「time」の検索結果

Proves iOS build/install/launch truth and triages hangs, crashes, jank, memory kills, and stale builds. Use when simulator, bundle, or runtime performance state is in doubt.

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/install/launch proof, ANR/jank/memory triage, and stale-build debugging for native Android apps. Use when runtime truth, performance, or crash root cause is in doubt.

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。

Designs real-time and collaborative systems. Use when building chat, presence, live dashboards, collaborative editing, WebSockets, SSE, WebTransport, or local-first sync.

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。

Designs tool and slash-command runtimes for coding-agent CLIs: registries, deferred loading, tool search, dispatch, forks. Use when modeling tool pools. Not approvals or plugins.

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 backtests demand and revenue forecasts: rolling-origin backtests, prediction intervals, reconciliation, zero-shot foundation models. Use when forecasting time series.

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。

Designs provider runtimes for coding agents. Use when modeling model abstraction, streaming semantics, tool-call normalization, retries, or fallback routing.

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。

Help order food timed to arrive at a specific time. Works backward from target arrival, suggests restaurants, builds cart, and monitors delivery.

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Runtime QA of a change: start the app, test acceptance criteria and edge cases, and report evidence. Use for "test this", "QA", "does it work", exploratory checks, or post-review runtime verification. Not static code review.

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。

Animate objects, cameras, lights, and properties in Blender — keyframes, F-curves, easing (Bezier, Linear, Sine, Bounce, Elastic), shape keys (morph targets / blendshapes / visemes), drivers (Python expressions on properties), NLA actions for reuse and layering. Use whenever the user asks to "animate this", "make it move / rotate / scale over time", "add keyframes", "loop / oscillate", "shape key / morph / blendshape", "visemes for lip sync", or any time-based property change. Make sure to use this skill even if the user does not say "animate" — also covers "spin it slowly", "make it wave", "fade in / out", "pulse", "facial expression".

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。

Designing scheduling and time-aware logic for cron, timezone/DST, retry/backoff, and business-calendar systems. Use when schedule design is needed.

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。

Nexscope Ozon product detail query: fetches the complete detail of a single Ozon product by SKU, returning title, price (RUB), rating, review count, QA count, total and daily average sales within the stats window, revenue, stock, category ranking, daily sales trend, brand, seller, fulfillment method (FBO/FBS/OZON), weight, and listing time/days/months. Use for single product deep analysis, competitor product teardown, Ozon product selection assessment, listing diagnosis, sales trend and category ranking tracking. Trigger when the user mentions Ozon product detail, Ozon single product analysis, Ozon SKU query, competitor product data, Ozon sales trend, Ozon category ranking, Ozon stock, Ozon listing time, Nexscope Ozon product search, Ozon product detail, Ozon SKU lookup, single product analysis, competitor product teardown, Ozon sales trend, category rank. Also trigger when the intent is to view detailed data of an Ozon product, even without explicitly mentioning Nexscope.

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。

Nexscope Ozon Russia single SKU daily time-series performance. Returns daily sales units, price, stock, rating, and optionally search position/visibility data for one Ozon product by date granularity. Use for validating growth trends, seasonality, and anomaly detection. Trigger when the user mentions Ozon trend, Ozon sales trend, Ozon price trend, Ozon daily data, Ozon stock trend, Ozon search ranking, Ozon product history, Nexscope trend, Ozon daily performance, Ozon time series, Ozon search visibility, Russian marketplace product history. Also trigger when the intent is to view the daily/period trend of an Ozon product, even without explicitly mentioning Nexscope.

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。

Google Trends keyword search popularity comparison and trend analysis, supporting global regions and custom time ranges. Triggered by: Google Trends, keyword popularity over time, search interest comparison, keyword trend analysis, seasonal trend detection, regional search popularity, keyword heatmap, multi-keyword comparison on Google, keyword research, market trend analysis, search trends, seasonal analysis, regional popularity.

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。

Fetch and analyze Amazon product reviews by ASIN, supporting 15 marketplaces (including US) with star rating filtering. Triggered when users mention Amazon reviews, US reviews, product reviews, buyer complaints, negative reviews, positive reviews, star ratings, review analysis, review sentiment, product improvement suggestions, Vine reviews, verified purchase reviews, competitor review research, Amazon reviews, US reviews, Amazon.com reviews, product feedback, negative review analysis, positive review analysis, star rating filter, review sentiment analysis, product improvement insights, Vine reviews, competitor reviews, customer feedback. Even if users do not explicitly say "reviews", this skill should be triggered whenever the task involves reading, filtering, or analyzing Amazon product customer reviews.

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 Nexscope data to reverse-lookup traffic keywords for any Amazon ASIN, including organic ranking, ad ranking, search volume, traffic share, organic/paid scores, ABA TOP3 click concentration, click conversion rate, year-over-year search volume changes, and weekly/monthly time windows. Trigger when the user mentions ASIN keyword analysis, ASIN reverse lookup, traffic keyword research, organic ranking query, ad ranking query, keyword position tracking, Nexscope keyword data, competitor keyword peeking, viewing which keywords drive traffic to a product, analyzing keyword performance for a specific ASIN, time windows by week/month/recent N days, ASIN reverse keyword lookup, traffic keywords, organic ranking, ad ranking, search volume, Nexscope keywords, competitor keyword reverse lookup, click concentration, click-to-purchase conversion, week-over-week search volume. Even if the user does not explicitly mention "Nexscope", if their need involves finding keywords associated with a specific Amazon product (ASIN), this skill should also be triggered.

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。

Nexscope keyword historical search volume query, returning Amazon keyword exact search volume trends in 7-day periods, covering 10 marketplaces including US, UK, DE, JP, etc. Trigger when the user mentions keyword search volume trends, historical search volume, search popularity changes, keyword seasonality, search volume fluctuations, Nexscope search volume, keyword search volume history, keyword trend, search volume over time, seasonal search volume, keyword popularity trend. Even if the user does not explicitly mention "Nexscope", if their need involves viewing the search volume trend of an Amazon keyword over time, this skill should also be triggered.

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。

Nexscope ASIN sales estimates query, returning daily estimated sales and latest known price for a specified ASIN over a given time period, covering 10 marketplaces including US, UK, Germany, and Japan. Triggered when users mention ASIN sales estimates, ASIN daily sales, sales estimation, competitor sales monitoring, average daily sales, sales trends, product sales tracking, Nexscope sales data, sales estimates, daily sales, estimated units sold, ASIN sales tracking, competitor sales monitoring, product sales trend, daily unit sales. Even if users do not explicitly mention "Nexscope", this skill should be triggered whenever the task involves viewing daily estimated sales data for an Amazon ASIN over a time period.

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。

Simulates a real user searching on Amazon's storefront to get real-time keyword ranking and search results page data. Use when the user mentions Amazon product search, search result scraping, keyword ranking on search pages, ASIN ranking position check, competitor discovery, search page price comparison, sponsored product analysis, new product monitoring, or storefront search simulation. Even if the user does not explicitly mention "search simulation", trigger this skill whenever their need involves real-time Amazon search results, product ranking data, or storefront SERP 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。

予測モデリング（教師あり機械学習 / 予測モデル / 精度評価 / 交差検証 / リーク、決定木 / ランダムフォレスト / GBDT / LightGBM / XGBoost / CatBoost、モデル解釈 / 特徴量重要度 / SHAP / PDP / ICE、時系列 / 季節性 / ARIMA / SARIMA / Prophet / VAR / 変化点 / 時系列予測）を実行したら必ずセットで出す図と値のルーター。train_test_split, cross_val_score, GridSearchCV, Pipeline, TimeSeriesSplit, DecisionTree, RandomForest, lgb., xgb., CatBoost, dtreeviz, shap., permutation_importance, PartialDependenceDisplay, statsmodels.tsa, SARIMAX, STL, adfuller, plot_acf, Prophet, ruptures がコードに現れたとき、または ユーザーが「予測モデルを作って」「精度を出して」「どの変数が効いているか」「来月を予測して」「季節性を見て」と 言ったときに使う。診断・リーク・ベースライン・残差に言及がなくても適用する。SKILL.md のルーティング表で手法を特定し、 対応する references/<手法>.md を読んでから実行する。回帰係数の推論・検定は statistical-inference-diagnostics、 効果推定は causal-inference-diagnostics を使う。

When the user wants to optimize post-signup onboarding, user activation, first-run experience, or time-to-value. Also use when the user mentions "onboarding flow," "activation rate," "user activation," "first-run experience," "empty states," "onboarding checklist," "aha moment," "new user experience," "users aren't activating," "nobody completes setup," "low activation rate," "users sign up but don't use the product," "time to value," or "first session experience." Use this whenever users are signing up but not sticking around. For signup/registration optimization, see signup. For ongoing email sequences, see emails.

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 building, debugging, or answering questions about Liveblocks. Liveblocks gives you the building blocks and infrastructure to enable people and AI to work together inside your app, powering realtime collaboration. Liveblocks features include collaboration, rooms, organizations, workspaces, comments, composer, threads, notifications, multiplayer, conflict resolution, realtime presence, avatar stacks, AI collaborators, AI agents, text editors, Tiptap, BlockNote, Lexical, React Flow, Chat SDK. Common components include AiChat, Thread, InboxNotification, Composer, Toolbar (for Lexical Tiptap), FloatingToolbar, FloatingComposer, FloatingThreads, AnchoredThreads. Common hooks include useThreads, useStorage, useMutation, useOthers, useInboxNotifications, useAiChats. Common issues are related to authentication (ID tokens vs access tokens), permissions, room limits, connection errors, user info.

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。

Internal helper contract for calling the pi-companion runtime from Claude 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。

Win #1 Product of the Day on Product Hunt. Completely reverse-engineered from 30x daily winner and 3x weekly winner campaigns — ranking algorithm decoded, launch timing optimized, hunter selection guide, community engagement approach, and post-launch momentum playbook. Use this if you're: (1) already read PH launch guides but still can't break into Top 5 and need algorithm-level deep optimization, (2) targeting Daily #1 and 5,000+ sign-ups with a proven 30+ case validated approach, or (3) an investor/accelerator coaching portfolio companies on PH launches. What's inside: PH ranking algorithm decoded: weighted factors for first 3 hours, 24 hours, and weekly ranking · Pre-launch preparation: gallery optimization, product URL selection, tagline refinement (≤60 chars) · Launch timing optimization: exact PST launch windows + scheduling guide · Community engagement KPIs: upvote targets, comment response time, maker comment strategy · Platform compliance guide: understanding PH's quality signals and organic growth · Post-launch 72-hour plan: maintaining momentum, conditions for Weekly #1 · Case studies: 3 different categories (AI Tool, App, SaaS) dissected Expected outcomes: Product Hunt Daily #1 ranking · strong community engagement on launch day · 3,000–10,000+ sign-ups (depending on product-market fit) 🇨🇳 Product Hunt 打榜完整攻略 | 🇯🇵 Product Hunt ローンチガイド | 🇰🇷 Product Hunt 런칭 가이드 Keywords: Product Hunt #1, Product Hunt launch strategy, PH ranking algorithm, PH #1 daily winner, Product Hunt tips, product launch, startup launch, ranking optimization, PH gallery optimization, Community engagement strategy, maker community engagement, product strategy, launch tactics, how to win Product Hunt, product hunt best practices

Startup Launch Day Playbook — Hour-by-hour execution checklist for your big launch day. Covers timezone timing, channel sequencing, real-time crisis management, and post-launch momentum. By @WeiYipei.

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。