「x」の検索結果

添付された表計算や文書を先に読み、必要なデータを抽出して出典ファイル名を示します。不足時だけ外部検索や推定へ進み、その情報を区別する作業手順です。

• 添付Excelからのレポート作成
• CSVやPDFから必要な数値を抽出
• 集計結果に出典ファイル名を示したいとき

openpyxlで結合セルを使うExcel帳票を作る際に、値の入力先と処理順を整理します。左上セルへの入力から結合・書式設定・保存後の確認までを案内します。

• 複数列にまたがる帳票タイトル作成
• 見出しやグループラベルの書式設定
• 既存の結合セルの値を更新したいとき

見出しの位置が不明な表や結合セルを含むExcelから、文字列のパターンで見出し行を探し、列名の整理や空行の除去を行ってデータを取り出すスキル。

• タイトル行の下にある表を取り出したいとき
• 見出し位置が異なるシートの読み込み
• 空欄や重複のある列名の整理

Pythonの実行ツールが繰り返し失敗するときに、コードをファイルへ保存してシェルから直接実行し、生成物やエラーを確認する代替手順を案内するスキル。

• 実行ツール失敗後にPythonを再実行したいとき
• 保存したPythonでExcelを作成したいとき
• エラー出力と生成ファイルの確認

Pythonのサンドボックス実行が繰り返し失敗した際に、コードをファイルへ保存してシェルで実行し、標準出力やエラーを確認する代替手順を示します。

• サンドボックス失敗後にシェルで再実行したいとき
• 長時間処理のタイムアウトを調整したいとき
• 複数ファイルや外部コマンドを使いたいとき

マルウェアを識別するYARA-Xの検出ルールを作成・レビューし、文字列や条件式の選び方、誤検知の削減、処理速度の改善、旧YARAからの移行を支援するスキル。

• 特定のマルウェア向けルールを作りたいとき
• 誤検知や検出処理の遅さを見直したいとき
• 既存のYARAルールをYARA-Xへ移したいとき

許可された侵入テストでデータの外部持ち出しを検証するために、通信経路の選択、圧縮・暗号化・分割、受信後の整合性確認と検知の観点を整理するスキル。

• 許可された演習の転送経路を比較したいとき
• 圧縮・暗号化・ファイル分割の計画
• SHA-256による受信データの照合

上場企業の決算や会社予想をExcelモデルの入力項目に対応づけ、元ファイルを残してコピーを更新し、変更履歴や評価への影響、未反映の項目を整理するスキル。

• 決算後に企業分析モデルを更新
• 開示数値と入力セルを対応づけ
• 変更内容と出典を確認したいとき

Mixpanelのログイン状態や認証情報を確認し、アカウント・プロジェクト・ワークスペースを切り替えるスキル。接続先の組み合わせも名前付きで保存できます。

• 現在のアカウントと接続先の確認
• 初回ログインと認証の接続テスト
• プロジェクトやワークスペースの切り替え

ProteomeXchangeのデータセットやタンパク質、スペクトルなどをPROXI APIで照会し、識別子や対象を絞った結果を簡潔にまとめます。

• 識別子でデータセットを確認したいとき
• タンパク質・ペプチド・PSMの照会
• スペクトルやUSIの例を取得したいとき

CELLxGENE Discoverの公開コレクションやデータセットの基本情報を取得し、単一細胞研究のデータを探す際に役立つ簡潔な概要にまとめるスキル。

• 単一細胞データの概要を知りたいとき
• 公開コレクションの候補探し
• 基本情報をJSONで取得したいとき

OKX-aggregated DeFi (no specific DApp named) — product discovery, deposit/withdraw/claim execution, AND positions viewing. **If the user names ANY third-party protocol/DApp (Aave, Lido, PancakeSwap, Uniswap, Curve, Compound, Morpho, Pendle, Kamino, Raydium, Hyperliquid, Polymarket, …), route to okx-dapp-discovery — NOT here, even for 'show my Aave positions'.** INVEST triggers: 'invest in DeFi', 'earn yield', 'find best APY', 'deposit/stake for yield', 'search DeFi products', 'redeem/withdraw position', 'claim DeFi rewards', 'borrow against asset', 'repay loan', 'add/remove CLMM liquidity', 'APY/TVL history', 'depth chart', yield farming, lending, staking, liquidity pools. PORTFOLIO triggers: 'check my DeFi positions', 'view DeFi holdings/portfolio', 'my staking/lending positions', 'DeFi balance', 'DeFi 持仓', '我的DeFi资产'. Do NOT use for: DEX swaps (okx-agentic-wallet), token prices (okx-dex-market), wallet token balances (okx-agentic-wallet).

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。

YouTube transcript extraction and content reformatting: given a YouTube video URL, opens the video's transcript panel, extracts all timestamped segments, and transforms the raw transcript into summaries, chapter outlines, Twitter/X threads, blog posts, or notable quotes. Use when the user shares a YouTube URL or video link, asks to summarize a video, get a transcript, extract content from a YouTube video, get YouTube captions, extract YouTube captions, download YouTube captions, transcribe YouTube video, YouTube video to text, make a thread from YouTube, YouTube to blog post, YouTube to article, pull transcript from YouTube, YouTube content extraction, convert YouTube to text, video to transcript. Also applies when user wants to reformat any YouTube video content into structured output (chapters, threads, blog articles, key quotes).

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。

Amazon Alexa for Shopping Q&A automation: submits questions to Amazon's Alexa/Rufus AI shopping assistant and collects response text; supports optional keyword search context (navigate to search results page before asking for category-specific answers). Use when user mentions Amazon Alexa, Rufus, Amazon shopping assistant, Amazon AI chat, ask Amazon, Amazon Q&A, automate Alexa questions, Rufus chatbot, Amazon assistant automation, collect Alexa responses, bulk question submission to Amazon, keyword search context, category research. Also applies to extracting Amazon product recommendations from conversational AI, automating repeated queries to Amazon's AI shopping feature, collecting Alexa shopping responses at scale, or market research within a specific product category.

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。

Production-ready CSS transitions for web apps. Use when implementing notification badges, dropdowns, modals, panel reveals, page transitions, card resizes, number pop-ins, text swaps, icon swaps, success checks, avatar group hovers, error state shakes, search/input clear, skeleton loaders, shimmer text, sliding tabs, tooltips, staggered text reveals, card hover tilt, plus-to-menu morph, accordions, toasts, like buttons, learn-more hovers, checkbox checks, spinning counters, toggles, AI thinking states, reasoning streams, streaming text, matrix dot loaders, or banner stacking. Triggers on "add a transition", "animate the dropdown", "make the modal open smoothly", "swap icon", "page slide", "stagger animation", "open / close transition", "make it animate", "fade between", "success animation", "form error", "shake on invalid", "hover lift", "avatar stack hover", "clear the search", "skeleton loader", "loading shimmer", "shimmer text", "sliding tabs", "segmented control", "tooltip", "reveal text", "tilt card", "3D hover tilt", "cursor glare", "plus to menu", "FAB morph", "accordion", "collapsible", "expand / collapse", "disclosure", "toast", "snackbar", "like button", "heart animation", "learn more arrow", "checkbox", "check animation", "spinning counter", "odometer", "slot machine digits", "toggle", "switch", "thinking states", "AI status line", "agent reasoning", "reasoning stream", "streaming text", "stream words in", "matrix loader", "dot loader", "banner stack", "stacked toasts". Also "motion tokens", "scan for ad-hoc transitions", "replace hardcoded durations with motion tokens", "tokenize my animations", and the commands transitions reveal, transitions review, transitions apply, transitions refine.

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。

DOCA Rivermax (doca_rmax) receive streams for timing-precise media over IP (SMPTE ST 2110 video / audio, market data, scientific feeds) on a BlueField DPU or ConnectX host, once the Rivermax SDK and its license are installed; getting the SDK or license is doca-setup / doca-public-knowledge-map territory. Trigger on "ST 2110 receive isn't getting frames", "sub-microsecond jitter on my media receiver", "NOT_SUPPORTED from doca_rmax_init", "no recv events after stream start", or "license check failing on a media receiver", even without naming Rivermax. Covers doca_rmax_in_stream sessions, the SDK + license precondition check, doca_rmax_get_*_supported capability queries, pairing with doca-eth queues and doca-flow steering, and DOCA_ERROR_* from Rivermax calls. Refuse and route elsewhere for programming the underlying queue (doca-eth), steering rules (doca-flow), PTP / clock sync (doca-firefly), or best-effort packet I/O.

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。

Azure Monitor OpenTelemetry Exporter for Python. Use for low-level OpenTelemetry export to Application Insights. Triggers: "azure-monitor-opentelemetry-exporter", "AzureMonitorTraceExporter", "AzureMonitorMetricExporter", "AzureMonitorLogExporter".

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。

Azure AI Text Translation SDK for real-time text translation, transliteration, language detection, and dictionary lookup. Use for translating text content in applications. Triggers: "text translation", "translator", "translate text", "transliterate", "TextTranslationClient".

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。

Export and build a Godot 4.7 project for distribution: install export templates, define export presets (Windows/macOS/Linux/Web/Android), run headless command-line exports for CI, and handle web (HTML5) COOP/COEP and dedicated-server/headless builds. Use when exporting a Godot game, configuring export_presets.cfg, building for web/desktop/mobile, or automating builds from the command line.

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。

Gates whether a new test should exist and forces it to be efficient, protecting CI from low-value test bloat. Use before any change to what a pytest, Jest, or Playwright test asserts or sets up, down to one fixture or one assertion added to an existing block. Front-loads the value bar (every test must catch a realistic regression no existing test already catches; extend the nearest existing test before writing a new standalone one; test behavior through the public interface, not implementation details; collapse near-duplicates into parameterized cases) and the efficiency bar (deterministic, isolated, fast; pick the cheapest test level; Django TestCase over TransactionTestCase; no sleeps, no real network; no time bombs from absolute dates left to age against the real clock; no database a test never uses). Includes a "don't write it" decision tree. For fixing an existing flaky test use `/fixing-flaky-tests`; after this gate says a Playwright test is warranted, use `/playwright-test` for mechanics.

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。

Build OCR pipelines in MATLAB using the ocr() function. Use this skill when the user wants to read text from images, documents, signs, meters, displays, license plates, gauges, receipts, or seven-segment displays. Covers image preprocessing, text detection (CRAFT, MSER), ROI-based recognition, multi-language OCR, and custom model training. Use when: OCR, text recognition, extract text from image, character recognition, document scanning, meter reading, gauge reading, receipt scanning, digitize text from photo.

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 skill for Adobe Substance 3D Painter texturing and material creation workflow. Use this skill when creating PBR materials, exporting textures for web/game engines, optimizing 3D assets for real-time rendering, or automating texture workflows. Triggers on tasks involving Substance 3D Painter, PBR texturing, material creation, texture export for Three.js, Babylon.js, Unity, Unreal, glTF optimization, or Python API automation. Creates optimized textures for threejs-webgl, react-three-fiber, and babylonjs-engine materials.

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。

Build customer journey maps and service blueprints that visualize the end-to-end user experience including touchpoints, emotions, friction, and underlying systems. Use this skill whenever the user wants to map a customer journey, build a service blueprint, identify friction across an experience, align teams on the user experience, or visualize touchpoints and pain points. Triggers on customer journey, journey map, service blueprint, user journey, experience map, touchpoint analysis, friction map, journey audit, end-to-end experience, customer experience map. Also triggers when the team has departmental views of users but no shared map of what the experience actually feels like.

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。