2.5万 件 ・ 関連度順
概要と使いどころ
[omh] Oversized or tangled UI component: behavior-preserving refactor of UI code - preview the full change plan first, apply as a second explicit step, and work impact-ordered from state architecture down to naming polish. Use when the user says: frontend-refactor, front-refactor, frontend refactor, refactor this component, refactor the component, refactor my component, component refactor, react refactor.
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。
Writes the character-performance layer of a video prompt as behavior under pressure, not displayed emotion — objective, obstacle, tactics, beats, subtext, listening, body/status/proxemics, and mandatory eye life. Produces a reusable 150–220-word acting master profile per character plus a per-scene rewrite of it, and a locked voice prompt. Use whenever a prompt needs acting, performance, or emotion direction; whenever characters read wooden, dead-eyed, or 'AI'; whenever a character must stay themselves across many shots; or when the user asks for character behavior, mannerisms, tics, a gait, or how someone reacts. Pairs with higgsfield-facs (muscle-level AU codes) and higgsfield-seedance (the prompt the paragraph goes into).
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Pre-production story-and-character development for Higgsfield projects — the upstream layer that decides WHAT to prompt before any model runs. Use when the user wants to build a character, design a world, develop a story or premise, create a character sheet / character bible / story bible, lock a visual style or 'visual DNA', plan a multi-shot narrative with consistent characters, or says things like 'help me design a character', 'build the world', 'I need a backstory', 'make this character consistent across shots', 'develop my film/series concept', or 'I keep getting generic AI characters'. Routes the locked outputs into higgsfield-prompt + the right model. Adapted from Higgsfield's official character-design framework by @vavavinca.
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。
Character consistency across AI-generated images with reference sheets and LoRA techniques. Covers turnaround views, expression sheets, color palettes, and style consistency tricks. Use for: character design, game art, illustration, animation, comics, visual novels. Triggers: character design, character sheet, character consistency, character reference, turnaround sheet, expression sheet, character art, consistent character, character concept, reference sheet, character creation, oc design, character bible
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。
欧盟 AI 法案知识引擎——以 70 份官方欧盟来源文件为基础的权威监管问答（包括 2026 年委员会关于 Art. 6 高风险分类的指南草稿——一般原则、附件 I、附件 III）。基于完整条例文本、委员会指南、EDPB/EDPS 意见、《实践守则》、协调标准、FRIA 指南、事件模板和行业特定指引，以条款级引用回答关于 AI 法案的任何问题。当用户询问"EU AI Act"、"AI Act regulation"、"Regulation 2024/1689"、"KI-Verordnung"、要求"explain an AI Act article"、"what does Article X say"、"AI Act requirements"、"AI Act penalties"、"AI Act timeline"、"GPAI obligations"、"high-risk AI"、"prohibited AI practices"、"AI Act and GDPR"、"fundamental rights impact assessment"、"AI Act standards"、"AI Act national implementation"、"AI literacy"、"serious incident reporting"、"Digital Omnibus"、"AI regulatory sandbox"，或任何关于欧盟人工智能监管的问题时，应使用本技能。德语术语同样触发："KI-Gesetz"、"KI-Verordnung"、"Hochrisiko-KI"、"KI-Kompetenz"、"Grundrechte-Folgenabschaetzung"、"GPAI-Verhaltenskodex"。对于产出分类决定而非知识答案的评估工作流，提议在会话内运行结构化风险等级分类。
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Design and review Action Scheduler jobs in WordPress plugins using Action Scheduler 3.9.x public APIs - async, single, recurring, and cron-expression actions; action_scheduler_init load timing; hook/args/group naming; unique and priority parameters; idempotent callbacks; chunked workloads; activation/deactivation cleanup; WooCommerce-bundled or standalone dependency usage; admin and WP-CLI debugging; queue runner limits; and safe operational troubleshooting. Use when a plugin schedules background jobs with as_enqueue_async_action, as_schedule_single_action, as_schedule_recurring_action, as_schedule_cron_action, as_get_scheduled_actions, or integrates with WooCommerce background queues.
日本語の概要は準備中です。原文の説明を表示しています。
Interactive Domain-Driven Design modeling sessions based on DDD Distilled (Vaughn Vernon) and Domain Modeling Made Functional (Scott Wlaschin). Guides users through strategic and tactical design via 13 phases: discover, storming, contexts, mapping, aggregates, events, validate, glossary, workflows, types, simulate, publish, sync. Phase 13 (sync) reconciles divergences found by --analyze between the domain model and the implementation — judging whether the model or the code is authoritative, then planning and implementing the fix and re-aligning the model. Phases 9-11 take the conceptual model from phases 1-8 and produce workflow pipeline designs, compilable type definitions (TypeScript, Kotlin, Scala, Rust, C#, F#), workflow verification via type-level tests, and automatic UI field enumeration from the domain model. Phase 12 (publish) renders all docs/domain artifacts into one self-contained, cross-linked HTML site (separate files joined by a shared nav). Each phase is an interactive dialogue where AI acts as facilitator and domain expert challenger. Use when: "DDD", "ドメイン設計", "ドメインモデリング", "Event Storming", "Bounded Context", "集約設計", "ユビキタス言語", "コンテキストマップ", "ワークフロー設計", "パイプライン設計", "ステップ分割", "中間型", "型駆動フロー", "型駆動設計", "Domain Modeling Made Functional", "Railway Oriented Programming", "入力画面項目", "フォーム項目洗い出し", "UI 項目抽出", "HTMLにまとめる", "ドキュメントサイト化", "成果物を1つのHTMLに", "publish", "モデルと実装の差異", "差異解消", "実装との乖離", "モデルとコードを一致", "差異の実装計画", "sync", "複数フェーズを逐次実行", "フェーズをまとめて実行", "/ddd run", "サブコマンド一覧", "/ddd list", "/ddd", "ドメイン分析したい", "モデリングしたい", or any domain design / type modeling activity.
Create, implement, deploy, and debug Adobe Runtime actions with consistent layout, validation, and error handling. Use this skill whenever the user needs to add actions to an App Builder project, understand action structure (params, response format, web/raw actions), configure actions in the manifest, use App Builder SDKs (State, Files, Events, database), deploy and invoke actions via CLI, debug action issues, or implement patterns such as webhook receivers, custom event providers, journaling consumers, large payload redirects, action sequence pipelines, and Asset Compute workers. Also trigger when users mention serverless functions in Adobe context, action logging, IMS authentication for actions, or cron-style scheduled actions.
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。
Create, implement, deploy, and debug Adobe Runtime actions with consistent layout, validation, and error handling. Use this skill whenever the user needs to add actions to an App Builder project, understand action structure (params, response format, web/raw actions), configure actions in the manifest, use App Builder SDKs (State, Files, Events, database), deploy and invoke actions via CLI, debug action issues, or implement patterns such as webhook receivers, custom event providers, journaling consumers, large payload redirects, action sequence pipelines, and Asset Compute workers. Also trigger when users mention serverless functions in Adobe context, action logging, IMS authentication for actions, or cron-style scheduled actions.
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。
Reactで動画を作る際に、Remotionの実装例と制作ルールを参照できるスキル。アニメーション、音声、字幕、グラフ、3D表現や場面転換を扱います。
ReactやNext.jsのアニメーション設定を共通化し、動きを減らす利用者設定や端末性能、サーバー描画との整合性に配慮した実装の土台を整えるスキル。
YouTube channel business email and contact extractor: accepts a channel id (UCxxx), handle (@name), or URL; navigates the channel About view; extracts the business email from the description text plus full channel metadata (name, id, country, subscriber count, view count, video count, joined date, external links classified as social/aggregator/personal). When the description has no email, follows non-social outbound links (personal site, business site, link aggregator) and scans those pages for an email so creators who place their inquiry email on their own site are still covered. Use when user mentions youtube channel email, youtube business email, youtube creator email, youtube contact email, youtube channel contact, youtube channel scraper email, youtube email finder, youtube influencer email, youtube outreach, youtube sponsorship contact, youtube partnership email, scrape email from youtube, get email from youtube channel, find youtube creator contact, extract youtube channel emails in bulk, youtube channel inquiry email, business inquiries youtube, brand deal email youtube, lead generation youtube creators, youtube creator outreach list, youtube channel about email, ytInitialData about, youtube channel id to email, youtube handle to email, youtube channel url to email, youtube about page scraper, channel about page email, youtube channel metadata, youtube channel social links, youtube channel external links, youtube channel description email, follow channel website for email, scrape creator personal website from youtube. Also applies to building creator/KOL contact databases for influencer marketing, agency lead lists, sponsorship prospecting, brand-creator collaboration sourcing, and enriching existing YouTube channel lists with contact info.
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。
React 19 performance patterns and composition architecture for Vite + Cloudflare projects. 50+ rules ranked by impact — eliminating waterfalls, bundle optimisation, re-render prevention, composition over boolean props, server/client boundaries, and React 19 APIs. Use when writing, reviewing, or refactoring React components. Triggers: 'react patterns', 'react review', 'react performance', 'optimise components', 'react best practices', 'composition patterns', 'why is it slow', 'reduce re-renders', 'fix waterfall'.
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。
This skill should be used when the user asks to "create a character", "update a character", "add a character", "build a family tree", "character relationships", "character timeline", "character arc", "character profile", "relationship graph", "name a character", or needs to manage characters in a story project. NOT for recording or checking character voices (use voice-style), rewriting dialogue so the voices differ (use line-editing), or a thematic arc's lie, truth, and arc type (use theme-craft).
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 configuring Slack-specific Agentforce capabilities AFTER the basic Slack deployment is complete: enabling Slack-native actions, managing public vs. private action scope, mapping Salesforce-to-Slack user identity, or troubleshooting Slack action failures. Triggers: 'add General Slack Actions topic to agent', 'configure canvas creation for Agentforce in Slack', 'Agentforce private action requires user identity mapping', 'Slack agent cannot send DMs after deployment', 'Agentforce Look Up User action not working in Slack', 'how to map Slack users to Salesforce identities for private actions'. NOT for the initial Slack OAuth install, DM vs channel-mention mode, or core agent setup — use agentforce/agent-channel-deployment. NOT for sending a Slack message or creating a channel from a Flow — use flow/flow-for-slack.
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Extracts structured data from large sets of legal documents into tabular format for review, analysis, and reporting. Processes contracts, agreements, correspondence, filings, and other legal documents in bulk — extracting key terms, dates, parties, obligations, risks, and custom fields into organized tables. Use when conducting due diligence document review, bulk contract extraction, compliance audits across document sets, lease portfolio analysis, employment agreement review, regulatory filing review, or any task requiring structured extraction from multiple documents. Trigger keywords: tabular review, bulk extraction, document review table, data room review, batch document analysis, contract extraction, portfolio review, structured extraction, document comparison table.
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。
既存コード・既存関数・既存モジュールの暗黙契約を抽出するスキル。 戻り値型、副作用、例外、前提条件、境界条件、環境依存挙動を確認し、 reuse前のリスクを可視化する。名前やコメントではなく実際の挙動から 契約を読み取り、不一致を分類し、再利用可否を判定する。 Use when reusing legacy code, verifying actual behavior of existing functions, or extracting implicit contracts before implementation. Covers: hidden contract, implicit contract, reuse risk, legacy behavior, 暗黙契約, 既存資産調査. Analyzes return types, side effects, exceptions, preconditions, boundary conditions, and environment-dependent behavior.
Swiftのactorで共有データへのアクセスを管理し、メモリ内キャッシュとJSONファイル保存を組み合わせて、同時アクセス時のデータ競合を避ける保存処理を設計するスキル。
ReactやNext.jsで、ドラッグ操作、スワイプ、文字やSVGのアニメーションを実装します。複数段階の動きや読み込み表示まで扱い、用途に合うAPI選びも支えるスキル。
Create and engage with LinkedIn content using the Curviate CLI. Covers `post` (get, create, react, unreact, reactions, delete, save, saved, unsave, user-posts, user-reactions), `comment` (list, add, reply, edit, delete, replies, react, unreact, reactions, user), `feed home` and `notification`. Carries the write-versus-read reaction vocabularies, post-identifier forms, and the pagination trap on reaction lists. Use when posting, commenting, reacting, reading the home feed, or working through notifications.
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。
React Email renderer for json-render that turns JSON specs into HTML or plain-text emails using @react-email/components and @react-email/render. Use when working with @json-render/react-email, building transactional or marketing emails from JSON, creating email catalogs, rendering AI-generated email specs, or when the user mentions react-email, HTML email, or transactional email.
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。
This skill helps users automatically extract YouTube video transcripts and metadata in batch via the BrowserAct API. The Agent should proactively apply this skill when users express needs like batch extract full transcripts from YouTube videos for specific keywords, scrape YouTube subtitles for a list of videos, get batch video metadata and likes counts for analysis, automate YouTube search and subtitle extraction, collect multiple video transcripts published this week, download bulk YouTube video subtitles without writing crawler scripts, build a dataset of transcripts from top YouTube videos, extract YouTube video URLs and publisher info in batch, gather full video content for AI summarization pipelines, monitor recent YouTube videos and extract their transcripts, batch retrieve structured subtitle data for media research, extract transcripts from trending YouTube content automatically.
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。
This skill helps users automatically extract official website and social media profiles. Agent should proactively apply this skill when users express needs like search for official website and social media contacts of a company, find YouTube and LinkedIn profiles by company name, extract social media links from a specific website URL, discover a company's X and Facebook presence, gather business contact details using their brand name, retrieve TikTok and Instagram links from a target website, track competitor social media strategy, extract multi-platform social links for lead generation, find official contact channels of local businesses, collect canonical profile URLs for outreach campaigns, or build a contact database from Yellow Pages leads.
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。
Extract all 3rd-party (non-owned) addresses from a TRES Finance environment and export them as an XLSX workbook to help users build their contacts list. Use this skill whenever the user wants to export, list, or identify 3rd-party addresses, counterparties, or external addresses from their transactions — even if they don't say 'contacts'. Also trigger when the user asks to prepare a contacts import file, find unknown addresses, or build an address book from transaction history. Do NOT trigger for viewing existing contacts or searching the address book — only for extracting NEW 3rd-party addresses from transaction data.
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。