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Submission-grade remote-sensing Nature/high-impact journal figure workflow for Python or R. Use whenever the user asks to create, revise, audit, or polish manuscript figures, multi-panel scientific plots, map-led composites, remote-sensing image plates, sensor/product workflow figures, validation/error/uncertainty panels, or journal-ready SVG/PDF/TIFF outputs, especially for Nature-family, Remote Sensing of Environment, ISPRS, IEEE TGRS, or other high-impact venues. Before plotting, define the figure's conclusion, evidence logic, export needs, and review risks. If the user has not chosen Python or R, ask "Python or R?" and stop. Use only the selected backend for figure generation, previewing, exporting, and QA. Supports matplotlib/seaborn and ggplot2/patchwork/ComplexHeatmap. Not for dashboards or Illustrator/Figma-first infographics. Also trigger on general academic-writing figure needs even without the word "Nature", such as making figures/plots for a paper, scientific/academic plotting, data visualization for a manuscript, and Chinese phrasings like 遥感论文配图、学术写作配图、科研绘图、科研作图、地图出图、论文图表、可视化.

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。

Toolkit for interacting with and testing local web applications using Playwright. Supports verifying frontend functionality, debugging UI behavior, capturing browser screenshots, and viewing browser logs.

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。

Gmail helper for sending email via the gws CLI. Handles RFC 2822 formatting and base64 encoding automatically. Supports plain text, HTML, CC, and BCC.

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。

Given the product and the existing page inventory, identify missing blog topics and generate new blog posts that capture search/AI intent using the query-fanout pattern. Use this skill whenever the user asks to "spawn blogs", "generate blog posts", "expand the blog", "build out a content cluster", "write blog ideas", "cover more search intents", "query fanout for blogs", or wants new long-form content that supports existing pillars. Output is one new post per topic in the project's existing post format (markdown, MDX, component, or CMS migration note), with frontmatter shape mirrored from an existing post.

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 production-grade Appium mobile automation scripts for Android and iOS in Java, Python, or JavaScript. Supports real device and emulator testing locally and on TestMu AI cloud with 100+ real

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 project-specific AGENTS.md and companion rules by analyzing a codebase. Supports full, minimal, update, and dry-run modes with package-manager detection, monorepos, backups, managed blocks, c

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-driven Flutter E2E testing via VM Service CLI. Control running Flutter apps in debug mode for smoke tests, regression checks, exploratory testing, and UI automation. Trigger when user mentions E2E testing, UI automation, smoke tests, integration testing, test automation, controlling Flutter apps, VM Service interaction, debugging UI flows, automated regression testing, CI sanity checks, or testing native dialogs/permissions/WebViews. Supports stateful named instances or stateless direct URI connections. Ideal for rapid test feedback, AI agent workflows, element discovery, screenshot capture, log collection, and scriptable UI interactions (tap, scroll, enterText). Use for troubleshooting flaky tests, automating repetitive manual tests, verifying app behavior after merges, or setting up automated QA pipelines.

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 (GenUI) for Flutter using AI models like Google Gemini, Anthropic Claude, or OpenAI via the genui package. Use this skill when building dynamic AI-driven interfaces, conversational UI flows, server-rendered screen layouts, LLM-powered dashboards, chat-based UI generation, or implementing prompt-to-UI systems. Supports AI-generated widgets, reactive data binding, ContentGenerator integration, and A2uiMessageProcessor for real-time UI updates from AI responses. Ideal for apps where UI structure is determined by AI/LLM output rather than hardcoded layouts.

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。

SharedPreferences (v2.5.5) for Flutter providing simple persistent key-value storage for non-sensitive user settings and app preferences. Use this skill when storing user preferences (theme mode, language selection, font size), app settings and configuration, feature flags or A/B test assignments, simple cache data, onboarding completion status, last selected values (filters, sort options), user consent flags, or any non-sensitive data that survives app restarts. Supports String, int, double, bool, and StringList types. NOT suitable for storing tokens, passwords, API keys, or any sensitive/encrypted data (use flutter_secure_storage instead). Ideal for quick persistent storage without database overhead.

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。

FlutterSecureStorage (v10.x) for encrypted key-value storage of sensitive data using platform-native secure enclaves with biometric authentication support. Use this skill when storing OAuth tokens (access tokens, refresh tokens, ID tokens), API keys and secrets, user passwords or PINs, session tokens, encryption keys, private keys for cryptography, certificate data, biometric enrollment data, banking credentials, health data, or any sensitive information requiring platform-level encryption (iOS Keychain with Secure Enclave, Android Keystore with Hardware-backed keys). Supports Touch ID, Face ID, and Android biometric prompt integration. Handles biometric change invalidation (resetOnError), data migration from SharedPreferences/EncryptedSharedPreferences, Google Drive backup exclusion, automatic key rotation, configurable encryption algorithms (AES), and prevents data extraction even on rooted/jailbroken devices. Essential for authentication flows, secure credential management, or compliance requirements (PCI DSS, HIPAA, GDPR data protection).

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。

Hive CE (Community Edition v2.19.x) NoSQL object database for Flutter providing blazing-fast key-value and object storage with TypeAdapters. Use this skill when implementing offline-first architecture, high-performance local data caching, NoSQL document-style object stores, custom TypeAdapter serialization for complex objects, lazy loading boxes for memory efficiency, encrypted boxes (HiveAES encryption), database compaction for size optimization, storing large datasets without SQL schema overhead, implementing local-first sync patterns, or migrating from SQLite to NoSQL. Supports primitive types, custom objects, lists, and maps. Ideal for apps requiring ultra-fast read/write operations (microsecond latency), object persistence without ORM complexity, or local-first data architecture.

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。

Integrates Google Mobile Ads (AdMob) into Flutter using google_mobile_ads v7.x with Banner, Interstitial, Rewarded, and Native ad formats. Use this skill whenever implementing app monetization, adding AdMob ads to Flutter, fixing ad loading or display errors, managing ad lifecycle (preload/dispose), preventing memory leaks from undisposed ads, handling GDPR/ATT consent flows, troubleshooting AdMob dashboard integration, or any time the words 'AdMob', 'google_mobile_ads', 'banner ad', 'interstitial', 'rewarded ad', 'native ad', or 'ad monetization' appear in the request. Supports Android (GMA SDK 24.9+) and iOS (GMA SDK 12.14+).

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。

Declarative animation patterns using flutter_animate v4.5.x with composable effects (fade, scale, slide, shimmer, shake, blur), GLSL fragment shaders, and scroll-synchronized animations. Use this skill for adding UI animations and transitions, building staggered or sequenced effects, creating custom animation effects, fixing animation jank or performance issues, implementing shimmer loading states, or adding scroll-driven animations. Supports delays, curves, adaptive effects, and easy extension method chaining (.animate()).

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。

Implements data visualization charts in Flutter using the fl_chart package (v1.2.0). Supports LineChart, BarChart, PieChart, ScatterChart, RadarChart, and CandlestickChart with implicit animations, touch interactivity, and pan/zoom transformations. Use when building any Flutter chart, graph, or data visualization — including line graphs for trends, bar charts for comparisons, pie/donut charts for proportions, scatter plots for distributions, radar charts for multi-axis comparisons, or candlestick charts for financial OHLC data. Also use when customizing chart axes, titles, tooltips, grid lines, touch callbacks, chart animations, or chart zoom/pan behavior.

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。

Assign transactions to tax-aligned expense categories using vendor name, line items, and amount patterns. Handles ambiguous vendors like Amazon by inspecting line-item detail from the receipt source. Supports split categorization and learns from user corrections. Trigger on "categorize expenses", "sort transactions", "assign expense categories", "what category is this".

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。

Extract structured data from receipts and invoices for bookkeeping, expense tracking, and tax preparation. Supports email, photos, scanned images, PDFs, and accounting exports. Outputs vendor, date, amount, tax, line items as table, CSV, or JSON. Trigger on "process receipts", "extract receipts from email", "scan invoices", "capture expenses".

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。

TidyFactor Design track — code-native UI design lifecycle engine (Figma alternative). Supports all 7 stages of UI design (Discovery, Foundation, Architecture, Components, Motion, Quality, Delivery) with zero per-page CSS/JS and zero build step. Pluggable CSS foundations (Native, Tailwind utility-first, daisyUI, Hybrid, shadcn, Pico, Bootstrap, Alpine) and full Arabic/RTL support (El Messiri/Tajawal, never Amiri above 24px). Trigger on commands "study", "brief", "init", "brand", "typography", "school", "tokens", "palette", "assets", "layout", "nav-footer", "components", "page", "dashboard", "motion", "states", "flow", "i18n", "perf", "audit", "clone", "retrofit", "handoff", "deploy", or requests like "build a design system", "UI design lifecycle", "prototype this website", "Figma alternative", "نموذج أولي تفاعلي", "صمّم لوحة تحكم".

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。

Code-native UI design lifecycle engine (Figma alternative) with Contextual Decision Layer (CDL). Supports all 7 design stages with zero per-page CSS/JS and pluggable CSS foundations (Native, Tailwind, daisyUI, Pico, Hybrid). Trigger on commands 'brief', 'study', 'init', 'brand', 'tokens', 'palette', 'layout', 'components', 'page', 'dashboard', 'motion', 'i18n', 'audit', 'deploy', or design system 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。

Search PubMed/PMC medical literature via MCP server. Use for literature search, retrieving abstracts, full-text articles, and citation analysis. Supports MeSH terms, batch fetching, PDF downloads, and RIS export.

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 with Azure Cdn — azure CDN management covering profiles, endpoints, custom domains, caching rules, origin groups, and performance analytics. Supports Microsoft, Verizon, and Akamai CDN providers within Azure. Use when managing Azure CDN endpoints, analyzing cache performance, configuring delivery rules, or troubleshooting content delivery.

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。

F16 on BitNet-embedding-270M prefill (8 threads) Supports I2_S conversion with optimized kernels on x86 CPUs Lossless inference with 2 bits per weight

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。

Infer gene regulatory networks (GRNs) from gene expression data using scalable algorithms (GRNBoost2, GENIE3). Use when analyzing transcriptomics data (bulk RNA-seq, single-cell RNA-seq) to identify transcription factor-target gene relationships and regulatory interactions. Supports distributed computation for large-scale datasets.

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 Transformers.js to run state-of-the-art machine learning models directly in JavaScript/TypeScript. Supports NLP (text classification, translation, summarization), computer vision (image classification, object detection), audio (speech recognition, audio classification), and multimodal tasks. Works in browsers and server-side runtimes (Node.js, Bun, Deno) with WebGPU/WASM using pre-trained models from Hugging Face Hub.

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。

Train and fine-tune transformer language models using TRL (Transformers Reinforcement Learning). Supports SFT, DPO, GRPO, KTO, RLOO and Reward Model training via CLI commands.

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。