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This skill should be used when working on Lean 4 formalization projects to maintain persistent memory of successful proof patterns, failed approaches, project conventions, and user preferences across sessions using MCP memory server integration
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。
Configure Vite with vite-plugin-singlefile for mandatory single-file HTML bundling of MCP Apps. All assets (JS, CSS, images, fonts) must be inlined into a single HTML file for sandboxed iframe compatibility.
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。
Master C#/.NET backend development patterns for building robust APIs, MCP servers, and enterprise applications. Covers async/await, dependency injection, Entity Framework Core, Dapper, configuration, caching, and testing with xUnit. Use when developing .NET backends, reviewing C# code, or designing API architectures.
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。
Interactive assistant for creating new Claude commands with proper structure, patterns, and MCP tool integration
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。
Triage academic email and calendar (Gmail / Google Calendar via the session's MCP) into a prioritized digest plus a referee-obligations tracker — classifying referee requests, R&R and editor correspondence, co-author threads, seminar and conference invites, and grant/admin deadlines, and proposing a human-gated action for each (draft reply, calendar hold, scaffold a project, snooze). Use when user says "triage my inbox", "check my academic email", "what needs my attention this week", "go through my mail", or when run as a scheduled routine. NEVER auto-sends or auto-accepts anything.
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。
Unified Azure OpenAI model deployment skill with intelligent intent-based routing. Handles quick preset deployments, fully customized deployments (version/SKU/capacity/RAI policy), and capacity discovery across regions and projects. USE FOR: deploy model, deploy gpt, create deployment, model deployment, deploy openai model, set up model, provision model, find capacity, check model availability, where can I deploy, best region for model, capacity analysis. DO NOT USE FOR: listing existing deployments (use foundry_models_deployments_list MCP tool), deleting deployments, agent creation (use agent/create), project creation (use project/create).
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。
Azure VM/VMSS router. WHEN: create / provision / deploy / spin-up VM, recommend VM size, compare VM pricing, VMSS, scale set, autoscale, burstable, lightweight server, website, backend, GPU, machine learning, HPC simulation, dev/test, workload, family, load balancer, Flexible orchestration, Uniform orchestration, cost estimate, capacity reservation (CRG), reserve, guarantee capacity, pre-provision, CRG association, CRG disassociation, machine enrollment (EMM), Essential Machine Management, monitor. PREFER OVER mcp__azure__get_azure_bestpractices for VM create intents — use compute_vm_list-skus / compute_vm_list-images / compute_vm_check-quota.
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 current news for a topic, company, competitor, or hook and return dated, attributed articles. Uses the newsjack CLI and Medialyst REST API when available, tries direct Medialyst MCP if the CLI is missing, and falls back to host web/browser search with explicit caveats only when neither cloud path is available.
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 Design Thinking artifacts to a FigJam board or Figma Design file through the Figma MCP server. Use when a team wants collaborative visual review of Method 1, 3, 4, 5, or 6 artifacts, or accepts a DT Coach board-export offer.
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。
Diagnoses macOS system-level slowness, heat and fleet-wide timeouts: high load average, leaked per-session child processes (MCP servers, helpers), fork storms, busy loops. Use when the Mac feels slow or hot, fans spin up, or everything times out at once (电脑发烫/卡顿/负载高/全部超时). Not for single-app bugs, network slowness (use tunnel-doctor), launchd watchdog design (use macos-watchdog), or disk-full (use macos-cleaner).
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。
Pulls Bigdata.com (RavenPack) financial and news data via the official bigdata-client SDK and /v1/* REST endpoints — structured financials, prices, analyst estimates, entity-sentiment series, annotated chunk search, screener. Use when the Bigdata MCP's tearsheets/search feel thin and you need the machine-readable substrate: mentions of Bigdata.com, RavenPack, a bd_v2_ key, rp_entity_id, or chunk/query_unit cost.
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 skill's recommendations into real, executed actions — open the tickets, file the issues, post the updates — safely: dry-run preview, risk-classified, approval-gated, then recorded back to the brain. Use when asked to act on a plan, file tickets from a checklist, create issues from a PRD, execute the recommended next steps, or wire a skill's output into GitHub/Linear/Slack. Produces a dry-run actions plan with per-action risk, executes only after approval via the connected action MCP, and logs what was done. Nothing acts silently.
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。
Trains in China through the 12306 MCP server: routes, dates, options, connections and stops with no screen; booking only on the user's word, in the 12306 app on their phone, stopping before payment. Use when the user asks about 火车票 / 高铁 / 12306 or a train between two cities.
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。
Where a parcel is, when it will arrive and what a shipment would cost, through the 快递100 MCP server: any Chinese courier (顺丰, 京东, 中通, 圆通, 韵达, 邮政 and more) from a tracking number. Use when the user asks 快递到哪了, 什么时候到, 查一下单号, or how much it costs to send something.
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。
Places, routes, travel times, distances and weather in China through the 高德地图 (Amap) MCP server. Use when the user asks how far, how long, how to get somewhere (car, transit, bike, foot), what is near a place, or about the weather in a Chinese city.
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。
Intelligent media optimization with automated workflows for images, videos, and SVGs. Includes decision trees that detect LCP images (triggers format/lazy-loading/priority analysis), identify layout shift risks (missing dimensions), and flag lazy loading issues (above-fold lazy or below-fold eager). Features workflows for complete media audit, LCP image investigation, video performance (poster optimization), and SVG embedded bitmap detection. Cross-skill integration with Core Web Vitals (LCP/CLS impact) and Loading (priority hints, resource preloading). Provides performance budgets and format recommendations based on content type. Use when the user asks about image optimization, LCP is an image/video, layout shifts from media, or media loading strategy. Compatible with Chrome DevTools MCP.
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。
Intelligent interaction performance analysis with automated workflows for INP debugging, scroll jank investigation, and main thread blocking. Includes decision trees that automatically run script attribution when long frames detected, break down input latency phases, and correlate layout shifts with interactions. Features workflows for complete interaction audit, third-party script impact analysis, and animation performance debugging. Cross-skill integration with Core Web Vitals (INP/CLS correlation) and Loading (script execution analysis). Use when the user asks about slow interactions, janky scrolling, unresponsive pages, or INP optimization. Compatible with Chrome DevTools MCP.
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。
Intelligent Core Web Vitals analysis with automated workflows and decision trees. Measures LCP, CLS, INP with guided debugging that automatically determines follow-up analysis based on results. Includes workflows for LCP deep dive (5 phases), CLS investigation (loading vs interaction), INP debugging (latency breakdown + attribution), and cross-skill integration with loading, interaction, and media skills. Use when the user asks about Core Web Vitals, LCP optimization, layout shifts, or interaction responsiveness. Compatible with Chrome DevTools MCP.
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。
Intelligent network quality analysis with adaptive loading strategies. Detects connection type (2g/3g/4g), bandwidth, RTT, and save-data mode, then automatically triggers appropriate optimization workflows. Includes decision trees that recommend image compression for slow connections, critical CSS inlining for high RTT, and save-data optimizations (disable autoplay, reduce quality). Features connection-aware performance budgets (500KB for 2g, 1.5MB for 3g, 3MB for 4g+) and adaptive loading implementation guides. Cross-skill integration with Loading (TTFB impact), Media (responsive images), and Core Web Vitals (connection impact on LCP/INP). Use when the user asks about slow connections, mobile optimization, save-data support, or adaptive loading strategies. Compatible with Chrome DevTools MCP.
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。
Intelligent loading performance analysis with automated workflows for TTFB investigation (DNS/connection/server breakdown), render-blocking detection, script performance deep dive (first vs third-party attribution), font optimization, and resource hints validation. Includes decision trees that automatically analyze TTFB sub-parts when slow, detect script loading anti-patterns (async/defer/preload conflicts), identify render-blocking resources, and validate resource hints usage. Features workflows for complete loading audit (6 phases), backend performance investigation, and priority optimization. Cross-skill integration with Core Web Vitals (LCP resource loading), Interaction (script execution blocking), and Media (lazy loading strategy). Use when the user asks about TTFB, FCP, render-blocking, slow loading, font performance, script optimization, or resource hints. Compatible with Chrome DevTools MCP.
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 icons in any project. Provides CLI for searching 200+ icon libraries (Iconify) and retrieving SVGs. Commands: `better-icons search <query>` to find icons, `better-icons get <id>` to get SVG. Also available as MCP server for AI 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。
Use Lodestone (lodestone.studiocorsair.com) as one MCP connector for Meta Ads, Instagram insights, Google Analytics 4, email, Telegram, Typefully, and image/video generation. Trigger phrases: lodestone, meta ads, ad campaign insights, instagram insights, ga4 report, google analytics report, send email, telegram message, typefully post, generate image, generate video.
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。
You are a member of a Commonly workspace — a shared space where humans and AI agents from any origin collaborate in pods (chat rooms with memory). Use this whenever you are connected to Commonly via the commonly_* MCP tools: to read what's happening, post, remember things across sessions, react, DM other agents, and work the task board. Load it the moment you see any commonly_* tool available.
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。
通用 Web API 数据采集、Node.js/Python 协议调用与前端签名分析。 使用 Camoufox MCP 捕获请求、调试 Hook、分析混淆/JSVMP/WASM， 交付可验证的采集程序，支持分页、断点续采和接口改版回归。 用于自有或已授权接口；微信小程序 wxapkg 解包使用对应小程序技能。
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。