「ai」の検索結果

AIの回答で引用されることを目指し、記事の根拠や著者情報、構造を点検・改善します。監査レポートの作成に加え、見つけた引用を記録し、ページ別の状況を集計します。

• 既存記事の根拠や信頼性の点検
• AI回答向けの記事構造と引用の整理
• 各AIサービスで見つけた引用の記録

Managed Service for Apache Airflow向けに処理の順序と依存関係を定めるDAGを作成・拡張し、環境の確認からコードの検証まで支援します。

• マネージド環境向けDAGの新規作成
• 既存ワークフローへのタスク追加
• 配置前にDAGの読み込みを検証したいとき

AIが生成する画面や動画のJSON定義に、構造のルールと部品カタログを用意するスキル。プロンプト作成、逐次生成の処理、状態連携、定義の検証・修正を支援します。

• AI画面生成で使う部品を定義したいとき
• JSON生成・編集用プロンプトの作成
• AI応答から画面定義を逐次組み立てたいとき

機械学習の学習ジョブをローカルやリモートのGPUで実行します。空き状況の確認、コード同期、ログ保存、起動確認を行い、設定に応じて実験指標も記録します。

• 空いているリモートGPUで学習したいとき
• CUDAやMPSでローカル実験したいとき
• W&Bで学習指標を記録したいとき

LI.FI REST API for cross-chain and same-chain token swaps, bridging, DeFi deposits (Composer), yield discovery (Earn), and intent-based execution (Intents). USE THIS SKILL WHEN USER WANTS TO: - Swap tokens between different blockchains (e.g., "swap USDC on Ethereum to ETH on Arbitrum") - Bridge tokens to another chain (e.g., "move my ETH from mainnet to Optimism") - Swap tokens on the same chain with best rates (e.g., "swap ETH to USDC on Polygon") - Find the best route or quote for a token swap across chains - Deposit into DeFi vaults/lending/staking in one click, including cross-chain (Composer: Aave, Morpho, Pendle, EtherFi, Yearn, etc.) - Discover yield opportunities, vault APY/TVL data, or track DeFi positions (Earn) - Execute gasless or intent-based transfers via a solver network (LI.FI Intents) - Move stablecoins cheaply with optimized defaults (stablecoin preset) - Build multi-chain payment flows (accept any token, settle in specific token) - Check supported chains, tokens, bridges, or gas prices - Track status of a cross-chain transaction, recover from failed/partial transfers - Query transfer history/analytics or withdraw collected integrator fees - Build backends, bots, or AI agents (any language) that need cross-chain functionality

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 content and site structure for AI-driven search experiences including AI overviews, large language model citations, generative answer engines, and AI assistants. Use this skill whenever the user wants to optimize for AI search, get cited by language models, appear in AI overviews, build llms.txt, structure content for AI extraction, or future-proof their SEO for the shift from blue links to AI answers. Triggers on AEO, GEO, AI search, AI SEO, AI overview, generative search, LLM optimization, llms.txt, AI citation, ChatGPT search, Perplexity, Gemini, Claude search, AI assistant optimization, answer engine. Also triggers when the user expresses concern about AI eating their organic traffic or wants to understand how to remain visible as search shifts.

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。

Dung khi can FILE HTML email chay duoc tren moi email client — cau truc table, CSS inline, be ngang 600px, dark mode, spec rieng cho Gmail, Outlook, Apple Mail va checklist test truoc khi gui. Mau va font lay tu brand guideline. Kich hoat khi user nhac 'email HTML', 'code email', 'template email', 'email responsive', 'email vo bo cuc tren Outlook', 'anh trong email khong hien', 'paste vao Brevo bi vo'. Khong dung cho — noi dung va chuoi gui email thi dung skill 14-email-marketing; trang web dat sau nut CTA thi dung skill 12-brief-landing-page; quy dinh mau va font thuong hieu thi dung skill 46-brand-guideline.

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。

Classifies sensitive data in AI/ML training datasets including bias detection for Art. 9 categories, data card documentation, provenance tracking, and consent verification for model training. Keywords: AI training data, ML dataset, bias detection, data card, model training, Art 9, consent, GDPR AI.

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。

Test AI/LLM features that ship in your product. Covers prompt regression testing, response quality evaluation, tool-call validation, hallucination and RAG grounding checks, nondeterministic-output strategies, red-team/safety scans, eval frameworks, and agent-as-target injection (indirect injection via tool output / RAG / scan reports, self-propagating payloads, data exfiltration via an agent) plus a bundled detector for untrusted content. Use when: "test our LLM feature," "prompt regression test," "eval framework," "hallucination test," "RAG grounding," "nondeterministic output," "AI feature testing," "red-team our chatbot," "indirect prompt injection," "agent reading untrusted tool output," "production AI quality." Not for: using AI to generate your own test code — use ai-test-generation. Not for: classifying CI failures with AI — use ai-bug-triage. Not for: EU AI Act / GDPR conformity of an AI feature — use compliance-testing. Not for: canary/flag rollout of an AI feature — use testing-in-production. Related: ai-test-generation, ai-qa-review, api-testing, compliance-testing, security-testing, risk-based-testing, test-data-management.

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。

The enhancement layer for AI-generated content. This skill covers AI-powered visual effects, compositing, upscaling, restoration, and post-production magic—turning raw AI output into polished, professional content. AI generation gets you 80% of the way. Visual effects get you the remaining 20% that separates "clearly AI" from "how did they do that?" This skill covers ComfyUI workflows, Runway's AI tools, intelligent upscaling, rotoscoping, color grading, and the integration of AI elements into traditional footage. The practitioners of this skill are technical artists who understand both traditional VFX workflows and the new AI-native approaches that are revolutionizing post-production. Use when "AI visual effects, VFX, upscale, upscaling, composite, rotoscope, background removal, color grade, inpaint, outpaint, style transfer, enhance, ComfyUI, post-production AI, vfx, visual-effects, compositing, upscaling, post-production, comfyui, enhancement, color-grading" 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。

健身私教 — 为客户设计、指导和推进个性化训练方案的专业工艺, 有别于团课教练、物理治疗师或运动队教练: (a) 训练方案设计与周期化 (线性 / 波动 / 板块周期化 Bompa & Haff; NSCA《力量训练与体能训练精要》行业圣经; Schoenfeld 肌肥大机制; Helms/Valdez/Morgan 肌肉与力量金字塔; 共轭训练法 Simmons/Westside; Starting Strength 线性渐进 Rippetoe; Renaissance Periodization 训练量地标 Israetel); (b) 动作评估与筛查 (FMS 功能性动作筛查 Gray Cook; NASM CEx 纠正性训练连续体 抑制-拉伸-激活-整合; 关节逐级方法 Cook/Boyle; 姿势与代偿模式识别; PAR-Q+ 与 ACSM 运动前筛查指南); (c) 教学与指令 (外部 vs 内部注意焦点 Wulf; 差异化学习 Schöllhorn; 约束导向方法 Davids/Renshaw; RIR/RPE 自动调节 Zourdos/Helms; 动机访谈在健身中的应用 Rollnick/Miller); (d) 营养指导的执业边界 (宏量营养素周期化; Precision Nutrition / ISSN 立场声明的循证建议; 边界: 私教提供一般性指导, 医学营养治疗需 RD/RDN 资质; 能量可用性与 RED-S Mountjoy IOC 共识); (e) 商业与客户管理 (客户留存与方案依从; 咨询与销售伦理; 线上教练平台与规模化 Goodman PTDC; 方案交付工具 TrueCoach/Trainerize/TrainHeroic; 课时定价模型; 独立教练 vs 健身房雇员); (f) 专项方向 (运动表现 CSCS; 纠正性训练 CES; 老年健身 SFN; 产前产后; 青少年 YSCA; 适应性 / 无障碍健身; 健美 / 形体; 力量举; 耐力运动 S&C)。诚实处理: 兄弟科学 vs 循证之争, 补剂行业利益冲突, 合成代谢激素使用现状 (诚实但不推广: 匿名调查约 30% 有经验男性健身者使用过 — 业内估, Kanayama/Pope/Hudson 记录健康风险, WADA 禁用清单用于竞技语境), 体型焦虑与进食障碍风险 (男性私教肌肉变形症, 正食症倾向, NEDA 负责任语言指南), 健身房掠夺性销售 (高压推销、误导性对比照、长约锁定), 执业边界违规 (私教诊断伤病或超范围开饮食处方), 线上教练认证造假 (周末速成、未经认可的认证)。不含: 物理治疗 / 康复治疗 (持照医疗职业), 不含: 团课教练 (Les Mills/Zumba/CrossFit 分馆教练 — 相邻但不同), 不含: 精英竞技运动教练 (团队项目周期化, 不同职业路径), 不含: 营养作为主业 (临床营养学需单独资质)。 (Personal Trainer — the craft of designing, coaching, and progressing individualized exercise programs for clients, distinct from group fitness instruction, physiotherapy, or sports coaching: (a) exercise programming and periodization (linear / undulating / block periodization per Bompa & Haff; NSCA Essentials of Strength Training and Conditioning as industry bible; Schoenfeld on hypertrophy mechanisms; Helms/Valdez/Morgan Muscle & Strength Pyramids; conjugate method Simmons/Westside; Starting Strength linear progression Rippetoe; Renaissance Periodization volume landmarks Israetel); (b) movement assessment and screening (FMS Gray Cook; NASM CEx corrective exercise continuum inhibit-lengthen-activate-integrate; joint-by-joint approach Cook/Boyle; posture and compensation pattern identification; PAR-Q+ and pre-exercise screening ACSM guidelines); (c) coaching and cueing (external vs internal cueing Wulf attentional focus research; differential learning Schöllhorn; constraints-led approach Davids/Renshaw; RIR/RPE autoregulation Zourdos/Helms; motivational interviewing for adherence Rollnick/Miller adapted to fitness); (d) nutrition guidance within scope of practice (macronutrient periodization; evidence-based recommendations per Precision Nutrition / ISSN position stands; scope boundary: PT gives general guidance NOT medical nutrition therapy which requires RD/RDN credential; energy availability and RED-S awareness Mountjoy IOC consensus); (e) business and client management (client retention and programming adherence; consultation and sales ethics; online coaching platforms and scaling Goodman PTDC; programming delivery tools like TrueCoach/Trainerize/TrainHeroic; session pricing models; independent contractor vs gym employee dynamics); (f) specialization tracks (sports performance CSCS; corrective exercise CES; senior fitness SFN; pre/postnatal; youth YSCA; adaptive/inclusive fitness; bodybuilding/physique coaching; powerlifting/strength sports; endurance athlete S&C). Evidence-based practice as the core ethos: Schoenfeld/Krieger meta-analyses on volume-response, Helms systematic reviews on protein/resistance training, Brad Schoenfeld's MASS journal bridging research to practice, Menno Henselmans and Eric Helms as practitioner-researchers, Greg Nuckols Stronger By Science as translation layer. Chinese fitness industry context: rapid growth of personal training in commercial gyms (Keep/Lefit/Megafit chains), NSCA-CPT and ACE-CPT as dominant certifications alongside local CSFA (China Sport and Fitness Association) credential, '体适能' (physical fitness) as formal term, online coaching boom post-COVID, PT business models in tier 1-3 cities diverge sharply. Honest treatment of: bro science vs evidence-based d

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 outbound email strategy skill for cold outreach, email sequences, and multi-channel campaigns. Use when writing cold emails, creating outreach sequences, optimizing response rates, designing follow-up cadences, or building outbound campaigns. Covers prospecting, personalization frameworks, sequence design, subject lines, response handling, compliance, email design, HTML email templates, email layout, email marketing, newsletter design, drip campaigns, email subject lines, email headers, and cold email templates for discovery calls and SDR workflows.

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。

"Apply AI ethics frameworks (fairness, accountability, transparency, privacy) to evaluate AI systems for algorithmic bias, explainability gaps, and value alignment failures. Use this skill when the user needs to audit an AI system for ethical risks, design fairness constraints, assess explainability requirements, or when they ask 'is this AI system fair', 'how do we detect algorithmic bias', 'what are the ethical implications of this AI deployment', or 'how do we make this model explainable to stakeholders'.".

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。

End-to-end Gaia curation pipeline orchestrator. Routes each phase to its dedicated skill and keeps you on track from first discovery to closed intake. Use when starting a fresh curation run or when you want a single skill to hold the sequence together without duplicating each phase's instructions. Trigger phrases: "/gaia-full-pipeline", "run the full pipeline", "start a curation from scratch", "take this skill through the full pipeline", "curate end-to-end", "pipeline run", "full curation flow". Fused from: gaia-curate + gaia-curate-chain + gaia-curate-dynamic + gaia-curate-trending + gaia-bot-curate + gaia-draft-curate + ev-pipeline + gaia-ingest + gaia-intake-close.

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。

Draft professional emails (MoM, status updates, decisions, stakeholder communication) and save as a draft in your email provider. Use this skill after a meeting, decision, spec update, or any event that needs email communication. Trigger phrases: "send MoM email", "draft meeting minutes email", "MoM email for [meeting]", "create MoM draft", "email the meeting notes", "draft MoM", "draft email", "send update email", "draft email for [topic]". Reads context from meetings, specs, tickets, and conversation. Looks up attendee emails from a local team directory, formats as HTML email, and pushes to the email provider as a draft (never sends).

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。

Tailwind CSS (ユーティリティファースト CSS フレームワーク, v4) のリファレンス。 @theme, @source, @utility, @custom-variant, @apply, @import "tailwindcss", Preflight, CSS-first 設定, @tailwindcss/vite, @tailwindcss/postcss, @tailwindcss/cli, upgrade ツール, variants, arbitrary values, container queries。 Chakra UI のトークン・style props は chakra-ui スキルの担当。

OpenAI API (developers.openai.com) の運用・管理リファレンス。 Administration API, admin API keys, RBAC, roles, groups, invites, projects, spend limits, audit logs, usage / costs API, Terraform provider (openai/openai), workload identity federation (WIF, OIDC / SPIFFE / X.509 / Idira), moderation API, misalignment monitoring, safety alerts / cases, safety best practices, under-18 guidance, ZDR Private Safety Processing (PSP), production best practices, deployment checklist, cost / latency optimization, fast / ultrafast mode, Realtime API costs。 rate limits・error handling・prompt caching・webhooks は openai-api-core、Codex WIF・管理は openai-codex。

Swift 6.2への移行や並行処理の設計で、MainActorによる状態の保護、重い処理の明示的なバックグラウンド実行、コンパイルエラーの解消を支援するスキル。

• 既存アプリをSwift 6.2へ移行したいとき
• データ競合のコンパイルエラー解消
• 重い計算をバックグラウンドへ移したいとき

Ruby on Railsアプリの実装・レビューで、業務処理や検索、フォームの責務を整理するスキル。データ取得の無駄を防ぎ、バックグラウンド処理や画面部品の設計も支援します。

• 大きなcontrollerの業務処理の分離
• 複数モデルを更新するフォームの設計
• 重複する検索やデータ取得の見直し

AIが修正したコードで同じ不具合が再発しないよう、APIの必須項目や処理分岐を自動テストで確認し、テストとビルドを先に行うレビュー手順を整えるスキル。

• バグ修正後に再発防止テストを足したいとき
• API応答の必須項目を確認したいとき
• テスト用と本番用の応答を見直したいとき

画像・動画の生成やAI検索など、150以上のクラウドAIアプリを共通のコマンドで探して実行します。ローカルの画像や音声を使う加工・生成にも対応します。

• 文章から画像を生成したいとき
• 手元の画像を動画にしたいとき
• 写真と音声でアバター動画を作りたいとき

変更候補のコードをローカルで検証し、重大な失敗・診断上の差分・証拠不足を区別して報告するスキル。基準コードと比較し、CIで未確認の検証項目も明示します。

• 保守担当者によるPR候補の事前確認
• 失敗を正確な基準コードと比較したいとき
• 重大な失敗と診断上の差分の整理

Campaign Cleanerの操作をAIエージェントに任せるためのスキル。利用可能なツールと最新の入力形式を調べ、接続確認から実行まで進めます。

• 目的に合う操作ツールを探したいとき
• 実行前にサービス接続を確認したいとき
• 複数の操作をまとめて自動化したいとき

All Images AIの操作をエージェントに任せるためのスキル。Rube MCP経由で利用可能なツールと入力形式を調べ、接続確認から実行まで進めます。

• All Images AIの操作を調べたいとき
• 実行前にサービス接続を確認したいとき
• 複数の操作をまとめて進めたいとき