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Universal desktop application controller for Windows. Automate any desktop app — send messages, click buttons, type text, take screenshots, and interact with native Windows applications. Use this skill when the user asks to: "control my computer", "automate desktop", "send a message on [any chat app]", "click on [something]", "type into [app]", "take a screenshot", "操控电脑", "自动化桌面", "给某某发消息", "打开某某软件", "截个屏", or any task involving interacting with Windows desktop applications. WeChat triggers: "send a WeChat message", "message someone on WeChat", "打开微信发消息", "给某某发微信", "微信发送", "微信发消息", "用微信发", "帮我发微信". Email/Web triggers: "打开邮箱", "发邮件", "send email", "open browser", "操控浏览器", "网页操作". Supports: WeChat, WeCom, DingTalk, Feishu/Lark, QQ, Telegram, Slack, Teams, any browser-based web app, and any other Windows desktop application. Inspired by OpenAI's playwright-interactive skill, combining code-based automation with visual screenshot feedback loops. ROUTING: For web/Electron apps (email, Slack, browser), prefer Playwright with CSS selectors for speed and precision. For native desktop apps (WeChat, QQ, DingTalk), use Win32 API automation.

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DEPRECATED: This skill has been replaced by `mcp-app-builder`. Check if `mcp-app-builder` is available in the skills folder. If not, install it: `npx skills install mcp-use/mcp-use --skill mcp-app-builder` Use `mcp-app-builder` instead of this skill. Build ChatGPT apps with interactive widgets using mcp-use and OpenAI Apps SDK. Use when creating ChatGPT apps, building MCP servers with widgets, defining React widgets, working with Apps SDK, or when user mentions ChatGPT widgets, mcp-use widgets, or Apps SDK development.

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When the user wants to audit or optimize an App Store or Google Play listing. Also use when the user mentions 'ASO audit,' 'app store optimization,' 'optimize my app listing,' 'improve app visibility,' 'app store ranking,' 'audit my listing,' 'why aren't people downloading my app,' 'improve my app conversion,' 'keyword optimization for app,' or 'compare my app to competitors.' Use when the user shares an App Store or Google Play URL and wants to improve it.

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DEPRECATED: This skill has been replaced by `mcp-app-builder`. Check if `mcp-app-builder` is available in the skills folder. If not, install it: `npx skills install mcp-use/mcp-use --skill mcp-app-builder` Use `mcp-app-builder` instead of this skill. Build ChatGPT apps with interactive widgets using mcp-use and OpenAI Apps SDK. Use when creating ChatGPT apps, building MCP servers with widgets, defining React widgets, working with Apps SDK, or when user mentions ChatGPT widgets, mcp-use widgets, or Apps SDK development.

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Set up automatic keepalive for Appwrite free-tier projects to prevent them from being paused after 7 days of inactivity. Use when working in a codebase that uses Appwrite, when the developer mentions Appwrite free tier, project pausing, or keeping an Appwrite project alive, or when you detect Appwrite configuration (node-appwrite dependency, APPWRITE_ env vars, Appwrite SDK imports).

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Design and create Busabase-backed App-in-Skill packages with a canonical app project, Busabase-native setup and product onboarding, review/execution workflows, a standardized responsive UI, and AirApp-first delivery. Use when a user wants a Busabase research desk, review queue, planner, action console, operating dashboard, control panel, collaboration workspace, onboarding/readiness flow, or an existing Kelly App-based skill updated. Every generated skill contains a complete app/ project, deploys that source to Busabase AirApp by default, runs pnpm dev only when local preview is explicitly requested, follows the Kelly desktop and phone UI contract, delegates AirApp runtime, SDK, security, scaffolding, and deployment constraints to busabase-app-creator, and delegates repository-level conformance testing to kelly-app-skill-creator-tests.

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通过Mac查看、添加和编辑Apple Reminders中带截止时间的待办事项，管理列表并完成或删除任务，整理在iPhone或iPad上查看的个人任务。

• 查看今天和已逾期的任务
• 添加带截止日期的个人待办事项
• 整理iPhone或iPad上的任务

制作将习惯和日常任务呈现为任务挑战的移动应用原型，在单个HTML页面中展示封面、今日任务列表和详情三屏，并加入经验值、等级进度、步骤与奖励。

• 制作带游戏元素的习惯应用原型
• 探索含经验值的每日任务列表
• 设计带步骤和奖励的任务详情

Manage applications in the Flow Nexus marketplace, from finding and publishing apps to deploying templates with configuration settings. The skill also supports version management, usage analytics, and reviews of application quality and security.

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• Find apps for a specific purpose
• Publish an app and manage its versions
• Deploy a template with configuration settings

Launch a project's actual app and interact with it to check a change in use. Follow existing project launch instructions when available, or use patterns for command-line tools, servers, terminal interfaces, desktop apps, browser apps, and libraries.

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• Launch an app and check the changed behavior
• Capture and inspect a screen after interacting with the app
• Check command output or an API response

Prepare an existing Twenty app for npm or marketplace publication by reviewing its README, listing details, package metadata, logos, and screenshots. The workflow includes checking publication settings, validating the build, and running the publishing command.

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• Prepare a Twenty app marketplace listing
• Write or review README and About text
• Check logos, screenshots, and public assets

Write Python code to manage settings in Azure App Configuration. Covers reading, updating, filtering, and locking settings, managing feature flags that switch features on or off, and creating snapshots of configuration values.

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• Read and update shared application settings from Python
• Separate development and production values with labels
• Manage feature flags that enable or disable features

Turn git changes since the last tag into clear App Store release notes. Identifies updates users will notice, groups new features, improvements, and fixes, removes internal work and duplicates, and checks each bullet against the actual changes.

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• Drafting App Store What's New text for a release
• Turning technical commits into plain release notes
• Collecting user-visible changes between specified tags

Create Appium tests that automate Android and iOS app interactions in Java, Python, or JavaScript. Configure local devices, emulators, simulators, or TestMu AI cloud devices, with guidance on element selection, waits, and gestures.

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• Automate login tests for Android and iOS apps
• Create tests for local emulators and simulators
• Configure real-device tests on TestMu AI cloud

Design and implement native iPhone and iPad interfaces with SwiftUI using Apple's design guidelines. Covers screen navigation, flexible layouts, system icons, accessibility, adjustable text sizes, and colors that adapt to light and dark modes.

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• Designing iOS screens using Apple's guidelines
• Building layouts for both iPhone and iPad
• Implementing tabs and screen navigation

Prepare and deploy static sites to Azure Static Web Apps with the SWA CLI. Set up local previews, configure routing and authentication, connect Azure Functions APIs, and arrange automated deployment through GitHub Actions.

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• Deploying an existing static site to Azure
• Testing a frontend and API together locally
• Configuring routes and authentication

Develop and troubleshoot distributed applications with Aspire. Coordinate services written in different languages through AppHost, configure connections and startup dependencies, inspect logs and metrics, and prepare deployment settings.

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• Creating an Aspire application from a template
• Connecting services written in different languages
• Diagnosing services through logs and traces

Set up Cloudflare Access and Tunnel to protect internal applications using user identity and device health checks. Covers application access policies, WARP device enrollment, and audit logging for teams replacing VPN access.

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• Replace VPN access with application-specific permissions
• Expose internal apps without opening inbound ports
• Limit contractors to approved applications

Run language models locally with llama.cpp on CPUs, Apple Silicon, or AMD and Intel GPUs. The skill guides model setup, memory-saving GGUF format choices, interactive chat, and serving models through an OpenAI-compatible API.

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• Running a language model on a CPU-only computer
• Setting up local chat on an Apple Silicon Mac
• Choosing model quantization to fit available memory

Build web apps that run inside the Zoom client using SDK setup guidance, authorization examples, shared participant state, and custom video layouts. The reference also helps diagnose blank panels, missing capabilities, and other runtime problems.

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• Build a web app that runs inside Zoom
• Implement authorization inside the Zoom client
• Create custom video layouts or camera overlays

Guides an application’s first deployment to GKE, Google’s managed Kubernetes service. Covers application assessment, container creation, image storage, deployment configuration, security settings, health checks, and verification that the application is running.

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• Deploying an existing application to GKE for the first time
• Creating a Dockerfile and registering a container image
• Preparing deployment configuration with health checks

Import existing notes and agent instructions into LifeOS by splitting content into sections and proposing destinations. Review uncertain classifications, approve selected groups, and retain source information alongside the imported material.

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• Importing Obsidian or Notion exports into LifeOS
• Moving notes from an older LifeOS installation
• Reviewing and importing CLAUDE.md or Cursor rules

Configure ArgoCD in KubeSphere DevOps for GitOps, which keeps deployed applications aligned with configuration stored in Git. Covers application management, repository credentials, shared sign-in, multiple clusters, and troubleshooting synchronization problems.

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• Deploy applications from configuration stored in Git
• Create applications through the tenant API
• Configure shared sign-in through Dex

Guides you through running large language models on CPUs, Apple Silicon, and AMD or Intel GPUs. Covers choosing GGUF model files, reducing memory through quantization, and starting an OpenAI-compatible chat server.

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• Running a local LLM on an Apple Silicon Mac
• Trying LLM inference on a CPU-only machine
• Choosing a model format that fits available memory