「loading」の検索結果

Use when auditing slow page loads, heavy assets, or rendering delays related to Disable lazy loading for above-the-fold content. Verify the actual bottleneck in DevTools, Lighthouse, or field data before recommending changes.

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 reviewing image assets, markup, and CDN or build transforms related to Optimize all images for web. Check encoded size, rendered size, loading strategy, and above-the-fold impact together.

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 reviewing image assets, markup, and CDN or build transforms related to Compress images without quality loss. Check encoded size, rendered size, loading strategy, and above-the-fold impact together.

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 reviewing image assets, markup, and CDN or build transforms related to Serve images from a CDN. Check encoded size, rendered size, loading strategy, and above-the-fold impact together.

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 reviewing long pages, dashboards, or content feeds with expensive offscreen modules. Balance early enough loading for smooth scrolling against keeping the initial route light.

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 reviewing image assets, markup, and CDN or build transforms related to Use <figure> and <figcaption> for image captions. Check encoded size, rendered size, loading strategy, and above-the-fold impact together.

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 reviewing image assets, markup, and CDN or build transforms related to Set explicit width and height on images. Check encoded size, rendered size, loading strategy, and above-the-fold impact together. Separate real CLS risks from tiny decorative SVGs or ornament-only assets that are unlikely to move surrounding content.

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 optimising Largest Contentful Paint (LCP), reducing render-blocking resource contention, or fine-tuning resource loading order in the critical rendering path.

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 reviewing image assets, markup, and CDN or build transforms related to Use descriptive image filenames. Check encoded size, rendered size, loading strategy, and above-the-fold impact together.

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 reviewing image assets, markup, and CDN or build transforms related to Fix broken images. Check encoded size, rendered size, loading strategy, and above-the-fold impact together.

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 reviewing large SPA bundles, new dependency additions, or routes that feel slow to hydrate. Focus on code that is not required for the initial view and can move behind route transitions, user interaction, or viewport-based loading.

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 reviewing image assets, markup, and CDN or build transforms related to Use AVIF format for modern browsers. Check encoded size, rendered size, loading strategy, and above-the-fold impact together.

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 reviewing image assets, markup, and CDN or build transforms related to Provide meaningful alt text for images. Check encoded size, rendered size, loading strategy, and above-the-fold impact together.

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。

Clarify a material user-owned product decision before substantive implementation commitments. Use only when request and named/governing evidence still leave a choice that would materially change scope, platform support, user-facing surface, data/API contract, domain values, project placement, or cross-system architecture; also use when a truncated request omits such a choice. On the initial turn, inspect only named or governing requirements evidence, then ask directly without loading: prefer structured input; ask one blocking outcome question or at most three coupled questions; wait. After the answer, load the body before write or delegation. Do not use for implement/build/fix/debug/refactor work when expected behavior is settled, plan/design deliverables owned by Plan, complexity/greenfield/integration alone, agent-owned technical choices, locally discoverable facts, reversible defaults, permission to begin, or stop/no-tool turns. Never re-ask supplied facts or turn clarification into refusal.

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 a bounded Agent Skill catalog across explicit discovery scopes and report collisions before loading instruction bodies.

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。

When the user wants to audit or optimize an App Store or Google Play listing. Also use when the user mentions 'ASO audit,' 'app store optimization,' 'optimize my app listing,' 'improve app visibility,' 'app store ranking,' 'audit my listing,' 'why aren't people downloading my app,' 'improve my app conversion,' 'keyword optimization for app,' or 'compare my app to competitors.' Use when the user shares an App Store or Google Play URL and wants to improve 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。

Experimental NotebookLM harness for listing notebooks, managing sources, asking questions, generating artifacts, and downloading outputs through an installed notebooklm CLI.

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。

Hugging Face Transformers for loading Hub models, running pipeline inference, text generation, and Trainer fine-tuning on NLP, vision, audio, and multimodal tasks. Applies when working with AutoModel, pipelines, tokenizers, generation configs, or TrainingArguments within Transformers.

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。

Manages Zotero reference libraries using the pyzotero Python client: retrieves, creates, updates, and deletes items, collections, tags, and attachments via the Zotero Web API v3 or local API. Applies when working with Zotero libraries programmatically, managing bibliographic references, exporting citations, searching library contents, uploading PDF attachments, or building research automation workflows that integrate with Zotero.

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。

Supports local computational pathology research with PathML: slide loading and tiling, preprocessing and QC, h5path storage, multiplex quantification, spatial graphs, and bounded model inference. Use for whole-slide H&E, CODEX, Vectra, Mesmer, HoVer-Net, and HACTNet 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。

Builds and validates PyHealth clinical machine-learning pipelines for EHR, signals, imaging, and medical codes. Use for PyHealth dataset loading, MIMIC-III/IV, eICU or OMOP prediction tasks, patient-level evaluation, mortality/readmission/length-of-stay modeling, medication recommendation, sleep staging, Trainer checkpoints, and ICD/ATC/NDC/RxNorm mapping.

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 Neuropixels extracellular recordings end-to-end with SpikeInterface. Covers loading SpikeGLX/Open Ephys/NWB data, preprocessing, drift/motion correction, Kilosort4 (and CPU) spike sorting, quality metrics, and unit curation (threshold-based, model-based UnitRefine, and AI-assisted visual review). Use when working with Neuropixels 1.0/2.0 recordings, spike sorting, or extracellular electrophysiology analysis.

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。

Operates Fictiv (app.fictiv.com), the on-demand manufacturing platform, end to end in the user's browser. Covers uploading CAD parts, configuring process, material, finish, threads, tolerances and inspections, getting instant or manual quotes, reading and fixing DFM feedback, choosing lead time and region, checking out and paying (card or PO), tracking orders, reordering, and troubleshooting. Applies when the user mentions Fictiv, wants a part CNC machined, 3D printed, sheet-metal fabricated, urethane cast, injection or compression molded, or die cast through an online service, asks to "get a quote" or "order parts" for a STEP/SLDPRT/STL file, wants to check a Fictiv quote or order status, or has a problem with a Fictiv upload, DFM warning, price or checkout. Also applies when the user wants custom parts manufactured and has a Fictiv account, even if Fictiv is not named.

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。

Modern Angular UI patterns for loading states, error handling, and data display. Use when building UI components, handling async data, or managing component states.

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。