「event」の検索結果

When the user wants to optimize their App Store listing for seasonal events, holidays, or trending moments — including keyword opportunities, metadata updates, screenshot theming, and timing strategy. Use when the user mentions "seasonal", "holiday", "Christmas", "New Year", "Valentine's Day", "summer", "back to school", "seasonal keywords", "trending now", "limited time", or wants to capitalize on a calendar event. For general keyword research, see keyword-research. For full metadata rewrites, see metadata-optimization.

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Automate Google Calendar events, scheduling, availability checks, and attendee management via Rube MCP (Composio). Create events, find free slots, manage attendees, and list calendars programmatically.

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Generates complete FAERS-style pharmacovigilance disproportionality research designs from a user-provided drug class, comparator strategy, adverse-event domain, and patient-group stratification. Always use this skill whenever a user wants to design, plan, or build a spontaneous-report safety signal study using FAERS or a similar pharmacovigilance database, especially when the article logic includes product selection, indication-group stratification, MedDRA-based adverse-event extraction, serious-case filtering, suspect-drug and concomitant-exclusion logic, reporting odds ratio analysis, comparator-drug benchmarking, cross-drug comparison, and cautious signal interpretation without causal overclaiming. Covers five study patterns (single-drug disproportionality workflow, multi-drug class comparison workflow, indication-stratified workflow, comparator-controlled signal screening workflow...

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Generates CIOMS I-compliant ICSR narratives from adverse event case data for FDA and EMA regulatory submission. Includes temporal analysis, MedDRA coding, causality assessment using WHO-UMC or Naranjo criteria, and multi-format output.

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Generates complete reference-grounded single-drug adverse-effect network-pharmacology research designs from a user-provided drug, adverse event, and desired evidence depth. Always use this skill when a user wants to design, plan, or upgrade a conventional network-pharmacology study centered on one fixed drug and one fixed adverse-effect endpoint, using drug-target prediction, adverse-event target collection, overlap analysis, PPI hub prioritization, enrichment interpretation, molecular docking, and optional orthogonal transcriptomic or literature validation. Covers five study patterns (canonical hub-first, cardiotoxicity or electrophysiology-oriented, immune-inflammatory adverse effect, organ-toxicity pathway context, translational validation) and always outputs four workload configs (Lite / Standard / Advanced / Publication+) with a recommended primary plan, dependency/evidence map, step-by-step workflow, figure plan, validation strategy, minimal executable version, publication upgrade path, verified-reference pack, and self-critical risk review.

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Generates complete reference-grounded single-drug adverse-effect network-pharmacology research designs from a user-provided drug, adverse event, and desired evidence depth. Always use this skill when a user wants to design, plan, or upgrade a conventional network-pharmacology study centered on one fixed drug and one fixed adverse-effect endpoint, using drug-target prediction, adverse-event target collection, overlap analysis, PPI hub prioritization, enrichment interpretation, molecular docking, and optional orthogonal transcriptomic or literature validation. Covers five study patterns (canonical hub-first, cardiotoxicity or electrophysiology-oriented, immune-inflammatory adverse effect, organ-toxicity pathway context, translational validation) and always outputs four workload configs (Lite / Standard / Advanced / Publication+) with a recommended primary plan, dependency/evidence map, step-by-step workflow, figure plan, validation strategy, minimal executable version, publication upgrade path, verified-reference pack, and self-critical risk review.

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Guides Metronome usage-based billing integration decisions — event ingestion (single and batch, idempotency, billable metrics), contract design (rate cards, overrides, dimensional pricing, products), invoicing lifecycle (grace periods, finalization, Stripe sync), credit and commit management (prepaid, postpaid, thresholds, auto-recharge), and Stripe integration (arrears invoicing, tax providers, line item limits). Use when building, modifying, or reviewing any Metronome integration — including ingesting usage events, creating customers, contracts, or rate cards, designing billable metric group keys, managing credits and commits, configuring invoicing, syncing invoices with Stripe Billing, billing AI applications for token usage, setting up self-serve (PLG) pricing, reviewing customer health, commit burn, portfolios, or renewals, or migrating from Stripe Billing Meters to Metronome.

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Use this skill when creating database schemas or tables for Timescale, TimescaleDB, TigerData, or Tiger Cloud, especially for time-series, IoT, metrics, events, or log data. Use this to improve the performance of any insert-heavy table. **Trigger when user asks to:** - Create or design SQL schemas/tables AND Timescale/TimescaleDB/TigerData/Tiger Cloud is available - Set up hypertables, compression, retention policies, or continuous aggregates - Configure partition columns, segment_by, order_by, or chunk intervals - Optimize time-series database performance or storage - Create tables for sensors, metrics, telemetry, events, or transaction logs **Keywords:** CREATE TABLE, hypertable, Timescale, TimescaleDB, time-series, IoT, metrics, sensor data, compression policy, continuous aggregates, columnstore, retention policy, chunk interval, segment_by, order_by Step-by-step instructions for hypertable creation, column selection, compression policies, retention, continuous aggregates, and indexes.

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Use when oTP behaviors including gen_server for stateful processes, gen_statem for state machines, supervisors for fault tolerance, gen_event for event handling, and building robust, production-ready Erlang applications with proven patterns.

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Build read models and projections from event streams. Use when implementing CQRS read sides, building materialized views, or optimizing query performance in event-sourced systems.

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Write tests using React Native Testing Library (RNTL) v13 and v14 (`@testing-library/react-native`). Use when writing, reviewing, or fixing React Native component tests. Covers: render, screen, queries (getBy/getAllBy/queryBy/findBy), Jest matchers, userEvent, fireEvent, waitFor, and async patterns. Supports v13 (React 18, sync render) and v14 (React 19+, async render). Triggers on: test files for React Native components, RNTL imports, mentions of "testing library", "write tests", "component tests", or "RNTL".

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Writing plays and screenplays in the Chekhovian mode (契诃夫戏剧法) — distilled from the complete plays (没有父亲的人/普拉东诺夫, 伊凡诺夫, 林妖→万尼亚舅舅, 海鸥, 三姊妹, 樱桃园, nine one-acts) plus Chekhov's own letters, Stanislavsky's and Nemirovich-Danchenko's rehearsal notes and 焦菊隐/童道明 criticism. Covers the four-act mood-based structure without central climax, off-stage events, "correct answer stated in Act 1 and refused", the announced event that never happens, three-layer endings (hope monologue + nihilist line + household action), no-villain conflict with environment, every character's object and catchphrase, one-way love chains, half-step-off dialogue, philosophy cut by thirst, the shortest words for the deepest pain, stage-direction and sound discipline, and how Chekhov revised The Wood Demon into Uncle Vanya. Use when the user wants an ensemble drama, a "nothing happens" play, subtext-heavy dialogue, a family estate / provincial story, an anti-climactic structure, or asks how to "Chekhov-ize" a script.

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Set up player input in Unreal Engine 5 with Enhanced Input: Input Actions, Input Mapping Contexts, modifiers and triggers, adding the mapping context, and binding actions by ETriggerEvent. Use when wiring movement/look/jump input, creating IA_/IMC_ assets, binding in C++ or Blueprints, or when the user mentions Enhanced Input, Input Mapping Context, Input Action, IA_/IMC_, or ETriggerEvent.

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Build a PixiJS v8 render layer: create the async Application, load textures with Assets, compose the scene graph with Container and Sprite, drive the ticker loop, wire pointer events, and group draws with render groups. Use when building or debugging PixiJS v8 — when the user mentions PixiJS, Pixi, Application, app.stage, Container, Sprite, Assets.load, app.ticker, or eventMode. Pins the v8 async init() API.

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Use C#/.NET in Godot 4.7: partial classes extending nodes, the PascalCase lifecycle (_Ready/_Process/_PhysicsProcess), [Export] fields, [Signal] delegates as C# events, type-safe node lookup, and calling between C# and GDScript. Use when writing Godot game code in C# (.cs files, .csproj), needing the Godot .NET build, converting GDScript patterns to C#, or wiring Godot signals as C# events.

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Design and harden Roblox client/server networking with RemoteEvent, RemoteFunction, and UnreliableRemoteEvent; server authority, argument and Instance validation, rate limits, proximity/ownership checks, targeted replication, streaming, lifecycle, prediction, and reconciliation. Use for Roblox remotes, exploits, request spam, multiplayer replication, network ownership, high-frequency cosmetic updates, or server/client desynchronization.

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Build event-driven, decoupled Godot 4.7 gameplay with signals and node groups: declare and emit custom signals, connect with Callables (incl. bind/one-shot), and broadcast to many nodes via groups and call_group. Use when wiring node communication in a Godot project, replacing tight references with signals, emitting/connecting events, or porting 3.x connect("sig", self, "method") code.

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Build a smooth client-onboarding process that starts projects on the right foot — clear expectations, the info you need, and a professional first impression that prevents problems later. Use when asked to onboard a new client, create an onboarding process, kick off a client project, or client welcome packet. Produces a welcome and kickoff flow, the intake you need to start, expectation-setting on scope/comms/timeline/payment, a kickoff-meeting agenda, and the templates to reuse — so onboarding is consistent, not improvised, and prevents the scope and communication problems that sink projects.

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Read-only Polymarket market data: events, markets, prices, order books. No trading, no key needed. Trigger phrases: polymarket, prediction markets, market odds, event prices.

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Extract leads from conferences, meetups, hackathons, and podcasts by analyzing speaker lists, sponsor lists, hackathon entries, and podcast guests. Discovers events via Sessionize, Confs.tech, Meetup, Luma, ListenNotes, and Devpost. Looks back 90 days and forward 180 days.

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End-to-end news-triggered signal composite. Takes any piece of news — an article, LinkedIn post, tweet, announcement, event, trend, regulation, product launch, acquisition, layoff, expansion, or any other public event — and evaluates whether the companies or people mentioned are ICP fits. If yes, identifies the connection between the news and your product, finds the right people to contact, and drafts personalized outreach using the news as the hook. Tool-agnostic. Accepts both company-level and person-level news triggers. AUTO-TRIGGER: Load this composite whenever a user shares a URL (LinkedIn post, article, tweet, blog post) or mentions a company/person they "came across", "saw", or "found" from any external source and asks about relevance, fit, ICP match, or whether to reach out. The user does NOT need to explicitly say "outreach" — any signal evaluation request from an external source triggers this.

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Find attendees at conferences/events, research their companies, qualify against ICP, and launch outreach

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Find speakers, hosts, and guest profiles at conferences and events on Luma. Two modes - free direct scrape for hosts, or Apify-powered search for full guest profiles with LinkedIn/Twitter/bio.

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Detects m6A modifications from Oxford Nanopore direct-RNA-seq (DRS) signal using m6Anet (multiple-instance-learning over DRACH 5-mer signal). Covers the upstream pipeline (Dorado/Guppy basecalling -> minimap2 map-ont -> nanopolish eventalign -> m6anet dataprep -> m6anet inference), per-site vs per-read probability including the mod_ratio stoichiometry column, the DRACH-only constraint, minimum-coverage thresholds (20-50 reads/site), multi-condition comparison via xPore/Nanocompore/ELIGOS, Dorado native modification calling (RNA004, 2024+), and the cDNA-vs-DRS distinction (cDNA Nanopore CANNOT detect modifications). Use when calling m6A from ONT DRS without immunoprecipitation, choosing m6Anet vs xPore vs Nanocompore vs ELIGOS vs Dorado native, interpreting probability_modified vs mod_ratio vs per-read probabilities, deciding between m6Anet (known DRACH sites) and Dorado/Remora (genome-wide screening), pinning RNA002 vs RNA004 chemistry and basecaller versions, or troubleshooting eventalign/dataprep failures.

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