「embed」の検索結果

Semantic embeddings for Clojure code navigation via CIDER

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。

RAG（检索增强生成）管道全流程。覆盖文档处理、Embedding、向量存储、检索策略、 重排序、生成优化、评估迭代、生产化部署。 触发关键词：RAG、向量检索、Embedding、知识库、文档问答、向量数据库、 Chunking、Reranking、Hybrid Search、语义搜索。

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。

Deconstructs structured note payoffs with embedded option identification, issuer credit risk, and all-in cost analysis. Use when analyzing structured notes, evaluating embedded options, or assessing structured product costs.

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。

SwiftUI のレイアウト落とし穴・ベストプラクティス・非推奨パターンと、 iOS / watchOS アプリの実機配布 (Provisioning / App Group / Code Signing / Apple Watch Developer Mode / Xcode 15+ Debug dylib) トラブルシューティング のガイド。コード生成・修正時に既知のレイアウトバグを防ぎ、 App Group + watchOS + Apple Watch の実機インストール失敗を 7 段階フレームワークで診断・解消する。 Use when: SwiftUI のコードを書く・修正するとき。 レイアウト崩れを修正するとき。safeAreaInset や ViewThatFits を使うとき。 マルチデバイス対応するとき。iOS + watchOS アプリの実機ビルド / 配布で provisioning / App Group / Manual Signing / Apple Watch の UDID / Xcode 自動署名の 罠に詰まったとき。Apple Watch に "Could not install at this time" が出たとき。 Triggers: "SwiftUI", "layout", "safeAreaInset", "ViewThatFits", "GeometryReader", "レイアウト", "崩れ", "表示バグ", "iPhone SE", "ATT", "ATTrackingManager", "requestTrackingAuthorization", "AdMob", "広告", "Provisioning Profile", "App Group", "Manual Signing", "Code Signing", "Apple Watch", "watchOS", "Install できない", "Could not install at this time", "Bundle ID 紐付け", "Xcode Automatic Signing", "embedded.mobileprovision", "Spaceship", "App Store Connect API", "WKCompanionAppBundleIdentifier", "Developer Mode", "Privacy & Security", "watchOS Developer Mode", "Apple Watch に App を入れられない", "整合性を確認できなかった", "integrity check", "ENABLE_DEBUG_DYLIB", "__preview.dylib", "debug.dylib", "Xcode 15 Preview", "SwiftUI Preview dylib", "ENABLE_PREVIEWS", "AppIntents", "AppShortcut", "AppShortcutsProvider", "AppEntity", "AppEnum", "Siri", "Siri に流れる", "Siri がリマインダーに流れる", "updateAppShortcutParameters", "Invalid parameter type", "Invalid Utterance", "applicationName", "CFBundleDisplayName", "CFBundleSpokenName", "TaskEntityQuery", "AppEntityQuery", "TimelineProvider", "recommendations", "WatchConnectivity", "WCSession", "updateApplicationContext", "sessionDidBecomeInactive", "iPhone と Watch でデータ共有", "App Group 共有できない", "scenePhase", "ポーリング", "watchOS バッテリー", "TextField 文字色 watchOS", "Picker 文字色 watchOS", "Single Size AppIcon", "watchOS AppIcon"

Use this skill when read_file is not enough to read a PDF — it failed, timed out, or returned garbled or empty text, or the question is about figures, charts, layout, embedded images, attachments, or form-field values. Covers content inventory, text extraction, page rasterization for visual inspection, embedded image/attachment/table/form-field extraction, and choosing the right reading strategy for different document types (text-heavy, scanned, slide-decks, forms, data-heavy). Do NOT use this skill for PDF creation, form filling, merging, splitting, watermarking, or encryption — use the pdf skill instead.

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。

ブラウザを手足のように操る。ページ遷移、フォーム入力、スクショ、なんでもこい。Use when users ask to navigate websites, fill forms, take screenshots, extract web data, test web apps, or automate browser workflows. Trigger phrases include 'go to [url]', 'click on', 'fill out the form', 'take a screenshot', 'scrape', 'automate', 'test the website', 'log into', or any browser interaction request. Do NOT load for: sharing URLs, embedding links, screenshot image files.

MATLAB R2026a code generation and deployment workflow for MATLAB Coder, Simulink Coder, Embedded Coder, GPU Coder, HDL Coder, fixed-point conversion, MEX validation, and embedded deployment readiness. Use whenever the user asks to generate C/C++/CUDA/HDL/code, deploy algorithms, or validate generated code.

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 and edit Obsidian Flavored Markdown with wikilinks, embeds, callouts, properties, and other Obsidian-specific syntax. Use when working with .md files in Obsidian, or when the user mentions wikilinks, callouts, frontmatter, tags, embeds, or Obsidian notes.

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 a single self-contained HTML file — a horizontal slide deck, a vertical-scroll explainer document, or a fixed-canvas summary image — with zero external runtime dependencies, then verify it mechanically. Use when the user asks for HTMLスライド, 単一HTMLスライド, single-file HTML presentation, ブラウザーで開く説明資料, HTML 説明資料, HTMLサマリ画像, or self-contained HTML, or wants to hand someone a deck or explainer that opens anywhere without PowerPoint. Also use to embed images into such a file or to re-check an existing one. Does not output PPTX.

Test Retrieval-Augmented Generation (RAG) systems for data poisoning, prompt injection via retrieved documents, and data exfiltration through manipulated context windows. Use this skill when assessing RAG-based chatbots, knowledge bases, enterprise AI assistants, or any system that augments LLM responses with external document retrieval. Covers document injection, embedding manipulation, knowledge base poisoning, and cross-document inference attacks.

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。

Analyzes malicious PDF documents: parsing the object structure for JavaScript, OpenAction/launch triggers, embedded files, and URIs, then extracting and decoding suspicious streams without opening the document in a reader. Activates for requests to analyze a malicious PDF, inspect PDF JavaScript, or extract embedded payloads from a PDF.

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。

Convert Markdown (.md) files to a polished PDF with ALL images preserved and scaled to the page. Use whenever the user asks to "save this markdown as a PDF", "convert README.md to pdf", "export the .md as a pdf", "turn these notes/docs into a PDF", or wants a PDF deliverable of any GitHub-flavored Markdown document (README, design doc, report, notes) — especially when it contains images or diagrams (remote or local), tables, code blocks, or a linked table of contents. Pipeline: pandoc (GFM → standalone HTML with embedded resources) → headless Chrome print-to-pdf with a GitHub-like print stylesheet → pypdf verification that every referenced image is embedded. Do NOT use for .tex → PDF (use a LaTeX toolchain) or for .docx work (use word-docx / tex2docx).

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 a task needs the judgment of an Embedded/Firmware Engineer — analyzing worst-case execution time for a hard real-time deadline, choosing static over dynamic memory allocation to avoid fragmentation on a long-uptime device, keeping an interrupt service routine minimal and deferring work to the main loop, synchronizing a variable shared between an ISR and main context to prevent a torn read, or verifying a hardware register interaction against the datasheet's exact timing rather than observed bench behavior.

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。

Work with text embeddings — choosing models, pooling, similarity measures, evaluation, and multilingual considerations. Use when building semantic search, clustering, or any similarity-based feature.

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 with Cohere's enterprise AI — Command models, Embed embeddings, and Rerank for retrieval pipelines.

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。

Integrate PocketBase as a Go library using the github.com/castle-x/goutils/pocketbase (gopb) package to build single-binary full-stack applications. Use when building Go applications that need user authentication, embedding PocketBase into Go binary, registering custom API routes, managing default users, serving embedded SPA frontend, or deploying single-binary applications. NOT for using PocketBase as a standalone separate process.

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。

Embedded/IoT Engineer — Arduino/ESP32/Raspberry Pi, sensor integration, MQTT/BLE/LoRa, firmware development, battery optimization

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。

PXE/netboot 経由で Debian + HW RAID10 を自動インストール (OpenWrt ローカル TFTP + embed ipxe.efi + IPv4 リテラル mirror)。BMC USB redirector が累積劣化した機種 (TX1320 M3 等) や、 cross-site 拠点の install に使う。phase 19 で training-tx1320 で完遂、 物理操作なしで自力 SSH まで到達。

Converts transparent or solid-background single-color PNG files and SVG files containing one embedded or local PNG into verified path-only SVG output. Removes pure-color backgrounds automatically. Uses Potrace threshold and parameter scans with alpha error metrics to preserve detailed logos, stamps, engravings, and line art. Use when the user asks to convert images to SVG, vectorize PNG, convert bitmap to true SVG, remove embedded raster data from SVG, 图片转矢量, PNG 转 SVG, 真矢量, or 位图描摹.

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 in SQLite embedded database development for Tauri/desktop applications with focus on SQL injection prevention, migrations, FTS search, and secure data handling

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。

Produce and interpret PCA, t-SNE, UMAP, and PHATE plots for high-dimensional omics data with rigor about which method preserves what (variance, local structure, manifold, transitions), hyperparameter sensitivity, and the well-documented limits of 2D embeddings. Covers PCA biplot/scree/loadings, t-SNE PCA initialization (Kobak-Berens 2019), UMAP n_neighbors/min_dist trade-offs, and the Chari-Pachter 2023 critique. Use when visualizing high-dimensional data — bulk PCA, single-cell embeddings, multi-omics integration projections.

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 publication-quality figures with matplotlib using the object-oriented Figure/Axes API, constrained_layout, rcParams customization, TrueType (Type-42) font embedding for journal submission, and CVD-safe palettes. Covers seaborn integration, common chart types, axis formatting, and the small gotchas that distinguish reproducible matplotlib from notebook scratch. Use when producing publication figures in Python — RNA-seq scatter, single-cell embeddings, generic biological plotting.

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。

Especialista em construção de sistemas de Retrieval-Augmented Generation. Domina modelos de embedding, bancos de dados vetoriais, estratégias de chunking e otimização de recuperação para aplicações LLM. Use quando: construir RAG, busca vetorial, embeddings, busca semântica, recuperação de documentos.

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。

Padrões de Retrieval-Augmented Generation incluindo chunking, embeddings, vector stores e otimização de recuperação. Use quando: rag, retrieval augmented, vector search, embeddings, semantic 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。