6,840 件 ・ 関連度順
概要と使いどころ
Use when the user wants to start or reposition a Xiaohongshu account but is asking "what should I post" before clarifying monetization, timing, personal IP direction, or trust path. Trigger phrases: "小红书定位", "不知道做什么赛道", "怎么变现", "个人IP", "账号方向", "XHS positioning", "monetization first". Do not use for drafting a specific post; use topic or copy skills after positioning is clear.
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 the user wants to start or reposition a Xiaohongshu account but is asking "what should I post" before clarifying monetization, timing, personal IP direction, or trust path. Trigger phrases: "小红书定位", "不知道做什么赛道", "怎么变现", "个人IP", "账号方向", "XHS positioning", "monetization first". Do not use for drafting a specific post; use topic or copy skills after positioning is clear.
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。
Assists with building custom interactive data visualizations using D3.js. Use when creating charts, graphs, maps, force layouts, or hierarchical diagrams that require fine-grained control beyond what charting libraries provide. Trigger words: d3, data visualization, chart, svg, scales, force graph, treemap, choropleth.
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。
When the user wants to set up authentication and authorization for a web application. Use when the user mentions "auth," "login," "OAuth," "SSO," "single sign-on," "role-based access," "RBAC," "permissions," "user roles," "access control," "authentication," or "authorization." Covers OAuth 2.0 provider integration, session management, and role/permission architecture. For JWT-specific tasks, see jwt-handler. For security review, see security-audit.
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 waveform visualizations from audio files. Use when a user asks to create waveform images, build audio player visualizations, generate waveform data for web players, create podcast episode previews, build audio thumbnails, render waveform PNGs for social media, extract peak data as JSON, or integrate waveform generation into audio processing pipelines. Covers audiowaveform CLI, JSON/binary data output, and web player 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。
Optimize mobile app listings for discovery and conversion in Apple App Store and Google Play. Use when tasks involve ASO keyword research, title and subtitle optimization, screenshot and preview video design, A/B testing store listings, review management, localization for international markets, tracking keyword rankings, or improving download conversion rates. Covers both iOS and Android store algorithms and best practices.
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。
Automate online content creation and monetization with AI — blog posts, videos, social media, affiliate marketing, and digital products. Use when: building passive income streams with AI, automating content marketing, scaling content production.
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 reducing model size, improving inference speed, or deploying to edge devices - covers quantization, pruning, knowledge distillation, ONNX export, and TensorRT optimizationUse when ", " mentioned.
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。
World-class landing page expertise combining conversion rate optimization science, persuasive design psychology, and the craft of pages that turn visitors into customers. Landing pages are where marketing meets reality—the moment of truth where visitors decide. Great landing pages aren't just pretty—they're conversion machines. The best landing page designers understand that every element must earn its place, that friction is the enemy, and that the goal isn't to impress—it's to convert. Speed, clarity, and persuasion in perfect balance. Use when "landing page, conversion page, lead capture, signup page, pricing page, hero section, above the fold, CTA design, conversion rate, page optimization, lead gen page, squeeze page, sales page, landing-pages, conversion, CRO, design, optimization, lead-generation, signups, forms" mentioned.
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。
Strategies for managing LLM context windows including summarization, trimming, routing, and avoiding context rotUse when "context window, token limit, context management, context engineering, long context, context overflow, llm, context, tokens, memory, summarization, optimization" mentioned.
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 patterns for Clerk auth implementation, middleware, organizations, webhooks, and user syncUse when "adding authentication, clerk auth, user authentication, sign in, sign up, user management, multi-tenancy, organizations, sso, single sign-on, clerk, authentication, auth, user-management, multi-tenancy, organizations, sso, oauth" mentioned.
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。
Keeping codebases healthy, performant, and maintainable - refactoring, performance optimization, and technical debt managementUse when "refactor, optimize, performance, technical debt, cleanup, architecture, speed up, bundle size, memory leak, slow query, code smell, complexity, dead code, performance, refactoring, optimization, technical-debt, architecture, cleanup, bundle, memory" mentioned.
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。
Systematic code cleanup and refactoring agent that identifies and fixes code quality issues without changing functionality. Focuses on: - Dead code removal - Import organization - Type improvements - Consistent patterns - File organization Use when "User asks to "clean up" code, User mentions "dead code" or "unused", User wants to "organize imports", User asks about "code quality", User wants to "refactor" without changing behavior, Code review identifies cleanup items, After major feature completion, cleanup, refactoring, code-quality, maintenance, organization" mentioned.
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。
Building AI-powered personalization systems: recommendation engines, collaborative filtering, content-based filtering, user preference learning, cold-start solutions, and LLM-enhanced personalized experiences. Use when "recommendation system, personalization, collaborative filtering, content-based filtering, user preferences, recommend, suggestions, for you, similar items, you might like, " mentioned.
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。
Deep technical SEO auditing covering crawlability, indexation, Core Web Vitals, site architecture, canonicalization, structured data validation, and performance optimization. Use when the user asks about technical SEO, site audits, crawl issues, indexation problems, Core Web Vitals, page speed, or site architecture optimization. For content-focused SEO, see the seo-content-writer skill.
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。
Conversion Rate Optimization auditing for landing pages, signup flows, checkout funnels, forms, and CTAs. Identifies friction points, runs heuristic evaluations, produces prioritized recommendations using ICE/PIE frameworks. Use when the user asks about conversion optimization, funnel analysis, landing page audits, form optimization, CTA testing, or checkout improvement.
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。
Answer Engine Optimization (AEO) and Generative Engine Optimization (GEO) specialist. Optimize content and websites to appear in AI-generated answers from ChatGPT, Perplexity, Claude, Google AI Overviews, and other LLM-powered search experiences. Use when the user asks about AI search optimization, AEO, GEO, AI Overviews, appearing in AI answers, LLM citations, or optimizing for generative search.
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。
Authentication & Authorization Implementation Patterns workflow skill. Use this skill when the user needs Build secure, scalable authentication and authorization systems using industry-standard patterns and modern best practices and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
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。
Authentication & Authorization Implementation Patterns workflow skill. Use this skill when the user needs Build secure, scalable authentication and authorization systems using industry-standard patterns and modern best practices and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
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。
application-performance-performance-optimization workflow skill. Use this skill when the user needs Optimize end-to-end application performance with profiling, observability, and backend/frontend tuning. Use when coordinating performance optimization across the stack and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
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。
Authentication & Authorization Implementation Patterns workflow skill. Use this skill when the user needs Build secure, scalable authentication and authorization systems using industry-standard patterns and modern best practices and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
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。
application-performance-performance-optimization workflow skill. Use this skill when the user needs Optimize end-to-end application performance with profiling, observability, and backend/frontend tuning. Use when coordinating performance optimization across the stack and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
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。
SEO 专家 (SEO (search engine optimization)) Master OS — automated mastery of SEO (search engine optimization): top builders' mental models, tool stack, current workflows, jargon, and where to keep up. Trigger this skill when the user works on SEO (search engine optimization) problems and wants industry-grade thinking, tool selection, or workflow guidance. 触发词：「SEO」「搜索引擎优化」「排名」「外链」「页面优化」
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。
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」
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。