技能注册表

分析 Azure 基础架构与使用数据，以找出节省成本的机会，并透过 GitHub 组织后续运行。

根据自然语言需求设计 Azure 基础架构，或分析现有资源，并支持架构调整与以 Bicep 部署。

在运行前检查 Bicep 部署，包括验证范本语法、预览 Azure 资源变更及评估部署权限。

提供 Azure CLI 命令指引，用于管理 Azure DevOps 项目、保存库、管线、工作项目及相关服务。

截取最新的 Azure 零售价格并据此估算成本，包括 Copilot Studio 点数用量。

评估 Azure 资源的运作健康状况，运用记录与遥测数据调查问题，并提出具优先级的修复建议。

整理 Azure 资源群组中的资源及其已确认的链接，呈现清晰的架构概观。

它会将所需的 Azure 权限对应到符合最小权限原则的内置角色；若没有合适的内置角色，则使用自订角色。

本指南说明如何在 Azure 设置及部署静态前端，并使用本机模拟器及选用的无服务器 API。

可在 Chrome 中浏览与操作页面，并查看页面结构、浏览器诊断信息及性能表现。

以规则、固定种子的变化与演化行为塑造计算美学，让视觉风格从可重现的作品中自然浮现。

运用 Anthropic 的色彩配置与字体规范，为素材套用一致的品牌视觉并维持文字层级。

创建视觉理念，并将其转化为文字精简的原创设计作品。

Reference for the Claude API / Anthropic SDK — model ids, pricing, params, streaming, tool use, MCP, agents, caching, token counting, model migration. TRIGGER — read BEFORE opening the target file; don't skip because it "looks like a one-liner" — whenever: the prompt names Claude/Anthropic in any form (Claude, Anthropic, Fable, Opus, Sonnet, Haiku, `anthropic`, `@anthropic-ai`, `claude-*`, `us.anthropic.*`, `[1m]`); the user asks about an LLM (pricing/model choice/limits/caching) — never answer from memory; OR the task is LLM-shaped with provider unstated (agent/MCP/tool-definition/multi-agent/RAG/LLM-judge/computer-use; generate/summarize/extract/classify/rewrite/converse over NL; debugging refusals/cutoffs/streaming/tool-calls/tokens). SKIP only when another provider is being worked on (overrides all triggers): OpenAI/GPT/Gemini/Llama/Mistral/Cohere/Ollama named in the query; OR `grep -rE 'openai|langchain_openai|google.generativeai|genai|mistralai|cohere|ollama'` over the project hits (run this grep FIRST if no provider named — don't Read the file).

此技能透过搜集背景、逐节修订与读者测试，引导用户协作撰写文档。

依据主题发展界面方向，透过明确的视觉层次、字体、动态效果与易懂的用户文案呈现特色。

此技能提供撰写内部消息的指引，协助采用公司偏好的格式。

指引 MCP 服务器从设计、实作、审查、测试到评估的完整流程，让大型语言模型能透过设计完善的工具与外部服务交互。

协助规划、撰写、测试代理技能，并依据观察结果持续改进。

提供创建与优化 Slack 动画 GIF 的指引与工具。

透过套用相互搭配的色彩与字体，为演示文稿及其他成品创建一致的视觉风格。

使用已配置的 React 与 TypeScript 工具链，创建并封装适用于 Claude 对话的前端作品。

使用 Python Playwright 脚本自动化检查本机 Web 应用程序。

Build, debug, or review Cloudflare Agents SDK applications using the agents package.

Discover and choose Cloudflare products for apps, APIs, AI agents, storage, networking, and security. Use for architecture and product selection, including when the user describes a need without naming a Cloudflare product; then find the relevant skill or documentation.

Implement or troubleshoot Cloudflare Email Sending and Email Routing integrations and their delivery configuration.

Design, configure, troubleshoot, or review Cloudflare One Zero Trust and SASE deployments. Use cloudflare-one-migrations for migration planning from other vendors.

Assess and plan migrations from existing VPN, SWG, or SASE platforms to Cloudflare One, including policy mapping, parity gaps, and rollout.

Build, debug, or review Cloudflare Durable Objects code for persistent state and coordination.

Migrate Cloudflare Sandbox apps from stable @cloudflare/sandbox to @cloudflare/sandbox@next (SDK 1.0 preview). Use sandbox-next for apps already on the preview.

Build or maintain Cloudflare Sandbox apps on @cloudflare/sandbox@next (SDK 1.0 preview). Use sandbox-migrate-to-next when porting a stable app.

Build or maintain Cloudflare Sandbox apps on the stable @cloudflare/sandbox package. Use sandbox-next for preview apps and sandbox-migrate-to-next for stable-to-preview migrations.

Set up, repair, or migrate to Cloudflare Turnstile bot verification in an existing frontend and backend, including server-side Siteverify.

提供以测量为依据的流程，查看浏览器追踪、网络行为与可取得的源代码，并排列具体性能改善项目的优先级。

Cloudflare Workers best practices for production applications. Use when writing, reviewing, or configuring Workers.

Run or troubleshoot Wrangler CLI commands and configure Worker projects for local development, Previews, deployment, and Cloudflare resource management.

协助设计与查看 C# 异步代码，着重于 Task 的使用、例外传递与有效率的并行作业。

创建 XML 文档注解的撰写惯例，清楚说明 C# API 的行为与用法。

创建以 C# 撰写的 Model Context Protocol 服务器项目，使用主机建置器管理相依性注入与 stdio 传输，并提供可自动探索的工具及实务操作说明。

提供 MSTest 3.x/4.x 单元测试的实用指引，涵盖测试结构、现代断言、生命周期与数据驱动测试。

协助撰写可靠的 NUnit 单元测试，采用清晰的结构、聚焦的断言与合适的测试数据。

协助运用一致的结构、生命周期挂钩、断言与数据驱动模式，撰写清晰可靠的 TUnit 测试。

它说明如何依循 xUnit 惯例组织、整理与验证单元测试，涵盖断言、共用测试环境、隔离及分类。

协助 .NET/C# 代码遵循项目在文档、架构、相依性注入、异步作业、测试、设置、本地化、记录、安全性与可维护性方面的惯例。

评估 C#/.NET 代码如何运用设计模式、架构分层与平台最佳实务。

协助为 .NET 应用程序选择安全的时区处理方式，并整理成实用的实作指引。

它整理 .NET 方案升级工作，涵盖项目与相依性评估、代码和管线调整，以及发布验证。

将项目的文件类型与明确指定的惯例转化为编辑器通用规则，让格式保持一致。

提供 EF Core DbContext 与实体设计，以及查找、数据保存、迁移、安全性和测试的实务指引。

它透过一致地管理 NuGet 相依套件，并以还原检查验证版本修改，来维护项目完整性。

Build and submit iOS and Android apps with EAS to TestFlight, the App Store, or Google Play. Supports Expo and other React Native projects, plus existing native apps. Use for eas.json setup, release pipelines, signing, app versions and build numbers, store submissions, and listing metadata. For Expo websites and API routes, use eas-hosting; for adding React Native screens to a native app, use expo-brownfield.

Deploy Expo websites and Expo Router API routes to EAS Hosting - export the web bundle, run eas deploy for production and PR preview URLs, manage environment secrets and custom domains, and work within the Cloudflare Workers runtime. Also covers authoring API routes (+api.ts handlers, HTTP methods, request handling, CORS). Use when deploying an Expo web app or API routes, setting up EAS Hosting, or configuring hosting environments and domains. Not for native builds or store releases - use the eas-app-stores skill for those.

Use for anything related to EAS Observe - adding `expo-observe` to an Expo project (AppMetricsRoot/ObserveRoot HOC, markInteractive and ObserveInteractiveMarker, the useObserve hook, the Expo Router / React Navigation integrations for per-route metrics, user-defined events via `Observe.logEvent`, error reporting via ObserveErrorBoundary and `Observe.reportError`, and runtime config such as sampleRate and dispatchInDebug), querying via the EAS CLI (`eas observe:metrics-summary`, `observe:metrics`, `observe:routes`, `observe:events`, `observe:session`, `observe:versions`), interpreting the resulting metrics (cold/warm launch, TTR, TTI, navigation cold/warm TTR, update download, and the TTI frameRate/device/network params for triaging slow startups), or shipping an Observe integration inside a third-party package.

Run and control a user's app on a remote iOS/Android simulator hosted on EAS cloud. Read before running any `eas simulator:*` commands - it has the current syntax for this experimental API. Use whenever the user needs a simulator they can't run locally - 'run my app on a cloud simulator', 'use eas simulator to run/install/screenshot my app', 'I'm on Linux/Cursor and need an iOS device', 'no sim on this box / headless CI', 'let an agent click through my app and screenshot it', 'test my dev build on a remote sim with live reload', 'stream a sim to my browser' - even when they don't say 'EAS Simulator' or 'cloud'. On a host WITHOUT a local simulator (Linux, CI, cloud sandbox) it's the default; on macOS, do NOT auto-trigger for a plain 'run on the simulator' - use it only for a cloud/remote/shareable sim, an iOS version they lack, or an agent-driven session. NOT for local sims (expo run:ios, Xcode, Android Studio), EAS Build/Update, web preview, or physical devices.

Configure and use EAS Update for over-the-air JavaScript and asset updates with expo-updates and EAS CLI. Use when setting up OTA updates, running eas update:configure or eas update, publishing to preview/staging/production channels, explaining branches/channels/runtime versions, testing updates, or debugging why an installed build still shows old code. Load for TestFlight, preview, or production updates that do not appear, including questions about cold launches or reopening the app. Not for update health metrics; use eas-update-insights for adoption, crashes, and rollout monitoring.

Check the health of published EAS Update: crash rates, install/launch counts, unique users, payload size, and the split between embedded and OTA users per channel. Use when the user asks how an update is performing, whether a rollout is healthy, how many users are on the embedded build vs OTA, or wants to gate CI on update health.

Helps understand and write EAS workflow YAML files for Expo projects. Use this skill when the user asks about CI/CD or workflows in an Expo or EAS context, mentions .eas/workflows/, or wants help with EAS build pipelines or deployment automation.

Build animations in React Native and Expo, making the decisions in the order that determines whether they feel right — should it animate, which thread it runs on, which properties, spring or timing, how the gesture hands off, how it degrades. Writes the implementation with Reanimated, Gesture Handler, Expo Router and expo-haptics. Use when animating anything in an Expo app, adding gestures, sheets, screen transitions, press feedback or haptics, or fixing motion that stutters on device. For web animation use `animate`.

Add an iOS App Clip target to an Expo app. Use when the user mentions App Clip, AASA, apple-app-site-association, appclips, smart app banner, or wants to ship a lightweight iOS Clip invoked from a URL alongside their parent app.

Integrate Expo and React Native into an existing native iOS or Android app. Use for brownfield, embedding a React Native screen in SwiftUI/UIKit or Kotlin, or AAR/XCFramework packaging. Covers isolated and integrated approaches. For building or distributing a purely native app with EAS, use eas-app-stores.

Use when implementing or debugging ANY network request, API call, or data fetching. Covers fetch API, React Query, SWR, error handling, caching, offline support, loading/empty/error screen states, and Expo Router data loaders (`useLoaderData`).

Build and maintain a design system inside an Expo app - a reusable theme of design tokens (color, spacing, typography, radius, shadow, motion), reusable component structure with variant/size/state prop conventions, and rules for when to extract a repeated view into a shared component. Use when creating or organizing theme files and design tokens (theme.ts / theme/), extending an existing theme or styling library (NativeWind, Tamagui, Restyle, Unistyles) in its own idiom, standardizing styles so screens (including AI-generated ones) look consistent and polished, fixing an app that looks AI-generated or generic instead of native (the named native-slop tells), building an in-app component library, or auditing an app for design-system drift (hardcoded colors, spacing, fonts). For platform styling specifics (semantic colors, HIG rules, native controls) use expo-native-ui; for folder layout of a new app use expo-project-structure.

Build and distribute Expo development clients locally or via TestFlight for internal testing. For production TestFlight releases and store submission, use the eas-app-stores skill.

Use Expo DOM components to run web code in a webview on native and as-is on web. Migrate web code to native incrementally. For the end-to-end migration of a whole web app, use the expo-web-to-native skill.

Expo's official example projects - the expo/examples repo of ~70 `with-*` integrations (Stripe, Clerk, Supabase, OpenAI, maps, Reanimated, SQLite, Skia, NativeWind, and more). Use when integrating a third-party library or service into an existing Expo app and you want the canonical, version-matched pattern to adapt, or when scaffolding a new project from one with `npx create-expo --example`.

Guide for creating and writing Expo native modules and views using the Expo Modules API (Swift, Kotlin, TypeScript). Covers module definition DSL, native views, shared objects, config plugins, lifecycle hooks, autolinking, and type system. Use when building or modifying native modules for Expo. Not for migrating an existing Swift module from the definition DSL to the Expo Modules API 2.0 macros; use expo-migrate-module (from the expo-experiments plugin) for that.

Build beautiful, native-feeling Expo screens. Covers Apple HIG styling, semantic colors, native controls, SF Symbols, media, visual effects, gradients, storage, and responsive layout. For routing and navigation, use the expo-router skill; for motion and animation, use the expo-animation skill.

Entry point and router for every Expo or EAS task. Load this skill first — before writing code and before choosing another expo-* / eas-* skill — when the request, PRD, or spec mentions Expo, EAS, Expo Go, or an expo-* package, or the project has an `expo` dependency in `package.json`. Within that gate it also covers app specs and designs to implement (tabs, stacks, maps, lists, navigation, building from a screenshot), and phrasings like 'implement a mobile app', 'make my app look native', 'add navigation', 'fetch some data', 'upgrade my SDK', 'add Expo to my existing native app', 'ship to the App Store', or 'I'm new to Expo, where do I start'. A fully specified request (SDK pinned, libraries named, layout given) still routes through here — the shared setup rules still apply. Do NOT load it when neither signal is present: a bare React Native project with no `expo` dependency is not Expo work. Detects the real goal, routes to the right expo-* / eas-* skill, and owns the shared setup rules.

Folder structure for a new Expo app. Use when scaffolding or laying out a new Expo project with Expo Router, or deciding where a file should live in one. For new projects only — never restructure an existing app to match.

Navigation and routing for Expo Router. Covers file-based routes, groups and dynamic routes, folder organization, Link with previews and context menus, native Stack, page titles, modals and form sheets, NativeTabs, headers and toolbars, and header search bars.

协助用户提交对 Expo 的具体意见，并管理独立且预设关闭的使用情况遥测。

Build native UI with the @expo/ui package: real SwiftUI on iOS and Jetpack Compose on Android. Default to @expo/ui for sheets (BottomSheet), pickers, sliders, toggles, menus, and grouped-form sections — do NOT reach for Reanimated, @gorhom/bottom-sheet, or RN built-in Picker/Switch; use @expo/ui instead. Fall back to RN built-ins only when @expo/ui is missing the component. NOTE: @expo/ui List renders native grouped rows like an iOS Settings screen — it is NOT a virtualized list; use FlatList/FlashList for large datasets. Covers universal components (Host, Column, Row, Button, Text, List, BottomSheet, FieldGroup, Switch, Slider, Picker, Menu), drop-in replacements for RN community libraries, and platform-specific SwiftUI/Jetpack Compose trees. Not for Expo Router navigation, Reanimated, or data fetching.

Guidelines for upgrading Expo SDK versions and fixing dependency issues

Migrate an existing web React app to a native iOS/Android app with Expo. Use when the user wants to turn a website into a mobile app, port a Next.js/Vite/CRA React codebase to React Native, reuse web code on native incrementally, or asks how web idioms (the DOM, CSS, React Router, localStorage, window) map to native. This is the end-to-end migration guide; use the `expo-dom` skill for the DOM-component mechanism itself.

提供 CodeQL 扫描的完整指引，涵盖选择语言与建置模式、设置查找及处理分析结果。

它会依照保存库的拉取请求范本，将规格整理成一个标题与内文清楚完整的拉取请求。

说明如何设置 Dependabot，让保存库各套件生态系中的相依项目保持更新并处理已知安全漏洞。

协助你透过终端机使用 GitHub CLI 指令与选项管理 GitHub 工作。

依照 Git Flow 分析目前的 Git 变更，并命名符合变更目的的分支。

协助协调管理 GitHub issue，涵盖 issue 详情、分类、负责人与相关工作。

将 PlantUML 描述转换为易读的纯文字图表，并可使用 ASCII 或 Unicode 框线字符。

它会简化网站文件的准备、GitHub 保存库创建与 Pages 设置，让支持的内容能在在线提供浏览。

说明如何设置 GitHub 秘密扫描与推送保护、创建自订秘密模式，以及管理侦测到的秘密警示。

此技能整理透过 Data Manager API 设置及运行受众成员异动，并检查处理结果的步骤。

协助开发者使用 Data Manager API 建置及排查 Google Ads、Google Analytics 与 Floodlight 的事件导入集成。

说明如何准备 Data Manager API 的本机访问环境，并安装其用户端与辅助程序库。

透过比较广告活动与广告群组数据并检查脱机转换上传，协助诊断 Google Ads 帐户成效。

说明如何准备、安装、设置并连接官方 Google Ads MCP Server，涵盖本机及 Cloud Run 部署方式。

说明如何设置 Google Ads API 验证与帐户路由，让开发者能安全地运行首次广告活动查找。

透过调整 Gradle 设置、初始化流程、各广告格式 API 与回呼行为，指引一致的 SDK 迁移。

说明如何透过 Google Mobile Ads SDK 在应用程序版面中放置持续显示且可自动刷新的矩形广告。

引导开发者依平台设置 Google Mobile Ads SDK，从加入 SDK 到确认集成完成。

它说明全屏幕插页式广告的生命周期，以及如何将广告安排在应用程序内容之间的自然转场点。

说明如何提供须由用户主动选择观看的全屏幕广告，并在用户与广告交互后给予应用程序内奖励。

检查 Google Mobile Ads SDK 集成是否符合相关设置要求。

协助开发者运用 DAI SDK，在视频应用程序中集成 Google Ad Manager 的服务器端广告插入功能。

它协助在数字产品中集成 Google IMA SDK，以请求并管理串流视频与音频广告。

它协助你透过 Google Analytics Admin API 查看可访问的帐户与资源摘要，并管理支持的 Analytics 设置。

提供透过 v1beta Data API 激活访问、运行自订 Google Analytics 报表，以及确认指针与维度兼容性的设置与使用指引。

它以可靠性、成本、安全、资安及 Vertex AI 评估品质为内核，规划代理的告警。

Deploy open models or custom weights from Model Garden to Agent Platform endpoints, check the status of an in-progress deployment operation, or clean up resources by undeploying models and deleting endpoints. Use when asked to actively deploy a model, list the Model Garden CATALOG of available models, check if a specific model is deployable (`gcloud ai model-garden models list-deployment-config`), query deployment cost, troubleshoot deployment errors (like quota limits), or undeploy/clean up endpoints. Also use when copying and deploying a 1P Tuned Model. Don't use for pure listing/discovery questions of the form "is X deployed?", "list my endpoints", or "which regions have models running?" — for those use `agent-platform-endpoint-management`. Don't use for running model evaluations (use `agent-platform-eval-flywheel` skill).

Manages Agent Platform serving endpoints. Use when you need to create, list, describe, update, or delete serving endpoints for model deployment on Agent Platform. Also use when troubleshooting endpoint permission, quota, or resource busy errors. Don't use for deploying models to endpoints or for running model evaluations.

Measures and improves the quality of AI models and agents on Google Cloud using the Eval Quality Flywheel methodology. Use when generating synthetic user scenarios, evaluating an agent or model, building an eval dataset, picking or writing evaluation metrics, analyzing failures, comparing results before and after a fix, or when guidance is needed on Agent Platform eval methodology — including dataset schema, LLM-as-judge scoring, and common failure causes. For fine-tuning, use agent-platform-tuning. For general production deployment, use agent-platform-deploy.

连接 Google Cloud Agent Platform，并支持使用 Gemini、OpenMaaS 与自订端点模型进行推论。

说明如何将 Gemini API 应用程序从 Google AI Studio 迁移至 Gemini Enterprise Agent Platform，涵盖帐务、验证与用户端设置。

Agent Platform Model Registry Management. Use when you need to upload, list, describe, update, or delete machine learning models (and their versions) in the Agent Platform Model Registry. Don't use for model training, model deployment to endpoints, or managing non-Agent Platform models.

提供查看受管理提示详细数据、取得指定版本，以及以输入值填入已保存范本的工作流程。

Manage and query Agent Platform RAG Engine Corpora and retrieve grounded contexts using the Google GenAI SDK. Use when listing RAG corpora or files, inspecting a corpus, retrieving contexts, or generating content grounded in a RAG corpus. Do not use for standard database queries (use SQL/Spanner skills), Google Workspace RAG, or other RAG products like gRAG.

提供在 Gemini Enterprise Agent Platform 上探索与管理技能、监控长时间运行作业，并在本机创建新技能范本的方式。

它会调查 Agent Gateway、Agent Registry、Agent Identity、政策、IAP 委派授权及服务扩充功能中的设置、授权与运行阶段故障，并提出诊断结果和修正建议，但不会套用变更。

Agent Platform Model Tuning. Use when you need to fine-tune open models or Gemini models using Agent Platform infrastructure. Don't use for model training outside Agent Platform, model deployment to endpoints (use `agent-platform-deploy`), or managing serving endpoints (use `agent-platform-endpoint-management`).

Manages GenAI tuning jobs in Agent Platform. Use this to list, get, or cancel ongoing model tuning jobs. Don't use for fine-tuning models (use `agent-platform-tuning`), deploying models to endpoints (use `agent-platform-deploy`), or managing serving endpoints (use `agent-platform-endpoint-management`).

提供管理 AlloyDB for PostgreSQL 资源及使用 AlloyDB MCP 工具自动运行数据库作业的指引。

引导以本机 Terraform 为准的工作流程，透过 Application Design Center 设计、验证、评估、导入、部署及确认 Google Cloud 基础架构。

本技能说明如何在 BigQuery SQL 中运用机器学习与生成式 AI 功能（包括 Vertex AI 集成）进行数据分析。

说明 BigQuery 的基本设置方式，以及如何使用 SQL 分析大型数据集。

提供以 BigQuery 支持的 DataFrame 操作运行数据整理、分析、可视化与机器学习的指引。

Provides data-retrieval best practices, tool selection guidance, and performant SQL query syntax for BigQuery telemetry across INFORMATION_SCHEMA, Cloud Monitoring, and the REST API. Use when the telemetry to fetch is already known, selecting telemetry tools, writing performant INFORMATION_SCHEMA queries, retrieving telemetry for diagnosing single-job performance bottlenecks, investigating slot contention, job concurrency and queue latency, analyzing reservation capacity, utilization and autoscaling saturation, or auditing capacity-based and on-demand compute and storage resource billable usage. Don't use for root-cause diagnosis or symptom troubleshooting when the cause is unknown (use bigquery-troubleshooting first), or for writing or optimizing business logic SQL (use bigquery-optimization).

Provides workflows to optimize BigQuery environments (capacity planning, editions), storage assets (partitioning, clustering, storage lifecycles, billing models), and SQL queries. Use when optimizing cost, modeling Edition migrations, rightsizing reservations, evaluating logical vs. physical storage, designing table partitioning/clustering, generating table DDL, migrating unpartitioned tables, managing partition expiration, optimizing individual SQL queries, or evaluating acceleration structures (search indexes, materialized views, BI Engine). Do not use for raw usage reporting (use bigquery-observability), query execution plan analysis, error troubleshooting, or diagnosing why a specific job was slow (use bigquery-troubleshooting).

分析 BigQuery 工作遥测数据，以计算 slot 小时与查找成本，并找出性能瓶颈。

Provides diagnostic workflows and step-by-step root-cause analysis procedures for actively broken, failing, or slow BigQuery jobs, execution graph and query plan stage bottlenecks, system performance issues, or unexpectedly expensive workloads. Use when interpreting symptoms, isolating bottlenecks, diagnosing cost spikes (on-demand query spend, capacity slot autoscaling, storage growth), execution graph stages or substep variables, identifying root causes, and determining remediation steps. Don't use for writing or optimizing SQL, proactive capacity planning, or storage layout design (use bigquery-optimization), or when the user already knows which telemetry they want and just needs the query (use bigquery-observability).

提供建置与管理 Google Bigtable 系统的实用指引，着重于高效率数据访问与审慎变更。

Teaches the fundamentals of Cloud Build on Google Cloud. Covers core concepts, API enablement, console navigation to the Build History page, and the end-to-end workflow for creating and manually running a basic build trigger. Do not use for managing private pools or complex pipeline architectures.

协助将数据库需求转化为合适的 Google Cloud 服务选择。

Configure single-project Cloud Logging on Google Cloud: regional log buckets, log sinks, log views, restricting or hiding sensitive logs in the default view (_Default) filter, IAM permissions for views (Logs View Accessor, IAM conditions), logs-based metrics, log exclusions, and sampling. Don't use for cross-project logging or multi-project setups.

说明如何在集中式保存与查找时汇整之间选择，并概述各方案的设置方式与访问控制。

Generates Logging Query Language (LQL) queries for Cloud Logging on Google Cloud from natural language. Use this skill when you need to query log data or when you are debugging issues. You can filter log data by Google Cloud service. Don't use this skill to query other databases, such as SQL or Cloud Spanner.

Generates Cloud Monitoring Server-Driven UI (SDUI) Widget and XyChart Protocol Buffer textprotos on Google Cloud from resolved PromQL or ListTimeSeries queries. Use when: - Generating valid google.monitoring.dashboard.v1.Widget textprotos, containing PrometheusQuery or TimeSeriesFilter datasets, for use with the Cloud Monitoring Dashboards API, gcloud CLI, or declarative dashboard definitions. - Synthesizing Server-Driven UI (SDUI) widget titles, axis labels, and plot types for Prometheus or ListTimeSeries queries. Don't use for: - Metric discovery or PromQL query generation. For those tasks, use the cloud-monitoring-metric-selection or cloud-monitoring-promql-query skills.

它会将指针描述数据与资源脉络转换为 Cloud Monitoring 时间串行查找所需的参数。

Retrieve, query, and identify relevant Cloud Monitoring metric descriptors on Google Cloud for a service or resource (such as Compute Engine, Spanner, BigQuery, Cloud Run, Cloud SQL, Pub/Sub, Cloud Storage, etc.). Use when asked to find, list, search, or discover GCP metric types, names, kind/value schemas, or descriptors.

将 Cloud Monitoring 的指针与资源描述子转换为有效的 PromQL 查找，并采用合适的指针命名、筛选条件与汇总方式。

Configures best-practice, high-signal alerting policies for Cloud Run resources on Google Cloud (services, jobs, and worker pools) based on seasoned SRE practices. Use when analyzing, recommending, writing, or deploying Terraform PromQL alerting policies to monitor Cloud Run error rates (4xx/5xx), request latency, container instance saturation (warning/critical), container CPU/memory utilization and allocation, billable instance time, job execution status, and worker pool queue backlog. Don't use for GKE workloads (use gke-alert-configuration) or Compute Engine VMs.

本指南说明 Cloud Run 的设置与部署，涵盖资源类型选择、建置方式、IAM 前置条件及常见故障诊断。

说明 Google Cloud 的全代管关系数据库服务如何运行 MySQL、PostgreSQL 与 SQL Server 工作负载，并处理维护、备份、高可用性及安全连接。

Analyzes the downstream impact (blast radius) using data lineage on Google Cloud when a BigQuery table or view is broken, stale, or modified. Identifies all downstream tables, dashboards, and processes that will be affected. Use when: - Performing a blast radius or impact analysis for a BigQuery table or view. - Assessing the consequences of modifying, deleting, or pausing updates to a BigQuery asset. - Identifying downstream dependencies (tables, dashboards, processes) of a BigQuery asset. Don't use for: - General BigQuery querying or data analysis (use BigQuery-related tools instead). - Non-BigQuery assets (e.g., Cloud Storage files) unless they are part of the BigQuery lineage. - Creating or modifying lineage links directly.

Summarizes data lineage graphs on Google Cloud to help users debug data quality issues and understand data provenance for BigQuery and Cloud Storage. Use when summarizing upstream and downstream data flows, and presenting complex lineage data as an intuitive Markdown report. Don't use for generic BigQuery queries, editing lineage relationships, or downstream deprecation. Don't use for downstream blast-radius impact analysis (use datalineage-bigquery-asset-impact-analysis skill instead).

Translates Snowflake dbt SQL models to Standardized BigQuery SQL. Handles SQL compilation, Jinja macro placeholder masking, BigQuery Translation Service migration workflows, AST-based config transformations, explicit type casting, JSON extraction standardization, and deduplication. Use when migrating Snowflake dbt pipelines or models to Google Cloud BigQuery. Don't use for generic BigQuery queries or non-Snowflake SQL migrations.

它透过模拟的 UDM 事件追踪威胁行为，指出哪些既有 Google SecOps 规则能侦测到这些行为，以及哪些地方仍有涵盖缺口。

提供使用 Google Developer Device Platform 准备与管理远程 Android 设备工作阶段的实用指引。

Generates code and provides documentation for the Genkit Dart SDK. Use when the user asks to build AI agents in Dart, use Genkit flows, or integrate LLMs into Dart/Flutter applications.

Develop AI-powered applications using Genkit in Go. Use when the user asks to build AI features, agents, flows, or tools in Go using Genkit, or when working with Genkit Go code involving generation, prompts, streaming, tool calling, or model providers.

Develop AI-powered applications using Genkit in Node.js/TypeScript. Use when the user asks about Genkit, AI agents, flows, or tools in JavaScript/TypeScript, or when encountering Genkit errors, validation issues, type errors, or API problems.

Develop AI-powered applications using Genkit in Python. Use when the user asks about Genkit, AI agents, flows, or tools in Python, or when encountering Genkit errors, import issues, or API problems.

此技能协助创建应用程序设置所需的 Firebase CLI 使用条件与项目脉络。

透过逐一命令的语法验证、明确的项目与位置范围、数据精简及高风险操作防护，提升 gcloud 作业的安全性与可靠性。

说明控制平面 API 的验证方式、代理程序设置字段与请求回应流程。

说明如何使用 Gen AI SDK 在 Agent Platform 上建置 Gemini 应用程序，涵盖用户端设置、文字与多模态功能，以及高级工作流程。

Guides the usage of Gemini Interactions API on Gemini Enterprise Agent Platform. Use when the user wants to use the stateful, server-managed Interactions API for multi-turn conversations, background execution, streaming, structured output, and function calling on the Agent Platform.

创建 Gemini Enterprise Live API 的异步 WebSocket 用户端，支持双向串流、ADC 验证与工作阶段续接。

协助判断 Compute Engine 主机维护是否造成或可能造成 GKE GPU/TPU 节点中断，并找出保护受影响工作负载的方法。

透过比对 JobSet 指针、节点集区事件、节点与主机健康状况、Pod 调度状态及工作者记录，诊断 GKE 训练工作负载重新启动的原因。

协助解读 TPU 切片状态、找出布建问题、验证工作负载设置，并引导安全的清理或控制器变更。

透过 Cloud Monitoring 指针与 PromQL 协助评估 GKE TPU 的健康状态与性能。

提供一套结构化方法，诊断 GKE 中 TPU v6e 的 vBAR 相关当机与内存不足问题，并检查频繁收集指针是否造成冲突。

它指引团队为 GKE 的四大黄金信号与集群健康状况设计高信号且低杂讯的监控。

协助应用程序团队评估应用程序、将其容器化、准备 Kubernetes 信息清单，并部署至 GKE。

设置 Backup for GKE，透过调度或手动备份及受控还原来保护 Kubernetes 资源与持久磁盘区数据。

协助完成 Google Kubernetes Engine 集群的核心设置与操作，包括模式选择、凭证取得及工作负载部署。

协助在 Google Kubernetes Engine（GKE）上运行批量处理与高性能计算工作负载。

提供 GKE 自动扩缩的设置、调校与疑难排解指引，协助节点容量配合工作负载需求。

协助用户选择 GKE 集群模式，并设置供创建或就绪度检查使用的正式环境组态。

协助将工作负载与成本限制转化为可靠的 GKE ComputeClass 容量与放置政策。

它结合 BigQuery 导出的帐单数据、成本分配中继数据与实时资源用量，协助解读 GKE 费用，并说明 Autopilot 与 Standard 计价方式的差异。

透过调整工作负载资源配置并选择具成本效益的容量与承诺方案，在维持 GKE 集群安全与可靠性的同时降低支出。

依据 GKE 版本与节点设置，找出适用于自订节点映像档的对应黄金基础映像档。

Provides GKE golden path configuration defaults, production readiness checklists, and cluster default patterns. Use when designing GKE clusters, verifying GKE production readiness, or checking configurations against GKE defaults. Don't use for setting up workload autoscaling specifically (use gke-workload-scaling instead).

提供在 Google Kubernetes Engine 上准备、部署与调校 AI/ML 推论工作负载的流程，涵盖推论服务器信息清单、加速器选择与扩缩指引。

它将应用程序需求转化为一致的部署设置，协助 GKE 工作负载兼顾安全性、可靠性与成本效益。

协助规划共用的 GKE 集群，为不同团队与环境设置适当的访问、资源、网络及成本界线。

为 GKE 创建以私有集群和 VPC 原生模式为基础的网络基准，并说明集群地址配置、DNS、对外流量及网络政策如何配合。

透过比对节点状况与事件、kubelet 和运行阶段记录及监控数据，协助诊断 GKE 节点为何回报 NotReady 或 Unknown，并提出安全的处置建议。

说明如何设置与评估 GKE 记录、指针、Managed Prometheus 和告警，并涵盖相关营运成本。

提供透过平台层级设置、访问控制与服务集成来强化 GKE 集群安全性的指引。

统筹评估 GKE 集群与工作负载的正式上线准备程度，涵盖扩充性、可观测性、可靠性、安全性、备份与灾难复原、对外流量入口、成本、升级及建议预设设置。

它透过为集群拓扑、工作负载调度、健康检查与终止流程提供防护措施，协助提升 GKE 作业韧性。

Configures GKE edge networking, traffic routing, load balancing, and private service endpoints. Use when configuring Gateway API manifests, standard Ingress, Cloud Armor WAF security policies, Container-Native Load Balancing (NEGs), Private Service Connect (PSC), or Google-managed SSL certificates on GKE, and to troubleshoot Ingress and load-balancer 502/5xx errors, backend health-check failures, connection draining, and TLS/SSL policy enforcement. Don't use for core cluster IP planning, Dataplane V2 network policies, or node NAT egress (use gke-networking instead).

Manages GKE storage, including PVCs, PersistentVolumes, and Filestore. Use when configuring GKE storage or creating PVCs. For GCS FUSE mounts, use google-cloud-storage-fuse. For diagnosing storage failures (volume attach/mount errors, disk performance/node storage pressure, or Cloud Storage FUSE OOM), use gke-storage-troubleshooting. Don't use for database administration or replication strategies outside volume provisioning context.

它可诊断影响 GKE 工作负载的持久性保存故障，并提出可供审核的修正方案，而不变更运行中的集群。

Plans, executes, and validates Google Kubernetes Engine (GKE) cluster upgrades and maintenance operations for both Standard and Autopilot clusters. Produces upgrade plans, pre/post-upgrade checklists, maintenance runbooks with gcloud commands, release channel strategy, and troubleshooting guides. Handles node pool upgrade strategies (surge, blue-green), version compatibility, PDB management, and workload-specific concerns (stateful, GPU, operators). Use this skill whenever the user mentions GKE upgrades, Kubernetes version bumps, node pool maintenance, GKE patching, cluster version management, release channel selection, maintenance windows, surge upgrades, stuck upgrades, or any GKE lifecycle management task — even casual mentions like "we need to upgrade our clusters" or "plan our next GKE maintenance" or "our upgrade is stuck." Don't use for GKE cluster creation, application onboarding, general networking/routing setup, or security policy configurations (use gke-basics or relevant GKE skills instead).

根据可取得的证据，诊断 GKE 工作负载的 Google Cloud 身分设置及验证失败的位置。

本指南说明如何透过手动调整、HPA 与 VPA 扩缩 GKE 工作负载，并为 Pod 资源设置合适配置。

以唯读方式诊断 GKE HorizontalPodAutoscaler 故障并找出根本原因。

它将 GKE 工作负载安全需求转化为具体设置指引，并说明实作时对集群层级功能的依赖。

透过检查 Pod 状态、事件、日志与连接，系统性诊断 GKE 应用程序工作负载故障，并提出不直接变更在线资源的 GitOps 修正建议。

它引导用户完成 Google agents-cli 的初始设置，并为 Gemini Enterprise Agent Platform 上的代理程序开发指向合适的生命周期流程。

Audits Filestore instances on Google Cloud across projects for disaster recovery readiness (missing or stale backups), security access governance (overly permissive NFS export rules, 0.0.0.0/0 exposure, missing ROOT_SQUASH), and reliability compliance (Physical Zone Isolation PZI and Physical Zone Separation PZS). Use when assessing storage health posture, auditing NFS export permissions, identifying unprotected file shares, or validating zone failure domains. Don't use for Cloud Storage buckets, Persistent Disk, or NetApp Volumes.

Inspects Filestore capacity and utilization on Google Cloud, evaluates storage scaling rules, and performs capacity autoscaling (scale UP for low free space or scale DOWN for cost optimization). Use when monitoring Filestore instance headroom, resizing instance shares, configuring automated growth/shrink thresholds (custom thresholds apply globally across projects in session memory), or preventing out-of-space outages. Don't use for Cloud Storage (GCS) buckets, Persistent Disk block storage, or NetApp Volumes.

Diagnoses and resolves Filestore client mount failures on Google Cloud, permission errors (EACCES), and network timeouts (ETIMEDOUT). Use when an NFS mount hangs or fails from a Compute Engine VM, GKE pod, Cloud Run service, or Vertex AI workload, when `mount.nfs` reports "Connection timed out" or "access denied by server", when checking whether VPC ingress firewall rules or `nfsExportOptions` allow a client IP, or when a previously working Filestore share suddenly stops mounting after an administrative change. Don't use for Cloud Storage (GCS) buckets, Persistent Disk, or Cloud NetApp Volumes, and don't use for Filestore capacity scaling or backup and export-policy auditing.

Inspects, searches, and reads files and POSIX metadata on Filestore (NFS) instances on Google Cloud without local NFS client packages or root privileges. Use when browsing Filestore shares, searching files, reading remote logs, or inspecting file attributes; don't use for Cloud Storage buckets, Cloud NetApp Volumes, Persistent Disks, or modifying/deleting files.

将工作负载与流量需求转化为 Google Cloud 全球外部 Application Load Balancer 的建议设计。

Investigates Google Cloud networking issues by analyzing GCP logs, metrics, and diagnostics. Use when investigating dropped network traffic, packet drops, drop reasons, VPC Flow Logs (including Private Service Connect / PSC, serverless / App Engine Direct VPC, and cost estimation), NAT, firewall, or threat logs, querying latency and throughput metrics, or running Connectivity Tests for path diagnostics. Don't use for generic VM management or non-observability tasks.

Provides expert guidance on Identity and Access Management (IAM) and authenticating and authorizing to Google Cloud services and APIs, covering human users, service identities, Application Default Credentials (ADC), and best practices for secure access.

为 Google Cloud 创建安全的登陆区基础，涵盖基准安全控管、组织化的资源阶层，以及集中式记录与监控。

协助个人开发者检查 Google Cloud CLI 访问与验证状态、选择项目并确认帐务，为部署第一个工作负载做好准备。

Queries and retrieves active security findings, external exposures, toxic combinations, vulnerabilities, threats, and sensitive data risks from Security Command Center on Google Cloud. Use when retrieving details for a security finding by its name, validating finding scope (e.g., verifying findingClass is TOXIC_COMBINATION, VULNERABILITY, EXTERNAL_EXPOSURE, or THREAT), or fetching finding details for triage. Don't use to draft remediations, apply patches, or execute configurations.

Configures Cloud Monitoring PromQL-based Service Level Objective (SLO) alerting policies on Google Cloud for resources registered in App Hub or individually specified. Generates Terraform output. Use when the user asks to configure an SLO or Service Level Objective. Don't use for standard alerting policies.

协助为实时、双向的多模态 AI 工作负载设计量身打造的 Google Cloud 架构，并制定部署与验证指引。

协助端到端规划、设计、实作及验证结合分布式数据与代理式 AI 的多云开放式数据湖仓解决方案。

Designs a tailored multi-product agentic data science architecture on Google Cloud that incorporates opinionated best practices. Use when architecting multi-product solutions for agent-based data analytics or ML workloads. Don't use for simple queries, non-agentic pipelines, general cloud reviews, or writing agent code.

引导团队循序从工作负载需求走到经核准且可供部署的分析解决方案。

Interactively discovers requirements and designs holistic, multi-product system architectures, solution blueprints, and deployment recommendations for complex workloads on Google Cloud. Use when designing end-to-end cloud solutions, selecting and integrating Google Cloud services, generating architecture diagrams, or conducting requirements discovery for new cloud workloads or migrations. Don't use for single-product tasks (use product-specific skills), initial onboarding or authentication (use google-cloud-recipe-*), Well-Architected Framework reviews or audits (use google-cloud-waf-*), or workloads covered by specialized solution skills.

它提供一套有步骤的流程，将代理工作负载的需求转化为 Google Cloud 设计、实作计划与验证方式。

提供由架构师引导的途径，将既有 AI 推论部署改为由 GKE 自行托管推论。

它引导团队在 Google Cloud 上以 AlloyDB 为内核，设计、部署并验证安全且低延迟的混合式搜索解决方案。

提供实务指引，协助保护 Google Cloud Agent Gateway 各路径的流量并控管代理程序的工具访问。

协助在 Google Cloud 上设计安全的无服务器应用程序，将彼此隔离的运算层连接至私有数据服务。

协助规划以检索增强生成支持私有企业内容的安全、低延迟对话式搜索方案。

Stores, retrieves, and manages data as objects in Cloud Storage on Google Cloud (also known colloquially as GCS) buckets. Use when you need to interact with Cloud Storage — set up a Storage MCP server (remote or local Toolbox), create or configure buckets, upload, download, stream, or transfer data, organize objects with folders, generate signed URLs, control access (IAM, ACLs, public access prevention), set storage classes (Standard, Nearline, Coldline, Archive), manage lifecycle and cost, protect data (versioning, CMEK, retention, Bucket Lock, holds, soft delete), host static websites, trigger Pub/Sub notifications, mount buckets (gcsfuse), or optimize performance. Covers gcloud storage / gsutil, JSON/XML APIs, client libraries, Terraform, and Cloud Storage MCP servers. Don't use for non-Storage MCP servers, block storage (Persistent Disk), BigQuery, or databases (Cloud SQL, Spanner, Bigtable, Firestore).

Creates Cloud Storage on Google Cloud (also known colloquially as GCS) buckets. Analyzes the workload (sensitive data, media hosting, ingestion, web hosting, archiving, backup, logging, analytics, AI/ML, or general-purpose), validates project-level security settings, and designs a secure-by-default, cost-effective configuration (location, storage class, uniform bucket-level access, public access prevention, soft delete, lifecycle) before creating it. Use whenever a user wants to create, make, set up, provision, or spin up a bucket, or needs object storage for an app, service, pipeline, or dataset — even a "simple" or "default" bucket, or when bucket creation is one step in a larger workflow. Outputs or executes the creation via gcloud, the JSON/REST API, Terraform, or SDK client libraries (C++, Java, Python, Go). Don't use for anything other than creating new buckets — for uploads, downloads, access changes, or reconfiguring existing buckets, use google-cloud-storage-basics.

Mounts Cloud Storage buckets on Google Cloud as a POSIX file system using Cloud Storage FUSE (and the gcsfuse CLI tool). Use when interacting with the gcsfuse CLI or Cloud Storage FUSE mounts: decide whether FUSE, native gs:// reads, or Filestore/Managed Lustre fits a workload, deploy tuned mounts on GKE, Compute Engine, or Cloud Run, enable and size file, stat, and list caches, tune mount flags (--implicit-dirs) or config-file settings, apply workload profiles, keep ML checkpointing safe (rename atomicity, hierarchical namespace/HNS, close-time finalization, concurrent writers), or diagnose slow training, low throughput, or bill spikes with gcsfuse metrics. Covers mount semantics, gcsfuse CLI and config files, GKE gcsfuse CSI driver (Workload Identity principal:// bindings, profile StorageClasses, sidecar sizing), and Cloud Run volume mounts. Don't use for bucket administration or data management without a mount (google-cloud-storage-basics) or fully POSIX-compliant shared file systems (Filestore, Managed Lustre; use gke-storage).

Evaluates Google Cloud workloads for cost efficiency and FinOps alignment using the Google Cloud Well-Architected Framework Cost Optimization pillar. Use when the user asks to analyze Google Cloud spend, reduce cloud bills, rightsize resources (for example, Compute, GKE, Storage, Databases), evaluate discount options (for example, CUDs, SUDs, Spot VMs), or eliminate idle capacity. Do not use for standalone product pricing lookups or non-cost architecture design.

协助依据 Well-Architected Framework 评估 Google Cloud 工作负载的营运实务，并找出可行的改善方向。

协助评估并改善 Google Cloud 工作负载的性能，涵盖资源配置、模块化架构、弹性调整与持续量测。

此指引协助依循以用户为中心的服务目标、冗余、扩充性、可观测性、优雅降级及复原测试，提升工作负载可靠性。

依据架构完善框架的安全原则评估 Google Cloud 工作负载，并找出身分、网络、数据、营运、AI 与法规遵循方面的缺口。

协助团队将永续考量融入云端架构与营运决策，以降低工作负载对环境的影响。

提供一致的流程，用于查看及管理 Google Cloud 资源上的 IAM 允许政策与拒绝政策。

Safely simulates and applies Identity and Access Management (IAM) v1 (Allow) policy changes on Google Cloud. Uses the Policy Simulator to replay historical access logs against proposed policies to prevent breaking active workloads before applying the changes. Use when simulating or applying IAM v1 allow policies across Projects, Folders, or Organizations. Don't use for analyzing IAM v2 deny policies, VPC Service Controls, or performing general policy troubleshooting.

它指引如何透过 Privileged Access Manager 设计及管理具时限且可审核的 Google Cloud 角色权限提升。

Diagnoses, remediates, and manages Identity and Access Management (IAM) access issues on Google Cloud. Supports two distinct operational modes: (1) Requester Flow for developers encountering access denials (capturing error context, self-service PAM JIT activations, elevated developer self-remediation, or logging structured tickets), and (2) Resolver Flow for privileged administrators (authoritative Policy Troubleshooter analysis, deny policy exemptions, least-privilege role discovery, and PAM/IAM provisioning).

引导撰写与验证 Google 代管 Apache Airflow 服务的 DAG，并兼顾环境限制、版本兼容性及最佳实务。

透过交叉比对工作中继数据、日志、DAG 源代码及相关环境信号，诊断 Managed Airflow 中失败的 DAG 运行与工作运行个体。

协助调整现有 Managed Service for Apache Airflow DAG，使其兼容于 Airflow 2.11.1 或 Airflow 3。

它协助维护一致且可审核的事件纪录，涵盖案件信息、调查注记、优先级、状态与结案。

它引导在 Google SecOps 中完成 YARA-L 2.0 侦测规则的创建、验证、测试及经核准后部署，并支持侦测覆盖缺口的分析与补强。

透过验证行为假设、回查历史遥测中的指针，以及调查罕见或异常活动，支持 Google SecOps 的系统化威胁狩猎。

Expert guidance for deep security incident and entity investigations in Google SecOps. Use when investigating cases, analyzing entities (hosts, IPs, domains, hashes, users), extracting and searching UDM events, performing asset and user timeline analysis, and detecting lateral movement across enterprise networks. Don't use for detection rule authoring or YARA-L tuning (use secops-detection-engineering), proactive hypothesis-driven hunting (use secops-hunt), initial alert triage (use secops-triage), or basic case status updates (use secops-cases).

Expert guidance for security alert triage in Google SecOps. Use when investigating and triaging security alerts, determining false positives vs. true positives, assessing entity risk, adjusting alert severity or priority, and closing or escalating alerts and cases. Don't use for deep multi-hop incident investigations across host timelines (use secops-investigate), proactive threat hunting or retroactive IoC sweeps (use secops-hunt), or authoring new detection rules (use secops-detection-engineering).

Assists in provisioning instances and databases, designing performant schemas, and querying data in Spanner. Use when designing primary keys, writing SQL queries or client library code, or diagnosing performance issues.

它将工作负载最佳实务评估整理为可重复的流程，从选择范围与规则到查看可供修正的发现事项。

它会按需查找技能目录，将适用的请求导向相关的 Google 已发布技能。

Searches, retrieves, and synthesizes official Google developer documentation across Google Cloud, AI/Gemini, Android, Chrome, Web, Flutter, Go, Firebase, and other Google developer platforms. Integrates with the Developer Knowledge MCP server (search_documents, get_documents, answer_query) or the Developer Knowledge REST API fallback. Use when searching for gcloud CLI commands, API syntax, IAM permissions, official documentation, architectural comparisons, or product choice overviews. Don't use for local filesystem lookups or non-Google documentation.

本指南说明如何将 Google OAuth 更新权杖绑定至用户端持有的 P-256 密钥，同时让 Google API 调用沿用标准 OAuth 访问权杖授权方式。

gplay CLI 可用于处理 Google Play Console 工作，例如管理版本发布、应用程序中继数据、权限与报表。

它说明如何透过 Gradle 建置 Android 发行版本，涵盖签署、版本管理、代码优化及 Play Console 准备工作。

协助设置与管理 Google Play 一次性商品和订阅，包括定价、本地化商品信息、基本方案与促销优惠。

它可透过验证、比对、批量同步与个别编辑，协助维护 Google Play 商店文案、本地化中继数据及图片。

将 Fastlane 的 Android 中继数据目录转换为 gplay 的文件式格式，把上架文字集成至 listing.json，并保留变更记录与图片。

说明如何设置依购买力调整的地区价格，同时保留 Google Play 商品现有的价格设置。

说明如何安全地验证 Google Play 购买权杖，并管理商品与订阅的后续流程。

透过 gplay CLI 协调 Google Play 上的应用程序发布、测试轨道升级与分阶段推出。

列出并下载保存在 Google Cloud Storage 中的 Google Play Console 财务与应用程序统计报表。

协助截取、筛选、查看及分析 Google Play 评论，并提供回复评论与监控意见的范例。

控管 Google Play 应用程序的分阶段发布，以限制问题造成的影响。

协助整理并验证现有的 Android 商店截屏与宣传图片，为更新 Google Play 商店页面做好准备。

提供一套发布就绪检查流程，结合本机产物与商店信息验证、Play Console 状态检查、编辑内容验证及发布试行。

透过 gplay CLI 管理 Google Play 测试轨道的测试者访问权与版本发布流程，并以编辑工作阶段处理测试者和轨道变更。

它可管理 Google Play Console 开发者帐户与个别应用程序的访问权，并可依层级限定权限范围。

透过 Play Developer Reporting API 查找应用程序的稳定性、性能与错误数据。

协助团队透过寻找首批客户、快速验证产品价值并解决采用停滞的根本原因，将新产品上市过程转化为持续的客户学习。

提供人工智能产品的实用上市策略，涵盖定位、定价与销售，并特别着重企业信任与营运准备度。

协助领导者以坦诚且聚焦决策的方式，向董事会与投资人沟通公司绩效、风险、决策及策略。

协助团队透过生态系、社区与合作伙伴计划提升开发者采用，并着重于激活、正式环境部署及持续推荐。

提供企业客户策略、资格评估、利害关系人协调与交易健康度管理的结构化方法。

提供四阶段框架，引导企业客户从签约走向持续采用并实现价值。

创建一套可随公司成长扩展的营运节奏，以维持团队步调一致并改善回报、规划与决策速度。

本技能提供实用架构，协助设计能为公司、合作伙伴及共同客户创造可衡量价值的伙伴生态系。

协助根据现有产品优势与客户共鸣创建清晰且具防御力的市场定位，并在扩大推行前先验证消息。

协助团队选择并创建合适的自助式、销售辅助或销售主导成长模式，并以可衡量的渠道经济指针链接获客、激活、转换与留存。

提供决策框架，协助依据客户价值、服务成本与目标市场定位，制定技术产品的价格。

以韧性、扩展性、安全性、营运、性能与成本方面的实务，指引符合正式环境需求的 Azure 系统设计。

它协助代理程序透过截取、反思、保存并运用经验，在不同工作阶段之间延续有用的学习成果。

说明应用程序如何透过 stdio 或 TCP 与 Copilot CLI 通信，以及如何设置和管理 SDK 工作阶段。

说明如何透过 Microsoft Graph beta 布建 Microsoft Entra 代理程序身分蓝图与支持 OAuth2 的代理程序身分，并涵盖验证和所需权限。

它会依据设计系统、可用性、无障碍与视觉品质标准评估既有界面，或打造独特且可投入正式环境的前端体验。

将零散的问题信息整理成精简且可采取行动的 GitHub issue 报告。

提供 MCP 服务器的完整开发流程，从研究 API、选择框架与传输方式，到实作、测试及评估实用工具。

它为 Microsoft Agent Framework 开发提供共通设计指引与各语言的实务做法。

依据官方文档与可运作的代码范例，确认 Microsoft API 与 SDK 的用法。

透过官方 Microsoft 文档查找或截取所需内容，以支持可靠的技术说明。

说明如何使用 Azure OpenAI Realtime 将文字转为语音叙述，并准备供播放的音频。

提供设计、测试与维护精简代理技能的实用架构，并涵盖 Azure SDK 指引的惯例。

说明 Microsoft Fabric Lakehouse 如何组织数据、管理访问权，以及支持查找与性能调校，以协助规划解决方案。

协助开发者建置代码优先的 Power Platform 应用，集成 Power Apps SDK 并提供连接器范例。

查看 DAX 度量值中的高成本模式、清晰度、正确性与可维护性，并提出实用的改进建议。

评估 Power BI 数据模型的架构、性能、可扩展性、可维护性、数据完整性与安全性。

提供有系统的方法，找出 Power BI 性能瓶颈并选择可行的优化措施。

协助依据受众、决策需求与数据特性，设计清晰、易用且易于访问的 Power BI 报表视觉呈现、版面与交互方式。

协助建置与调整符合 Copilot Studio 规范的 MCP 连接器，涵盖 JSON-RPC 处理、以工具输出提供资源，以及 OAuth 安全性。

协助依据建模最佳实务评估并改善 Power BI 语意模型。

Write and debug Tampermonkey userscripts - scripts that change or automate a website from inside the browser. Use when the user wants a userscript (Tampermonkey, Greasemonkey or Violentmonkey), wants JavaScript or CSS injected into a site they visit, wants page elements hidden or rearranged, wants a form auto-filled or page data scraped, needs to react to URL changes in a single-page app, needs cross-origin requests from a page script, has a userscript that does not run, or is setting one up in TypeScript. Do NOT use for Selenium, Puppeteer or Playwright automation - use a browser-automation tool; for building a browser extension (WebExtensions/Manifest V3) - write it as an extension; or for server-side scripts.

透过有架构的流程、分级回馈与聚焦可行改进，让代码审查成为共同学习的过程。

将拟议的软件变更整理为结构化、可运行的实作计划，并列出分阶段任务与可验证的完成条件。

协助将项目内容整理成吸引人、信息丰富且易于阅读的介绍。

创建清晰且完整的规格文档，定义供生成式 AI 使用的解决方案需求、限制与界面。

将两个项目状态的比较转化为可重用的 Copilot 指引，让后续变更延续观察到的代码演进模式。

分析保存库中的变更、将相关文件分组，并以 Conventional Commit 格式记录提交。

引导以渐进方式重整既有代码，提升清晰度与可维护性，同时维持外部行为不变。

它将重构工作整理成安全且考量相依性的流程，涵盖从调查到验证。

透过脉络化查看与跨文件数据流追踪，辨识并验证简单模式比对可能漏掉的安全弱点。