「data loading」の検索結果

Design the UX of custom-code Databricks Apps (AppKit/React) data screens — KPI/overview pages, reports, charts, tables, and Genie/chat data assistants — mapped to concrete AppKit components. Use when BUILDING or reviewing the UI of an AppKit/React app that displays data or answers data questions: choosing genre, layout, charts, KPIs, semantic color, required states (loading/empty/error), IBCS notation, and AI-result trust (showing generated SQL/sources for Genie/chat). A plain "create a dashboard" request means a managed AI/BI (Lakeview) dashboard → use databricks-aibi-dashboards, NOT this skill. Also NOT for non-data frontend (forms, settings, auth, marketing) or scaffolding/build/deploy (→ databricks-apps). Complements databricks-apps; use it alongside whenever a custom app has a chart, table, KPI, report, or Genie/chat/AI surface.

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。

Nuxt 4アプリのサーバーとブラウザの表示ずれを防ぎ、データ取得・ページ別の描画やキャッシュ・遅延読み込みを整理します。実装やレビュー、性能改善に使えます。

• サーバーとブラウザの表示ずれを直したいとき
• ページのデータ取得をレビューしたいとき
• ページ別の描画・キャッシュを決めたいとき

Guide for dataset construction — SupervisedDatasetBuilder, RLDatasetBuilder, ChatDatasetBuilder, and custom dataset creation from JSONL, HuggingFace, or conversation data. Use when the user asks about datasets, data loading, data preparation, or custom data formats.

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。

Adaption AI SDK for synthetic data augmentation and dataset adaptation. Use when building data pipelines with the Adaption Python SDK, uploading datasets (local files, Hugging Face, Kaggle), running augmentation/adaptation jobs, configuring brand controls (hallucination mitigation, safety categories, length), recipe specifications (reasoning traces, deduplication, preference pairs, prompt rephrase), evaluating dataset quality, downloading results, or any workflow involving `pip install adaption`, `from adaption import Adaption`, Adaptive Data, or the adaptionlabs.ai API. Also trigger when the user mentions synthetic data generation for fine-tuning, dataset augmentation pipelines, DPO preference pair generation, or grounding-based hallucination reduction on training 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。

Persist player data in Roblox with DataStoreService: GetDataStore, GetAsync/ SetAsync/UpdateAsync/IncrementAsync wrapped in pcall, load-on-join and save-on-leave plus BindToClose, retries, and OrderedDataStore leaderboards. Use when saving or loading persistent data in a Roblox experience — when the user mentions DataStore, DataStoreService, GetAsync, SetAsync, UpdateAsync, save player data, or leaderboards. For general Luau scripting use roblox-luau.

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。

Patterns for loading PDE simulation datasets (PDEBench, PhiFlow, JAX-CFD) from HDF5 files. Handles layout detection (single tensor vs separate variables), spatial/temporal downsampling, multi-variable systems, HuggingFace and DaRUS data sources, and efficient PyTorch DataLoader creation. Use when preparing PDE data for neural operator training.

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。

Connect SaaS data (HubSpot, Stripe, Salesforce, GitHub, Slack, etc.) to Wren Engine for SQL analysis. Guides the user through the full flow: install dlt, pick a SaaS source, set up credentials, run the data pipeline into DuckDB, then auto-generate a Wren semantic project from the loaded data. Use this skill whenever the user mentions: connecting SaaS data, importing data from an API, dlt pipelines, loading HubSpot/Stripe/Salesforce/GitHub/Slack data, querying SaaS data with SQL, or setting up a new data source from a REST API. Also trigger when the user already has a dlt-produced DuckDB file and wants to create a Wren project from it.

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。

Trigger on-chain data collection (a "Commit") in Tres Finance for wallets that have already been onboarded. Use this skill whenever the user wants to collect data, pull balances, sync wallets, refresh on-chain data, run a commit, trigger a commit, or "collect" anything in Tres. This is the second step of the Tres onboarding flow — it sits between wallet upload (tres-wallets-upload) and balance validation (tres-asset-balance-validation). Always trigger this skill for any request that mentions "commit", "collect", "data collection", "pull data", "fetch on-chain data", "sync wallets", "refresh balances", or anytime the user just finished uploading wallets and is ready to bring in their on-chain 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。

Build with Aurora DSQL — manage schemas, execute queries, handle migrations, diagnose query plans, diagnose cluster performance, load data, and develop applications with a serverless, distributed SQL database. Covers IAM auth, multi-tenant patterns, MySQL-to-DSQL and PostgreSQL-to-DSQL schema conversion, foreign key constraints, OCC retry patterns, ORM migration (Django/EF Core/Hibernate/Rails/SQLAlchemy), DDL operations, query plan explainability, system diagnostics via CloudWatch AAS, SQL compatibility validation, and bulk data loading. Load before answering DSQL limit or error-code questions; it has limits docs omit. Triggers on phrases like: DSQL, Aurora DSQL, distributed SQL database, serverless PostgreSQL-compatible database, migrate to DSQL, DSQL query plan, DSQL EXPLAIN ANALYZE, DSQL ENUM, DSQL foreign key, DSQL OCC retry, DSQL multi-region, DSQL JSONB, DSQL GIN index, load into DSQL, load CSV into DSQL, bulk load DSQL, aurora-dsql-loader, DSQL slow, DSQL performance, DSQL wait events, DSQL AAS.

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 Aurora DSQL — manage schemas, execute queries, handle migrations, diagnose query plans, load data, and develop applications with a serverless, distributed SQL database. Covers IAM auth, multi-tenant patterns, MySQL-to-DSQL and PostgreSQL-to-DSQL schema conversion, FK replacement code generation, OCC retry patterns, ORM migration (Django/Hibernate/Rails), DDL operations, query plan explainability, SQL compatibility validation, and bulk data loading. Triggers on phrases like: DSQL, Aurora DSQL, distributed SQL database, serverless PostgreSQL-compatible database, migrate to DSQL, DSQL query plan, DSQL EXPLAIN ANALYZE, DSQL ENUM, DSQL foreign key, DSQL OCC retry, DSQL multi-region, DSQL JSONB, DSQL GIN index, load into DSQL, load CSV into DSQL, bulk load DSQL, aurora-dsql-loader.

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。

PyTorchのモデルや学習コードを作成・点検し、データ読み込み、実験の再現性、学習状態の保存、GPUメモリや処理速度の改善を支援するスキル。

• PyTorchの学習コード作成・レビュー
• 実験結果が揃わない原因を調べたいとき
• 読み込み速度やGPUメモリの改善

Spring Bootのデータベース処理を設計・改善するスキル。JPA/Hibernateのデータ構造や関連付け、検索、トランザクションを見直し、不要な問い合わせを減らします。

• データ構造と関連を設計したいとき
• 関連データ取得のSQLを減らしたいとき
• 更新処理や変更履歴を見直したいとき

大量データの取り込みや過去分の補充、変換、同期の遅さを工程別に調べ、処理方法を比較して高速化し、行数・ファイル数・時刻の整合性まで確認するスキル。

• 大量データの取り込み・書き出しの高速化
• 過去データの補充時間を短縮したいとき
• バッチサイズや並列処理数を比較したいとき

Guidance for implementing CollectionView in .NET MAUI apps — data display, layouts (list & grid), selection, grouping, scrolling, empty views, templates, incremental loading, swipe actions, and pull-to-refresh. USE FOR: "CollectionView", "list view", "grid layout", "data template", "item template", "grouping", "pull to refresh", "incremental loading", "swipe actions", "empty view", "selection mode", "scroll to item", displaying scrollable data, replacing ListView. DO NOT USE FOR: simple static layouts without scrollable data (use Grid or StackLayout), map pin lists (use Microsoft.Maui.Controls.Maps), table-based data entry forms, non-MAUI list controls, CarouselView or BindableLayout questions, platform-specific handler or renderer customization, diagnosing CollectionView bugs in the MAUI framework itself, or general MVVM/binding questions that merely happen to mention a list (use maui-data-binding).

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 this skill when conditioning, loading, preparing, or labeling signal data for analysis or ML training. Covers: cleaning a single signal (fill gaps, remove drift, deoutlier, denoise, resample/align a time base) BEFORE analysis; building a `signalDatastore` pipeline; creating a `labeledSignalSet` for Signal Labeler; deriving labels (filename, folder, in-file, ROI, time-frequency ROI); stratified train/val/test splits; framing long signals; parallel processing; and shaping datastore output for `trainnet`. Triggers include "clean up this signal", "remove drift / detrend", "fill gaps", "remove spikes / outliers", "denoise", "resample to a uniform rate", "align channels", "labels from filenames", "stratified split", "prepare for Signal Labeler", and function names like `fillgaps`, `fillmissing`, `detrend`, `filloutliers`, `smoothdata`, `resample`, `synchronize`, `signalDatastore`, `labeledSignalSet`, `filenames2labels`, `folders2labels`, `splitlabels`, `framesig`, `framelbl`, `createDatastores`.

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。

Guidance for implementing CollectionView in .NET MAUI apps — data display, layouts (list & grid), selection, grouping, scrolling, empty views, templates, incremental loading, swipe actions, and pull-to-refresh. USE FOR: "CollectionView", "list view", "grid layout", "data template", "item template", "grouping", "pull to refresh", "incremental loading", "swipe actions", "empty view", "selection mode", "scroll to item", displaying scrollable data, replacing ListView. DO NOT USE FOR: simple static layouts without scrollable data (use Grid or StackLayout), map pin lists (use Microsoft.Maui.Controls.Maps), table-based data entry forms, non-MAUI list controls, CarouselView or BindableLayout questions, platform-specific handler or renderer customization, diagnosing CollectionView bugs in the MAUI framework itself, or general MVVM/binding questions that merely happen to mention a list (use maui-data-binding).

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。

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

ClickHouseの分析用テーブル設計、集計クエリの作成、遅い処理の見直しを支援します。大量データの一括取り込みやリアルタイム集計の設計にも使えます。

• 大量データ向けのテーブル設計
• 遅い集計クエリを改善したいとき
• 大量データを一括投入したいとき

Scalable data processing for ML workloads. Streaming execution across CPU/GPU, supports Parquet/CSV/JSON/images. Integrates with Ray Train, PyTorch, TensorFlow. Scales from single machine to 100s of nodes. Use for batch inference, data preprocessing, multi-modal data loading, or distributed ETL pipelines.

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Discover connected data sources, add new data connectors through a user-confirmed form, inspect table metadata, and run bounded read-only probes when the current workspace data is insufficient.

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。

Scalable data processing for ML workloads. Streaming execution across CPU/GPU, supports Parquet/CSV/JSON/images. Integrates with Ray Train, PyTorch, TensorFlow. Scales from single machine to 100s of nodes. Use for batch inference, data preprocessing, multi-modal data loading, or distributed ETL 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。

Scalable data processing for ML workloads. Streaming execution across CPU/GPU, supports Parquet/CSV/JSON/images. Integrates with Ray Train, PyTorch, TensorFlow. Scales from single machine to 100s of nodes. Use for batch inference, data preprocessing, multi-modal data loading, or distributed ETL 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。

Read, write, and create single-cell data objects using Seurat (R) and Scanpy (Python). Use for loading 10X Genomics data, importing/exporting h5ad and RDS files, creating Seurat objects and AnnData objects, and converting between formats. Use when loading, saving, or converting single-cell data formats.

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CesiumJS entities and data sources - Entity, EntityCollection, DataSource, GeoJsonDataSource, KmlDataSource, CzmlDataSource, Graphics types, PathGraphics, PathMode, Visualizers. Use when adding points, labels, models, polygons, polylines, or time-segmented paths, loading GeoJSON/KML/CZML/GPX data, or working with the high-level Entity API.

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。