847 件 ・ 関連度順
概要と使いどころ
Use when one reviewer angle is insufficient or the user suspects a direction is tunnel-visioned or inherited its framing. Modes: artifact, direction. Not for source or remote-system changes.
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。
Design and visually validate implementation-agnostic website pages or modules from an approved Design System and confirmed content. Use for Module Design Solutions, Page UI Architecture Maps, media, evidence, page motion budgets, section motion intent, visual-direction images, and direction review before HTML. Do not use to write or review HTML/CSS/JS, run browser implementation QA, plan platform fields, or implement CMS components.
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 3–5 confirmed same-industry or cross-industry websites and translate their brand narrative, page architecture, art direction, and visual-system evidence into one reusable Visual Reference Brief. Use when strong references are scarce, a site needs brand-led or premium positioning, a page lacks a narrative spine, or a key module lacks a credible design precedent. Do not create page content, a Design System, UI directions, HTML, or a permanent brand gallery.
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, review, and browser-test interactive website page or module HTML from a confirmed UI Architecture Map and visual direction. Use for HTML First Drafts, restrained motion implementation, full-page or module-first prototypes, rendered design review, responsive and interaction QA, page composition, and implementation handoff. Do not use for design exploration, unconfirmed visual or motion direction, content strategy, Design Systems, Elementor controls, CMS templates, or framework components.
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。
コンペ戦略の横断synthesis を実行する。competition-strategist agent を起動し、daily_reports/* と全 SESSION_NOTES.md / claudeSummary.md / submit/SUBMISSIONS.md / KAGGLE_DIRECTION.md を一括ロードして「次の一手」を出力する。週1回や局面の変わり目で使う。
Set an ECC-specific frontend design direction for production UI work. Use when building or improving websites, dashboards, applications, components, landing pages, visual tools, or any web UI that needs stronger product-specific design judgment.
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。
Develop an independent visual language and turn the human-chosen direction into a production-ready graphic, brand, campaign, publication, web, or product system. Use when originality and differentiation matter enough to escape generic AI and Big Tech aesthetics. Guides a minimum-evidence start, ten isolated typography-led HTML design directions made only from type, color, and space, visual-first human judgement, explicit commitment, and concrete Round 2 production. Do not use for routine UI fixes, small components, or applying an existing design system.
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 a team of 50 LLM personas through a 30-round structured debate to predict market direction. Uses the Canvas Agent Swarm Service — the same shared infrastructure other skills use for smaller agent teams. Output is an influence-weighted consensus direction + trade recommendation with a transparent record of every argument, research query, and cross-examination.
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 CRISPR drug-modifier (chemogenomic) screens with drugZ (Colic et al. 2019 Genome Med), a bidirectional Z-score method that identifies synthetic-lethal sensitizing genes and resistance-conferring suppressor genes from vehicle vs drug comparisons. Covers vehicle-anchored design (not Day-0), the bidirectional Z math giving greater sensitivity to small-effect hits than MAGeCK / STARS / edgeR / RIGER on drug screens, per-gene sumZ and normZ, synth (sensitizer) vs supp (suppressor) FDR, multi-dose handling, integration with control sgRNAs, and comparison with MAGeCK MLE with dose covariate. Use when running a drug-modifier CRISPR screen, identifying sensitizing or resistance genes for a drug candidate, choosing drugZ vs MAGeCK MLE for chemogenomic analysis, troubleshooting low-effect drug screens where MAGeCK lacks sensitivity, or designing a drug-screen layout (vehicle vs drug arms).
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。
Define a brand identity constraint set from a confirmed strategy — colour story, type story, logo direction, imagery direction. Defines the tokens that token emission and asset generation consume.
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。
Critically analyze whether a product, repo, skill, plugin, harness, or architecture vision still fits the real application, user intent, implementation, evidence, and likely future direction. Use when asked to assess vision vs application, predict future fit, critique roadmap direction, analyze strategic intent, or decide whether to keep, refine, pivot, or kill a 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。
Set up and manage a UI redesign experiment with three git worktree branches for modern minimal, bold creative, and classic refined design directions. Use when asked to run a UI redesign experiment, compare design approaches, or prototype multiple visual directions in parallel branches.
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。
プロジェクトのデザインシステムを SSOT として作成・監査・更新する。トークン、デザインシステムに関わるアクセシビリティ、Typography、Motion、aesthetic direction の作業に使う。デザインシステムに紐づかない単発UI実装や個別デザインレビューには使わない。ユーザーが /ai-design-system と入力したら使う。
Use when a website needs a strong visual point of view, distinct concept routes, composition, typography, color/materiality, imagery direction, spatial rhythm, or a memorable signature idea.
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。
Translate creative intent into coherent cinematography, framing, camera, lens, lighting, motion, and sound direction for a video shot.
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 the confirmed design direction to all visual surfaces. This skill now bridges the pipeline's placeholder system with the `impeccable` skill's PRODUCT.md and DESIGN.md files. If `{{DESIGN_DIRECTION}}` still appears as a literal placeholder, run `/impeccable teach` to establish design context.
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。
SwiftUI ↔ Figma translation. Use whenever the user mentions Swift, SwiftUI, iOS, iPhone, or iPad — in EITHER direction — translating a Figma design into SwiftUI (design → code), or pushing SwiftUI views / screens / tokens back into a Figma file (code → design). Triggers on phrases like 'implement this Figma design in SwiftUI', 'build this screen in Swift', 'push this SwiftUI view to Figma', 'mirror my Swift code in a Figma file', or whenever a Figma URL appears alongside `.swift` files / an `.xcodeproj`. Routes to a direction-specific reference doc; loads alongside `figma-use` for the code → design path.
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。
Implement, review, or improve maps and location features in iOS/macOS apps using MapKit and CoreLocation. Use when working with Map views, annotations, markers, polylines, user location tracking, geocoding, reverse geocoding, search/autocomplete, directions and routes, geofencing, region monitoring, CLLocationUpdate async streams, or location authorization flows. Also use when working with maps, coordinates, addresses, places, directions, distance calculations, or location-based features in Swift apps.
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。
Activate for any ticket that builds new UI, reshapes an existing interface, or requires visual direction. Guidance for distinctive, intentional visual design, aesthetic direction, typography, and layout choices. Do not apply when the project has an established design system and the ticket is within its constraints.
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。
Mendelian randomization (MR) causal inference — does an exposure, risk factor, or biomarker CAUSALLY affect a disease/outcome, using genetic variants as instrumental variables (IEU OpenGWAS / EpiGraphDB MR-EvE). Use this whenever the user asks if X causes Y, whether an observational association is actually causal or just correlation, if a biomarker/trait is a causal risk factor, wants to triangulate epidemiology against genetic evidence, or mentions Mendelian randomization, instrumental-variable analysis, two-sample MR, or genetic causal evidence — even if they never say "MR" (e.g. "is LDL cholesterol actually causal for heart disease?", "does BMI cause type 2 diabetes or just correlate?", "is CRP a causal driver of stroke?"). Covers trait-label resolution, MR effect direction/magnitude, instrument quality (MOE score), method agreement (IVW vs MR-Egger vs weighted median), bidirectional MR for reverse causation, and distinguishing causation from genetic correlation. Not for plain GWAS association lookups (use the GWAS skills) or fitting your own instruments from raw summary statistics.
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。
Three.js (WebGL / WebGPU ベース 3D グラフィクスライブラリ) リファレンス。 Scene, PerspectiveCamera, OrthographicCamera, WebGLRenderer, WebGPURenderer、 BufferGeometry, BufferAttribute, Mesh, Group, InstancedMesh, SkinnedMesh、 MeshStandardMaterial, MeshPhysicalMaterial, ShaderMaterial, node-materials (TSL)、 DirectionalLight, PointLight, SpotLight, AmbientLight、 TextureLoader, GLTFLoader, DRACOLoader, FBXLoader、 AnimationMixer, AnimationClip, AnimationAction, KeyframeTrack、 Vector3, Matrix4, Quaternion, Euler, Color, Raycaster, Box3、 OrbitControls, TransformControls, WebXRManager。
Analyzes CRISPR drug-modifier (chemogenomic) screens with drugZ (Li & Hart 2019 Genome Med), a bidirectional Z-score method that identifies synthetic-lethal sensitizing genes and resistance-conferring suppressor genes from vehicle vs drug comparisons. Covers vehicle-anchored design (not Day-0), the bidirectional Z math giving 2-3x sensitivity over MAGeCK / STARS / edgeR / RIGER on drug screens, per-gene sumZ and normZ, synth (sensitizer) vs supp (suppressor) FDR, multi-dose handling, integration with control sgRNAs, and comparison with MAGeCK MLE with dose covariate. Use when running a drug-modifier CRISPR screen, identifying sensitizing or resistance genes for a drug candidate, choosing drugZ vs MAGeCK MLE for chemogenomic analysis, troubleshooting low-effect drug screens where MAGeCK lacks sensitivity, or designing a drug-screen layout (vehicle vs drug arms).
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。
Pick a product archetype from a curated catalog before any UI gets built — the single biggest lever for not looking vibecoded. Routes the request to one of B2B-productivity, enterprise-trust, premium-consumer, playful-consumer, editorial, brutalist-distinctive, dev-tool, or marketing-landing, and returns a complete design philosophy: typography pair, color logic, motion curve, density, icon stance, reference sites, and a "feels like X" claim. Load when the user asks to pick an aesthetic, choose a design direction, decide what a UI should feel like, classify a product visually, says "what should this look like", "make this feel like [Linear/Stripe/Apple/Duolingo/...]", "give me a design direction", "what's the right aesthetic for this", or when frontend-design routes here during archetype selection. Sub-skill of frontend-design.
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 chosen design direction into a canonical DESIGN.md plus production tokens that don't look vibecoded — state-level colors (8-step neutral ramp, rest/hover/active/ disabled, focus ring), APCA-checked contrast, typography, spacing/radius/motion/elevation, an icon strategy, and component contracts for the core atoms. Emits tokens in the project's stack format (shadcn HSL vars or Tailwind v4 @theme). Load when the user asks to build a design system, generate design tokens, create a DESIGN.md, set up a theme, design a color or type system, pick icons, or when frontend-design routes here. Replaces design-tokens-craft and icon-craft. Sub-skill of frontend-design. Reads DIRECTION.md first.
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。