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概要と使いどころ
Helps an AI assistant work with the Miraheze wiki farm — writing wiki requests that get approved, navigating ManageWiki, doing common how-to tasks (templates, skins, permissions, custom domains, backups), writing regex for MediaWiki search-and-replace, AND writing actual article and page content for Miraheze-hosted wikis. Miraheze does NOT ban generative AI; AI-written content is permitted (subject to per-wiki rules). MADE BY SQERSTERS
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。
Forces the laziest solution that actually works, simplest, shortest, most minimal. Channels a senior dev who has seen everything: question whether the task needs to exist at all (YAGNI), reach for the standard library before custom code, native platform features before dependencies, one line before fifty. Supports intensity levels: lite, full (default), ultra. Use on ANY coding task: writing, adding, refactoring, fixing, reviewing, or designing code, and choosing libraries or dependencies. Also use whenever the user says "ponytail", "be lazy", "lazy mode", "simplest solution", "minimal solution", "yagni", "do less", or "shortest path", or complains about over-engineering, bloat, boilerplate, or unnecessary dependencies. Do NOT use for non-coding requests (general knowledge, prose, translation, summaries, recipes).
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。
Solves UI lock-in. Reverse engineers your existing UI, strips all layout/token decisions, preserves every hook, handler, and API call untouched, then rebuilds the UI from scratch with a fresh design system. Use /ui-restructure to fully redesign, --style apple|linear|minimal|dashboard for named styles, --mode layout|theme|grid for partial rebuilds, --god-mode to redesign from the user's perspective using 100-user mindset simulation and 6 UX principles, --keep-tokens to preserve your token system, --prompt for custom redesigns. Applies mandatory world-class polish: spring motion, 5-state micro-interactions, 8pt spacing grid, WCAG accessibility. Supports Next.js, React, Vue 3, Tailwind, CSS Modules, styled-components, shadcn.
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 adding, customizing, or troubleshooting shadcn/ui components to build modern, accessible design systems.
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。
General-purpose browser automation skill. Use when a task needs custom Playwright code — end-to-end testing, scraping a JS-rendered page, or driving a browser interactively — executed via this skill's universal executor rather than a one-off script.
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。
AI agent development workflow for building autonomous agents, multi-agent systems, and agent orchestration with CrewAI, LangGraph, and custom agents.
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 modern Swift language patterns and idioms for non-concurrency, non-SwiftUI code. Covers if/switch expressions (Swift 5.9+), typed throws (Swift 6+), result builders, property wrappers, opaque and existential types (some vs any), guard patterns, Never type, Regex builders (Swift 5.7+), Codable best practices (CodingKeys, custom decoding, nested containers), modern collection APIs (count(where:), contains(where:), replacing()), FormatStyle (.formatted() on dates, numbers, measurements), and string interpolation patterns. Use when writing core Swift code involving generics, protocols, enums, closures, or modern language 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。
Implement SwiftUI navigation patterns including NavigationStack, NavigationSplitView, sheet presentation, tab-based navigation, and deep linking. Use when building push navigation, programmatic routing, multi-column layouts, modal sheets, tab bars, universal links, or custom URL scheme handling.
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 SwiftUI views with modern MV architecture, state management, and view composition patterns. Covers @Observable ownership rules, @State/@Bindable/@Environment wiring, view decomposition, custom ViewModifiers, environment values, async data loading with .task, iOS 26+ APIs, Writing Tools, and performance guidelines. Use when structuring a SwiftUI app, managing state with @Observable, composing view hierarchies, or applying SwiftUI best practices.
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。
Embeds and controls web content in SwiftUI with WebKit for SwiftUI, including WebView, WebPage, navigation policies, JavaScript execution, observable page state, link interception, local HTML or data loading, and custom URL schemes. Use when building iOS 26+ article/detail views, help centers, in-app documentation, or other embedded web experiences backed by HTML, CSS, and JavaScript.
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 Swift Codable models for JSON and property-list encoding and decoding with JSONDecoder, JSONEncoder, CodingKeys, and custom init(from:) or encode(to:). Use when parsing API responses, remapping keys, flattening nested JSON, handling date or data decoding strategies, decoding heterogeneous arrays, or integrating Codable with URLSession, SwiftData, or UserDefaults.
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 SwiftUI gesture handling. Use when adding tap, long press, drag, magnify, or rotate gestures, composing gestures with simultaneously/sequenced/exclusively, managing transient state with @GestureState, resolving parent/child gesture conflicts with highPriorityGesture or simultaneousGesture, building custom Gesture protocol conformances, or migrating from deprecated MagnificationGesture to MagnifyGesture or using the newer RotateGesture.
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 data visualizations using Swift Charts. Use when building bar, line, area, point, pie, or donut charts; when adding chart selection, scrolling, or annotations; when plotting functions with vectorized BarPlot, LinePlot, AreaPlot, or PointPlot; when customizing axes, scales, legends, or foregroundStyle grouping; or when creating specialized visualizations like heat maps, Gantt charts, stacked/grouped bars, sparklines, or threshold lines.
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。
When the user wants to A/B test App Store product page elements to improve conversion rate. Also use when the user mentions "A/B test", "product page optimization", "test my screenshots", "test my icon", "conversion rate optimization", "CPP", or "custom product pages". For screenshot design, see screenshot-optimization. For metadata optimization, see metadata-optimization.
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 accessibility in iOS/macOS apps with SwiftUI and UIKit. Use when adding VoiceOver support with accessibility labels, hints, values, and traits; when grouping or reordering accessibility elements; when managing focus with @AccessibilityFocusState; when supporting Dynamic Type with @ScaledMetric; when building custom rotors or accessibility actions; when auditing a11y compliance; or when adapting UI for assistive technologies and system accessibility preferences.
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。
Collect and analyze on-device performance metrics and crash diagnostics using MetricKit. Use when setting up MXMetricManager, handling MXMetricPayload or MXDiagnosticPayload, processing crash/hang/disk-write diagnostics via MXCallStackTree, adding custom signpost metrics, or uploading telemetry to an analytics backend.
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。
Scaffold, build, and package SwiftPM-based macOS apps without an Xcode project. Use when you need a from-scratch macOS app layout, SwiftPM targets/resources, a custom .app bundle assembly script, or signing/notarization/appcast steps outside Xcode.
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。
Best practices and example-driven guidance for building SwiftUI views and components, including navigation hierarchies, custom view modifiers, and responsive layouts with stacks and grids. Use when creating or refactoring SwiftUI UI, designing tab architecture with TabView, composing screens with VStack/HStack, managing @State or @Binding, building declarative iOS interfaces, or needing component-specific patterns and examples.
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。
Send a push notification to Moshi app with custom title and message
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。
Discuss a community-edition product requirement with the user, draft a bilingual (Chinese + English) PRD for review, then — after explicit user approval — create the English-only issue on GitHub (<OWNER/REPO>). Use whenever the user wants to turn a requirement (from a Feishu doc, a meeting note, a customer ask, or a rough idea) into a GitHub issue. Triggers on phrases like "create requirement doc", "create a requirement", "put this requirement on GitHub", "create new feature issue", "draft an issue for...", or anytime the user describes a product feature/improvement they want tracked. The format is opinionated (Background / Goal / Spec / Verification / Related, NO implementation details, draft shown bilingually for human review, issue itself written in English 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。
MUST USE whenever the user wants to automate a real desktop or sandbox - clicking, typing, scrolling, screenshotting, running an OS shell command, or handing a high-level 'open browser and do X' task to an autonomous computer-use agent. Wraps the trycua/cua Python toolkit via its `cua` CLI - pynput-based, cross-platform (macOS / Linux / Windows). NO custom tools are registered; you call `cua` through pi's built-in bash and read screenshots back through the Read tool. Triggers: cua, computer use, computer-use, GUI automation, screenshot the desktop, click on the screen, type into the active app, scroll the page, control my computer, drive my browser, sandbox, docker sandbox, QEMU sandbox, Lume sandbox, ComputerAgent, cua do, cua sandbox, 컴퓨터 자동화, 스크린샷 찍어, 내 컴퓨터 조작, 브라우저 열어서, 샌드박스, 화면 자동화, 마우스로 클릭, 키보드 타이핑, computer use 위임, 자동으로 클릭, 자율 에이전트로 처리.
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。
Guide for using renderers — the bridge between chat-style messages and token sequences. Covers renderer setup, TrainOnWhat, vision inputs, model family matching, and custom renderers. Use when the user asks about renderers, tokenization, message formatting, or vision inputs.
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。
Guide for evaluation — inline evaluators, Inspect AI integration, and custom evaluators for measuring training progress. Use when the user asks about evaluation, metrics, benchmarks, or how to measure model quality during training.
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 run reinforcement learning with verifiable rewards (RLVR/GRPO) for math, code, or custom environments using the Tinker API. Use when the user wants to do RL training, GRPO, reward-based optimization, or train with verifiable rewards.
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。