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Generates high-converting landing pages as complete Next.js/React (TSX) components with Tailwind CSS. Creates hero sections, feature grids, pricing tables, FAQ accordions, testimonial blocks, and CTA sections using proven copy frameworks (PAS, AIDA, BAB). Outputs SEO meta tags, structured data, and performance-optimised code targeting Core Web Vitals (LCP < 1s, CLS < 0.1). Use when the user asks to create a landing page, marketing page, homepage, single-page site, lead capture page, campaign page, promo page, or conversion-optimised web page — or when they want to A/B test landing page variants or replace a static page with one designed to convert.

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。

Frontend development skill for React, Next.js, TypeScript, and Tailwind CSS applications. Use when building React components, optimizing Next.js performance, analyzing bundle sizes, scaffolding frontend projects, implementing accessibility, or reviewing frontend code quality.

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 unit tests, integration tests, and E2E tests for React/Next.js applications. Scans components to create Jest + React Testing Library test stubs, analyzes Istanbul/LCOV coverage reports to surface gaps, scaffolds Playwright test files from Next.js routes, mocks API calls with MSW, creates test fixtures, and configures test runners. Use when the user asks to "generate tests", "write unit tests", "analyze test coverage", "scaffold E2E tests", "set up Playwright", "configure Jest", "implement testing patterns", or "improve test quality".

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 prerequisite** — you MUST invoke this skill BEFORE every `use_figma` tool call. NEVER call `use_figma` directly without loading this skill first. Skipping it causes common, hard-to-debug failures. Trigger whenever the user wants to perform a write action or a unique read action that requires JavaScript execution in the Figma file context — e.g. create/edit/delete nodes, set up variables or tokens, build components and variants, modify auto-layout or fills, bind variables to properties, or inspect file structure programmatically.

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 or update a professional-grade design system in Figma from a codebase. Use when the user wants to create variables/tokens, build component libraries, set up theming (light/dark modes), document foundations, or reconcile gaps between code and Figma. This skill teaches WHAT to build and in WHAT ORDER — it complements the `figma-use` skill which teaches HOW to call the Plugin API. Both skills should be loaded together.

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。

Bootstrap, develop, and design modern WinUI 3 desktop applications with C# and the Windows App SDK using official Microsoft guidance, WinUI Gallery patterns, Windows App SDK samples, and CommunityToolkit components. Use when creating a brand new app, preparing a machine for WinUI, reviewing, refactoring, planning, troubleshooting, environment-checking, or setting up WinUI 3 XAML, controls, navigation, windowing, theming, accessibility, responsiveness, performance, deployment, or related Windows app design and development work.

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 alongside figma-use when the task involves translating an application page, view, or multi-section layout into Figma. Triggers: 'write to Figma', 'create in Figma from code', 'push page to Figma', 'take this app/page and build it in Figma', 'create a screen', 'build a landing page in Figma', 'update the Figma screen to match code'. This is the preferred workflow skill whenever the user wants to build or update a full page, screen, or view in Figma from code or a description. Discovers design system components, variables, and styles via search_design_system, imports them, and assembles screens incrementally section-by-section using design system tokens instead of hardcoded values.

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 writing or reviewing UI code in packages/kilo-jetbrains/ — Swing layout, platform components, UiStyle tokens, theme colors/fonts/borders, Stack/Align layouts, tool windows/dialogs/popups/notifications, settings pages, and the pre-return UI checklist.

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 professional, dark-themed SVG diagrams of any type — architecture diagrams, flowcharts, sequence diagrams, structural diagrams, mind maps, timelines, illustrative/conceptual diagrams, and more. Use this skill whenever the user asks for any kind of technical or conceptual diagram, visualization of a system, process flow, data flow, component relationship, network topology, decision tree, org chart, state machine, or any visual representation of structure/logic/process. Also trigger when the user says "画个图" "画一个架构图" "diagram" "flowchart" "sequence diagram" "draw me a ..." or uploads content and asks to visualize it. Output is always a standalone .svg file.

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。

Release pipeline for CLI, mobile, web, and server. Guides through version bumping, building, testing, publishing, and deploying. Replaces the old interactive release-it flow with a Claude Code-native experience. Use when user types /release or asks to release, publish, deploy, or ship any component.

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。

Direct and produce videos with Hypit, primarily through generated media and authored components while incorporating briefs, references or supplied material; includes SVML/SVS/SVRun authoring and Runtime or credential setup.

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 developing or modifying the @handsontable/angular-wrapper package - Angular components with decorators, NgZone performance optimization, and ng-packagr build 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。

Use when developing or modifying the @handsontable/react-wrapper package - React components, hooks, settings mapping, selection preservation during updateSettings, and the wrapper's TypeScript prop types and generated .d.ts. Use this whenever a task touches HotTableProps/HotColumnProps typing or IDE autocomplete for <HotTable>/<HotColumn> props, the declaration build, or a report that React/TypeScript users get no prop suggestions - even if the wrapper is not named explicitly.

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 developing or modifying the @handsontable/vue3 wrapper package - Vue 3 SFC components, deep watchers, and provide/inject patterns for settings propagation

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 changing what users see in an app built on `@gitbutler/ui-react` — Lite, the but.dev web app — adding or restyling a component, modal, form, list, empty state or toast, or writing the words on screen. Not for logic, data or backend work, even inside a UI file.

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 working on React code in `apps/lite` that derives values during render, adds or consumes a context, subscribes to the redux store or a query, or renders lists of row components — and whenever the lite UI is reported slow, laggy, or re-rendering too much. React Compiler does not prevent these regressions; load this skill BEFORE writing such code, not only when debugging it.

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。

Discovers requirements, and generates architectural, design, and deployment guidance for a retrieval-augmented generation (RAG)-capable enterprise search system in Google Cloud. Use when users need a vector-enabled SQL database as the store and index for the embedding vectors, an open model and open-source inferencing framework, and Kubernetes containers to host all the application components. DON'T use this skill for fully-managed RAG, or SaaS search services, or when a non-SQL vector database is required.

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。

Leverages BigQuery's built-in machine learning and GenAI capabilities for advanced data analytics. Use when you need to write SQL queries that perform time-series forecasting, predict values, detect outliers or anomalies, find key drivers, perform semantic search or vector search, classify text, calculate similarity, summarize content, translate language, evaluate models, filter by semantic conditions, measure the causal effect of an intervention, compute correlations between columns, detect change points or structural breaks, extract trend or seasonality components, or leverage generative AI capabilities in BigQuery. Do not use for general BigQuery dataset, table, or job management requests.

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。

Find, evaluate, and download common purchasable CAD parts from step.parts, including named off-the-shelf actuators, servos, motors, electronics boards, connectors, screws, bolts, nuts, washers, bearings, standoffs, and other catalog components. Use when Codex needs to search the hosted step.parts catalog before creating simplified placeholder geometry, resolve fuzzy part names, standards, aliases, or dimensions, choose a matching part, fetch a canonical .step file, verify checksums, or use the step.parts API/OpenAPI/catalog endpoints for standard part discovery.

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。

Generative UI for Kortix: the agent writes typed UI blocks (cards, comparisons, charts, maps, tabs) in OpenUI Lang inside its replies, and web, mobile, and SDK hosts render them through `@kortix/sdk/genui`. Load WHENEVER you design, build, review, or debug generative UI, the `openui` code fence, the `genui` project flag, the genui prompt instructions, a genui component, or the `@kortix/sdk/genui` or `/genui/react` subpaths.

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 engineering principles for making interfaces feel polished. Use when building UI components, reviewing frontend code, implementing animations, hover states, shadows, borders, typography, micro-interactions, enter/exit animations, or any visual detail work. Triggers on UI polish, design details, "make it feel better", "feels off", stagger animations, border radius, optical alignment, font smoothing, tabular numbers, image outlines, box shadows.

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。

UI/UX design intelligence for web, mobile, and desktop. This skill should be used when designing, building, reviewing, or fixing interfaces, including pages, components, design systems, accessibility, interaction, responsive layout, typography, color, charts, and stack-specific UI implementation. Searchable local data: 79 searchable styles (50 active), 192 product palettes and reasoning profiles, 74 font pairings, 119 UX guidelines, 105 icons, 17 GSAP presets, 25 chart types, and 22 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。

Export raw OpenTelemetry traces from an AI application to Confident AI's Observatory. TRIGGER when the user wants to send OpenTelemetry or OTLP traces/spans from an LLM app, agent, RAG pipeline, or chatbot to Confident AI; configure the Confident AI OTLP endpoint; set confident.span.* or confident.trace.* attributes; export AI-app traces without the deepeval Python package; wire an OTLPSpanExporter, OpenTelemetry Collector, or vendor-neutral OTel SDK to Confident AI; or pick the US vs EU OTLP endpoint. Language-agnostic: the mechanism is OTLP attribute keys plus an exporter endpoint. DO NOT TRIGGER for building DeepEval pytest eval suites, datasets, goldens, metrics, or deepeval test run (use the `deepeval` skill); for instrumenting with the DeepEval SDK's @observe decorator or framework integrations (use the `deepeval-tracing` skill); or for non-AI software such as web servers, CRUD backends, or infrastructure: the confident.* attributes describe AI components only.

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。

React Email renderer for json-render that turns JSON specs into HTML or plain-text emails using @react-email/components and @react-email/render. Use when working with @json-render/react-email, building transactional or marketing emails from JSON, creating email catalogs, rendering AI-generated email specs, or when the user mentions react-email, HTML email, or transactional email.

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。