers discover their aesthetic through visual exploration rather than abstract choices.

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 and visualize the world's data - get charts, insights, and embeddable knowledge cards for finance, economics, demographics, sports, and more

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。

Portable visual skill pack for the Agent Skills ecosystem (Claude Code, Claude Desktop, Claude Cowork, Claude Design, Goose, Cursor, Codex). Discovers community-published styles + formats via the gooseworks CLI, runs an extract-style workflow on reference images, and exports rendered PNGs via Playwright.

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。

Recreate a static graphic ad (Pinterest pin, IG/FB feed image, poster) from a reference image, swapping in a new brand's product and new copy while keeping the reference's layout, composition, and visual energy. ALWAYS generated with GPT Image 2 in edit-the-reference mode (fal-ai/gpt-image-1/edit-image, a billed FAL generation); the HTML/goose-graphics overlay is only an optional text-finishing step, never the generator. The static-graphics counterpart to the video remix-ad skill; this is what the app calls when a user picks a reference ad and wants it for their own product.

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。

Research a company or brand from its website and produce a reusable Brand Core covering products, audience, competitors, positioning, offers, messaging evidence, voice, and visual identity. Use before growth, ad, content, creator, or product work when reliable brand context is missing.

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。

Render a designed 'value prop' video from a config — 3-5 noun-phrase benefit claims (<=4 words each) revealed sequentially over per-SKU product visuals, one crisp editorial frame per claim (hook sticker -> N claim beats -> brand end card). Deterministic PIL/HTML beat renderer frame-stepped via Playwright and encoded with FFmpeg, sound-off legible, hard cuts, uniform pacing. FREE (no paid calls); music is added separately (create-music-elevenlabs). Use for the value-prop format.

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。

Write a short-form video ad from an evidence-backed angle and the selected template's actual recipe. Reuses or fetches ad-angle-miner, preserves the chosen angle, plans words and visuals together, checks product claims and production fit, and delivers the strongest script with useful alternatives. Use before video production and for script rewrites. Customer language is one input alongside audience, promise, product proof, offer and format.

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。

Render a punchy ~12s vertical (9:16) music-only direct-response OFFER ad as a 4-beat kinetic-typography film — HEADLINE slam → real PRODUCT drop → CLAIM/proof → CTA pill — from one config of copy slots, a real product photo, a brand palette, fonts, bpm, and beat split. DETERMINISTIC + FREE (a bundled Remotion project; springs + interpolate, no AI-gen for visuals). Backgrounds are engine gradient divs off the palette, props are inline SVG, the ONLY composited bitmap is the REAL product photo (objectFit:contain, never stretched), and ALL headline/claim/CTA/URL/wordmark text is typeset in the engine — never AI-rendered (the format's credibility guard). A driver binds the config to Remotion input props, renders the 9:16 master, and derives a 1:1 center-crop with ffmpeg. Two gating checks run before render (claim verbs must match the product's physical format; the claim beat needs an edge-entry mechanism prop). Use for the motion-graphics-offer-ad format.

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。

Assemble a myth-vs-fact kinetic-typography explainer video ad (≈29.5s, 9:16) from N myth/fact pairs + hook / turn / punch copy + palette + a brand end-card PNG + a VO track — a hook, 3 red-strike MYTH cards that flip to teal-check FACT cards (per-line strikethrough that crosses EVERY wrapped line), a "what actually works" turn, an optional proof reveal, a punch line, and a static end card. DETERMINISTIC assembly with ZERO AI-gen visuals — HTML hyperframes rendered frame-exact via Playwright (`window.renderAt(t)`, animation a pure function of beat-local time), Whisper beat-snap to VO word onsets, concat at a uniform fps, karaoke `.ass` captions burned last (suppressed on the proof + end-card beats), and a VO + optional music mix (music −20 dB, `amix normalize=0`, tail fade). FREE (Python + Playwright + ffmpeg); the recipe supplies the copy / palette / end-card / VO and gates the paid VO / music / Whisper calls to their own capabilities. Use for the myth-vs-fact format.

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。