「roi」の検索結果

Upgrade an Android project to Android Gradle Plugin (AGP) 9. Use when migrating to AGP 9, updating Gradle build files, migrating to built-in Kotlin, or adopting the new AGP DSL.

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Harden Intent handling, WebView configuration, and FileProvider access in Android apps. Use when securing Intent extras, configuring WebViews, or exposing files via FileProvider; defer manifest export flags and generic Bundle typing to focused Android security guidance.

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Diagnoses and fixes .NET MAUI development environment issues. Validates .NET SDK, workloads, Java JDK, Android SDK, Xcode, and Windows SDK. All version requirements discovered dynamically from NuGet WorkloadDependencies.json — never hardcoded. Use when: setting up MAUI development, build errors mentioning SDK/workload/JDK/Android, "Android SDK not found", "Java version" errors, "Xcode not found", environment verification after updates, or any MAUI toolchain issues. Do not use for: non-MAUI .NET projects, Xamarin.Forms apps, runtime app crashes unrelated to environment setup, or app store publishing issues. Works on macOS, Windows, and Linux.

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Calculate comprehensive ROI for AI implementation projects. Takes current costs, manual process time, team size, and hourly rates. Generates detailed roi-analysis.md with executive summary, cost-benefit tables, sensitivity analysis, break-even timeline, and comparison scenarios. Use when evaluating AI investments, building business cases, or justifying automation spend.

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XML-specified Android UI journey testing, interactive step execution, assertion verification, and JSON outcome reporting.

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Symfony UX Icons for rendering SVG icons in Twig templates. Supports 200,000+ Iconify icons (Lucide, Heroicons, Tabler, Material Design, etc.), local SVG files, and custom icon sets with aliases. Use when displaying icons, configuring icon defaults, importing or locking on-demand icons, creating icon aliases, or styling SVG icons with CSS. Code triggers: <twig:ux:icon />, ux_icon(), UX_ICONS_DEFAULT_ICON_ATTRIBUTES, icon.yaml, icons/, iconify:, lucide:, heroicons:, tabler:, mdi:, bin/console ux:icons:lock, bin/console ux:icons:import. Also trigger when the user asks "how to add an icon", "how to use Lucide/Heroicons/Tabler icons", "how to render an SVG icon in Twig", "how to lock icons for production", "how to create icon aliases", "how to style an icon", "icon not found", "icon not rendering". Do NOT trigger for interactive maps (use ux-map) or general Twig components (use twig-component).

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Expert mobile game developer specializing in iOS and Android game optimization, touch input design, battery and thermal management, device fragmentation handling, and App Store/Play Store submission. Deep knowledge of mobile-specific constraints and best practices for shipping performant, player-friendly mobile games. Use when "mobile game, ios game, android game, touch input, mobile optimization, mobile performance, app store, play store, mobile battery, thermal throttling, mobile porting, tablet game, phone game, mobile monetization, mobile build, mobile, ios, android, touch, optimization, performance, battery, thermal, app-store, play-store, game-development, unity-mobile, godot-mobile, device-fragmentation" mentioned.

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World-class expertise in measuring, attributing, and optimizing AI-generated content performance. Combining data science rigor with content strategy intelligence to answer the questions traditional content analytics can't: Is AI content performing? Which AI variations convert? What's the true ROI of AI vs traditional content creation? This isn't vanity metrics for robots. This is the discipline of proving AI content drives business outcomes - and using that data to make AI content systems better, faster, and more profitable. Built on the principles of companies like Jasper, Copy.ai, and Notion who've scaled AI content operations with measurement as the foundation. Use when "ai content performance, ai content analytics, measure ai content, ai content roi, ai vs human content, ai content attribution, ai content testing, ai variation testing, ai content dashboard, ai content metrics, prompt performance, ai content conversion, ai content quality score, content velocity, ai content efficiency, ai-content, analytics, measurement, attribution, roi, ab-testing, performance, optimization, data-driven, content-analytics" mentioned.

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语音转文字 (ASR) (语音转文字 (ASR / 语音识别 / Speech-to-Text) 应用工程 (从业者视角) — 选型、集成、优化语音转文字能力，尤其面向移动端、低成本、快速识别的场景。覆盖: (a) 引擎/API 地图 — 云端 API(OpenAI gpt-4o-transcribe / Whisper API、Deepgram、AssemblyAI、Google / Azure / AWS Transcribe、讯飞、字节火山、阿里、腾讯、百度) vs 开源模型(Whisper / faster-whisper / whisper.cpp / distil-whisper、NVIDIA NeMo Parakeet / Canary、阿里 FunASR / Paraformer / SenseVoice、Moonshine、Vosk、Kaldi / k2 / icefall) vs 端侧·移动 SDK(whisper.cpp + CoreML / Metal、iOS Speech framework、Android SpeechRecognizer、Picovoice、SenseVoice 端侧); (b) 准确率(WER / CER) × 延迟(RTF / 流式) × 成本 三角权衡与选型决策树; (c) 成本优化 playbook — 端侧免费 / 批量折扣 / VAD 裁静音 / 量化(int8 / ggml) / 蒸馏 / 自托管 break-even; (d) 移动端集成 — 端侧 vs 云、流式 vs 批量、断点检测(endpointing)、隐私 / 离线; (e) 后处理 — 标点 / 数字规整(ITN) / 说话人分离(diarization) / 时间戳。学派分歧: 云 API vs 端侧自托管、通用大模型(Whisper) vs 专用流式(RNN-T / Conformer)、闭源 API vs 开源、准确率派 vs 成本派、英文优先 vs 中文 ASR(FunASR / SenseVoice / 讯飞)。不含: 文字转语音(TTS / 语音合成，方向相反)、声纹识别 / 说话人验证为主业、语音 agent / 对话式 AI、ASR 模型训练科研深水区。) Master OS — automated mastery of 语音转文字 (ASR / 语音识别 / Speech-to-Text) 应用工程 (从业者视角) — 选型、集成、优化语音转文字能力，尤其面向移动端、低成本、快速识别的场景。覆盖: (a) 引擎/API 地图 — 云端 API(OpenAI gpt-4o-transcribe / Whisper API、Deepgram、AssemblyAI、Google / Azure / AWS Transcribe、讯飞、字节火山、阿里、腾讯、百度) vs 开源模型(Whisper / faster-whisper / whisper.cpp / distil-whisper、NVIDIA NeMo Parakeet / Canary、阿里 FunASR / Paraformer / SenseVoice、Moonshine、Vosk、Kaldi / k2 / icefall) vs 端侧·移动 SDK(whisper.cpp + CoreML / Metal、iOS Speech framework、Android SpeechRecognizer、Picovoice、SenseVoice 端侧); (b) 准确率(WER / CER) × 延迟(RTF / 流式) × 成本 三角权衡与选型决策树; (c) 成本优化 playbook — 端侧免费 / 批量折扣 / VAD 裁静音 / 量化(int8 / ggml) / 蒸馏 / 自托管 break-even; (d) 移动端集成 — 端侧 vs 云、流式 vs 批量、断点检测(endpointing)、隐私 / 离线; (e) 后处理 — 标点 / 数字规整(ITN) / 说话人分离(diarization) / 时间戳。学派分歧: 云 API vs 端侧自托管、通用大模型(Whisper) vs 专用流式(RNN-T / Conformer)、闭源 API vs 开源、准确率派 vs 成本派、英文优先 vs 中文 ASR(FunASR / SenseVoice / 讯飞)。不含: 文字转语音(TTS / 语音合成，方向相反)、声纹识别 / 说话人验证为主业、语音 agent / 对话式 AI、ASR 模型训练科研深水区。: top builders' mental models, tool stack, current workflows, jargon, and where to keep up. Trigger this skill when the user works on 语音转文字 (ASR / 语音识别 / Speech-to-Text) 应用工程 (从业者视角) — 选型、集成、优化语音转文字能力，尤其面向移动端、低成本、快速识别的场景。覆盖: (a) 引擎/API 地图 — 云端 API(OpenAI gpt-4o-transcribe / Whisper API、Deepgram、AssemblyAI、Google / Azure / AWS Transcribe、讯飞、字节火山、阿里、腾讯、百度) vs 开源模型(Whisper / faster-whisper / whisper.cpp / distil-whisper、NVIDIA NeMo Parakeet / Canary、阿里 FunASR / Paraformer / SenseVoice、Moonshine、Vosk、Kaldi / k2 / icefall) vs 端侧·移动 SDK(whisper.cpp + CoreML / Metal、iOS Speech framework、Android SpeechRecognizer、Picovoice、SenseVoice 端侧); (b) 准确率(WER / CER) × 延迟(RTF / 流式) × 成本 三角权衡与选型决策树; (c) 成本优化 playbook — 端侧免费 / 批量折扣 / VAD 裁静音 / 量化(int8 / ggml) / 蒸馏 / 自托管 break-even; (d) 移动端集成 — 端侧 vs 云、流式 vs 批量、断点检测(endpointing)、隐私 / 离线; (e) 后处理 — 标点 / 数字规整(ITN) / 说话人分离(diarization) / 时间戳。学派分歧: 云 API vs 端侧自托管、通用大模型(Whisper) vs 专用流式(RNN-T / Conformer)、闭源 API vs 开源、准确率派 vs 成本派、英文优先 vs 中文 ASR(FunASR / SenseVoice / 讯飞)。不含: 文字转语音(TTS / 语音合成，方向相反)、声纹识别 / 说话人验证为主业、语音 agent / 对话式 AI、ASR 模型训练科研深水区。 problems and wants industry-grade thinking, tool selection, or workflow guidance. 触发词：「语音转文字」「语音识别」「asr」「speech to text」「stt」

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侍酒师与葡萄酒 (侍酒师 / 葡萄酒 (Sommelier & Wine) — 葡萄酒鉴赏 + 侍酒服务的职业认知操作系统，从业者/侍酒师/学习者视角。覆盖: (a) 两大认证体系张力 — WSET (Wine & Spirit Education Trust, 英国, 知识+SAT 系统化品鉴方法, Diploma/L4 笔试导向) vs Court of Master Sommeliers (CMS, 美/英, 服务+盲品演绎法+理论三位一体, MS 头衔), 外加 Institute of Masters of Wine (MW, 学术研究+论文) 与 UC Davis 葡萄酒学/葡萄栽培学学院派; (b) 盲品 = 纯 tacit 校准技能 — 演绎品鉴网格 (deductive tasting: 看色/闻香/品味 → 结构线索 calculated structure → 推断品种/产区/年份/酒龄), WSET SAT vs CMS deductive grid 两套方法论; (c) 第一性张力 — codifiable 理论层 (产区/品种/酒法/风土/酿造/葡萄栽培) ⇄ tacit 校准层 (品鉴味觉记忆/盲品/侍酒服务), 这行的核心矛盾是「可背诵的知识」与「不可言传的味觉」之间; (d) 旧世界 vs 新世界框架 — 旧世界以风土/法定产区为组织原则 (AOC/DOCG/QbA 分级法), 新世界以品种为标签; 风土 (terroir) 是核心组织概念; (e) 侍酒服务/酒店层 — 餐厅 floor 的配餐 (food pairing)、醒酒/侍酒温度、酒单与酒窖管理、by-the-glass 与饮品项目经济学、盲品 service exam; (f) 葡萄酒商业层 — 三级分销体系 (美国 three-tier)、定价/配额 (allocation)、期酒 (en primeur)、投资酒 (Liv-ex) 与拍卖; (g) 智识/品味演进 — 帕克百分制 (Robert Parker / Wine Advocate 100-point) vs Jancis Robinson 20 分制散文派; 经典派 vs 现代派酿造; 自然酒/低干预 (natural/minimal-intervention) vs 传统技术派. 学派分歧: WSET 知识体系派 vs CMS 服务盲品派、旧世界风土派 vs 新世界品种/技术派、自然酒派 vs 传统派、百分制点评派 vs 散文叙述派、经典平衡派 vs 浓郁现代派. 不含: 家庭自酿 DIY 套件、纯调酒/鸡尾酒/烈酒、葡萄园重型农机工程、酒精成瘾/公共卫生政策、酒类电商运营纯营销。) Master OS — automated mastery of 侍酒师 / 葡萄酒 (Sommelier & Wine) — 葡萄酒鉴赏 + 侍酒服务的职业认知操作系统，从业者/侍酒师/学习者视角。覆盖: (a) 两大认证体系张力 — WSET (Wine & Spirit Education Trust, 英国, 知识+SAT 系统化品鉴方法, Diploma/L4 笔试导向) vs Court of Master Sommeliers (CMS, 美/英, 服务+盲品演绎法+理论三位一体, MS 头衔), 外加 Institute of Masters of Wine (MW, 学术研究+论文) 与 UC Davis 葡萄酒学/葡萄栽培学学院派; (b) 盲品 = 纯 tacit 校准技能 — 演绎品鉴网格 (deductive tasting: 看色/闻香/品味 → 结构线索 calculated structure → 推断品种/产区/年份/酒龄), WSET SAT vs CMS deductive grid 两套方法论; (c) 第一性张力 — codifiable 理论层 (产区/品种/酒法/风土/酿造/葡萄栽培) ⇄ tacit 校准层 (品鉴味觉记忆/盲品/侍酒服务), 这行的核心矛盾是「可背诵的知识」与「不可言传的味觉」之间; (d) 旧世界 vs 新世界框架 — 旧世界以风土/法定产区为组织原则 (AOC/DOCG/QbA 分级法), 新世界以品种为标签; 风土 (terroir) 是核心组织概念; (e) 侍酒服务/酒店层 — 餐厅 floor 的配餐 (food pairing)、醒酒/侍酒温度、酒单与酒窖管理、by-the-glass 与饮品项目经济学、盲品 service exam; (f) 葡萄酒商业层 — 三级分销体系 (美国 three-tier)、定价/配额 (allocation)、期酒 (en primeur)、投资酒 (Liv-ex) 与拍卖; (g) 智识/品味演进 — 帕克百分制 (Robert Parker / Wine Advocate 100-point) vs Jancis Robinson 20 分制散文派; 经典派 vs 现代派酿造; 自然酒/低干预 (natural/minimal-intervention) vs 传统技术派. 学派分歧: WSET 知识体系派 vs CMS 服务盲品派、旧世界风土派 vs 新世界品种/技术派、自然酒派 vs 传统派、百分制点评派 vs 散文叙述派、经典平衡派 vs 浓郁现代派. 不含: 家庭自酿 DIY 套件、纯调酒/鸡尾酒/烈酒、葡萄园重型农机工程、酒精成瘾/公共卫生政策、酒类电商运营纯营销。: top builders' mental models, tool stack, current workflows, jargon, and where to keep up. Trigger this skill when the user works on 侍酒师 / 葡萄酒 (Sommelier & Wine) — 葡萄酒鉴赏 + 侍酒服务的职业认知操作系统，从业者/侍酒师/学习者视角。覆盖: (a) 两大认证体系张力 — WSET (Wine & Spirit Education Trust, 英国, 知识+SAT 系统化品鉴方法, Diploma/L4 笔试导向) vs Court of Master Sommeliers (CMS, 美/英, 服务+盲品演绎法+理论三位一体, MS 头衔), 外加 Institute of Masters of Wine (MW, 学术研究+论文) 与 UC Davis 葡萄酒学/葡萄栽培学学院派; (b) 盲品 = 纯 tacit 校准技能 — 演绎品鉴网格 (deductive tasting: 看色/闻香/品味 → 结构线索 calculated structure → 推断品种/产区/年份/酒龄), WSET SAT vs CMS deductive grid 两套方法论; (c) 第一性张力 — codifiable 理论层 (产区/品种/酒法/风土/酿造/葡萄栽培) ⇄ tacit 校准层 (品鉴味觉记忆/盲品/侍酒服务), 这行的核心矛盾是「可背诵的知识」与「不可言传的味觉」之间; (d) 旧世界 vs 新世界框架 — 旧世界以风土/法定产区为组织原则 (AOC/DOCG/QbA 分级法), 新世界以品种为标签; 风土 (terroir) 是核心组织概念; (e) 侍酒服务/酒店层 — 餐厅 floor 的配餐 (food pairing)、醒酒/侍酒温度、酒单与酒窖管理、by-the-glass 与饮品项目经济学、盲品 service exam; (f) 葡萄酒商业层 — 三级分销体系 (美国 three-tier)、定价/配额 (allocation)、期酒 (en primeur)、投资酒 (Liv-ex) 与拍卖; (g) 智识/品味演进 — 帕克百分制 (Robert Parker / Wine Advocate 100-point) vs Jancis Robinson 20 分制散文派; 经典派 vs 现代派酿造; 自然酒/低干预 (natural/minimal-intervention) vs 传统技术派. 学派分歧: WSET 知识体系派 vs CMS 服务盲品派、旧世界风土派 vs 新世界品种/技术派、自然酒派 vs 传统派、百分制点评派 vs 散文叙述派、经典平衡派 vs 浓郁现代派. 不含: 家庭自酿 DIY 套件、纯调酒/鸡尾酒/烈酒、葡萄园重型农机工程、酒精成瘾/公共卫生政策、酒类电商运营纯营销。 problems and wants industry-grade thinking, tool selection, or workflow guidance. 触发词：「侍酒师」「sommelier」「葡萄酒」「wine」「盲品」

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 短剧 (AI 微短剧) (AI 短剧 (AI 微短剧) (从业者视角) — 用 AIGC 工具（AI 视频生成 / AI 脚本 / AI 生图 / AI 配音 / 对口型 / 角色一致性）制作竖屏微短剧并变现的新兴行业。覆盖: (a) 创作工作流 — 选题→剧本/分镜→AI 生图→图生视频(首尾帧/运镜/参考)→配音/对口型→剪辑/调色→成片，其中角色/场景一致性是第一性难题; (b) 工具栈 — 视频生成(可灵 Kling / 即梦 Seedance / 海螺 Hailuo / Vidu / Runway Gen / OpenAI Sora / Pika / Luma / Pixverse)、脚本(LLM)、生图(Midjourney/SD/即梦)、配音(TTS)、对口型、剪辑(剪映/CapCut); (c) 商业/变现 — 国内付费投流(IAP + ROI + 平台分账, 抖音/快手/红果/微信小程序短剧)与海外订阅(ReelShort/DramaBox/ShortMax/GoodShort)两套打法、品牌定制、成本结构; (d) 合规 — 中国《人工智能生成合成内容标识办法》(2025-09 强制 AI 标识)、广电微短剧备案与分类分层审核、版权与肖像权。主战场 = 抖音/快手/视频号/B站 + 海外短剧 App。学派分歧: 纯 AI vs AI+实拍混合、国内投流 vs 海外订阅、视频工具派之争、「AI 短剧是真需求还是伪风口」。不含: 传统实拍短剧制作、通用影视编剧(已有 scriptwriting skill)、短视频信息流投放(已有 short-video-ads skill)、纯 AI 视频底层技术研发。) Master OS — automated mastery of AI 短剧 (AI 微短剧) (从业者视角) — 用 AIGC 工具（AI 视频生成 / AI 脚本 / AI 生图 / AI 配音 / 对口型 / 角色一致性）制作竖屏微短剧并变现的新兴行业。覆盖: (a) 创作工作流 — 选题→剧本/分镜→AI 生图→图生视频(首尾帧/运镜/参考)→配音/对口型→剪辑/调色→成片，其中角色/场景一致性是第一性难题; (b) 工具栈 — 视频生成(可灵 Kling / 即梦 Seedance / 海螺 Hailuo / Vidu / Runway Gen / OpenAI Sora / Pika / Luma / Pixverse)、脚本(LLM)、生图(Midjourney/SD/即梦)、配音(TTS)、对口型、剪辑(剪映/CapCut); (c) 商业/变现 — 国内付费投流(IAP + ROI + 平台分账, 抖音/快手/红果/微信小程序短剧)与海外订阅(ReelShort/DramaBox/ShortMax/GoodShort)两套打法、品牌定制、成本结构; (d) 合规 — 中国《人工智能生成合成内容标识办法》(2025-09 强制 AI 标识)、广电微短剧备案与分类分层审核、版权与肖像权。主战场 = 抖音/快手/视频号/B站 + 海外短剧 App。学派分歧: 纯 AI vs AI+实拍混合、国内投流 vs 海外订阅、视频工具派之争、「AI 短剧是真需求还是伪风口」。不含: 传统实拍短剧制作、通用影视编剧(已有 scriptwriting skill)、短视频信息流投放(已有 short-video-ads skill)、纯 AI 视频底层技术研发。: top builders' mental models, tool stack, current workflows, jargon, and where to keep up. Trigger this skill when the user works on AI 短剧 (AI 微短剧) (从业者视角) — 用 AIGC 工具（AI 视频生成 / AI 脚本 / AI 生图 / AI 配音 / 对口型 / 角色一致性）制作竖屏微短剧并变现的新兴行业。覆盖: (a) 创作工作流 — 选题→剧本/分镜→AI 生图→图生视频(首尾帧/运镜/参考)→配音/对口型→剪辑/调色→成片，其中角色/场景一致性是第一性难题; (b) 工具栈 — 视频生成(可灵 Kling / 即梦 Seedance / 海螺 Hailuo / Vidu / Runway Gen / OpenAI Sora / Pika / Luma / Pixverse)、脚本(LLM)、生图(Midjourney/SD/即梦)、配音(TTS)、对口型、剪辑(剪映/CapCut); (c) 商业/变现 — 国内付费投流(IAP + ROI + 平台分账, 抖音/快手/红果/微信小程序短剧)与海外订阅(ReelShort/DramaBox/ShortMax/GoodShort)两套打法、品牌定制、成本结构; (d) 合规 — 中国《人工智能生成合成内容标识办法》(2025-09 强制 AI 标识)、广电微短剧备案与分类分层审核、版权与肖像权。主战场 = 抖音/快手/视频号/B站 + 海外短剧 App。学派分歧: 纯 AI vs AI+实拍混合、国内投流 vs 海外订阅、视频工具派之争、「AI 短剧是真需求还是伪风口」。不含: 传统实拍短剧制作、通用影视编剧(已有 scriptwriting skill)、短视频信息流投放(已有 short-video-ads skill)、纯 AI 视频底层技术研发。 problems and wants industry-grade thinking, tool selection, or workflow guidance. 触发词：「ai 短剧」「ai 微短剧」「ai short drama」「ai 短剧制作」「ai 短剧工作流」

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。

Suede Labs Google Play delivery skill: ship an Android release end to end from the agent interface, without opening the Play Console. Set up credentials, upload an AAB, promote between tracks, stage or complete a rollout, push per-locale release notes, and prove against the Play Developer API what is actually live. One step stays a Console action by design and the skill hands it to you: granting the service account release access the first time. Use when uploading or promoting an Android build, wiring a Play service account and fastlane supply lanes, fixing per-versionCode changelogs, raising or halting a staged rollout, or answering what version and fraction production is really serving. NOT FOR: planning or building the app itself (use android-app-factory); live listing and keyword audits (use suede-aso); CI required checks (use suede-ci-gate); iOS release (a private Suede Labs companion, not in this pack: ios-app-store-release).

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。

Analytics tracking, interpretation, funnel analysis, product metrics, and ROI measurement. Use when setting up GA4/GTM tracking, interpreting analytics data, analyzing conversion funnels, calculating ROI, or measuring product engagement. Triggers on "analytics," "GA4," "Google Analytics," "conversion tracking," "event tracking," "UTM parameters," "tag manager," "GTM," "tracking plan," "funnel analysis," "conversion rates," "user flow," "cohort analysis," "retention," "product metrics," "North Star metric," "ROI," "break-even," "payback period," "investment analysis," "validate my funnel," "why isn't my funnel converting," or "executive financial report." For A/B test setup, see ab-test-setup.

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。

Guides native Android development with Kotlin, Jetpack Compose, and Views interop. Use when building, rewriting, or reviewing modern Android apps after establishing runtime truth.

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。

Constructs ROIC decomposition with invested capital measurement, operating return analysis, and value creation vs destruction assessment. Use when analyzing ROIC, building capital return frameworks, or assessing value creation.

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。

Comprehensive Android development expert covering Jetpack Compose, Kotlin coroutines/Flow, Architecture Components, Hilt DI, Navigation, testing, performance, Material Design 3, and modern Android patterns (MVI, Clean 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。

Mobile (Android) app recon and analysis — decompile APK, extract secrets, find hardcoded endpoints, exposed Firebase / AWS / API keys, deeplink abuse, intent vulnerabilities, exported components, WebView XSS, MobSF static analysis. Use when target's scope includes Android apps (.apk).

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 a new Android app with SDLC and Play-safe defaults — Kotlin/Compose Gradle project with version catalog, convention plugins, module layout, a targetSdk that meets the Play mandate, R8 on release, and git-ignored signing so it passes the pre-flight checker from day one. Use when starting a new Android project.

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。

One-shot NowStack Mobile Android deployment with GPC Google Play Console CLI. Use when preparing Play Store releases, uploading AABs, syncing listings/screenshots, monitoring rollout health, or submitting Android without EAS Submit.

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。

Find and set up Google Play Console service-account JSON credentials for gpc. Use when Android Play uploads need auth, GPC_SERVICE_ACCOUNT is unknown, or before ns-deploy-android-app/ns-android-beta.

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。

Expert guidance for working with Wear OS Compose Material3. Use this skill when creating, updating or migrating Wear OS projects. This includes the androidx.wear.compose.material3, androidx.wear.compose.foundation and androidx.wear.compose.navigation3 libraries. Also working with core components such as AppScaffold, ScreenScaffold and TransformingLazyColumn. Migration from earlier versions such as Material 2.5 and Horologist.

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。

Decompile, analyze, and reverse engineer Android applications (APKs). Utilize tools like JADX, Apktool, and dex2jar to extract source code (Java/Kotlin), analyze manifest configurations (Intents, Activities), and identify hardcoded secrets or insecure API endpoints.

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。

Android pentesting playbook. Use when testing Android applications for SSL pinning bypass, exported component abuse, WebView vulnerabilities, intent redirection, root detection bypass, tapjacking, and backup extraction during authorized mobile security assessments.

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。

Scan a project to detect configured quality and testing tools across JVM (Gradle/Maven), Android (AGP/Compose/KMP), Node.js/TypeScript, and Python ecosystems. Cross-reference against research-backed recommendations and assist with setup. Auto-detects project type(s) including monorepos with mixed ecosystems. Use when user asks to "audit tooling", "recommend tools", "quality stack", "what tools am I missing", "setup eslint", "setup detekt", "add coverage", "add ruff", "configure CI quality pipeline", "scan project tools", "tooling audit", "android tooling", "android quality", "compose testing", "kmp testing", or "screenshot testing".

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。