「action」の検索結果

Use when handling external API rate limits (e.g., OpenAI 429s, HubSpot or Stripe rate limits), preventing duplicate work from rapid event bursts (debouncing user actions), spreading load over time, ensuring per-tenant fairness, processing events in batches, limiting concurrent runs of the same operation, or assigning priority to important runs. Covers Inngest flow control: concurrency limits with keys, throttling, rate limiting, debounce, priority, singleton, and event batching.

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Build or audit Next.js 16 App Router apps using a next-beats-style React Server Components architecture. Use when scaffolding a new app, adding a feature, reviewing an existing app, refactoring route-loader-shaped pages into feature-owned async server components, deciding where queries/actions/components live, keeping pages synchronous with `params.then()`, placing Suspense boundaries, choosing the client/server boundary, designing skeletons, preventing CLS, or following Cache Components Instant Navigation. Also use when the user asks about RSC composition, components receiving IDs instead of route params, `'use cache'`, `cacheTag`, `updateTag`, static-shell prerendering, or making an app easier for AI agents to modify.

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Implement idempotency keys and handling to ensure operations can be safely retried without duplicate effects. Use when building payment systems, APIs with retries, or distributed transactions.

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Use when the user wants to design, redesign, shape, critique, audit, polish, clarify, distill, harden, optimize, adapt, animate, colorize, extract, or otherwise improve a frontend interface. Covers websites, landing pages, dashboards, product UI, app shells, components, forms, settings, onboarding, and empty states. Handles UX review, visual hierarchy, information architecture, cognitive load, accessibility, performance, responsive behavior, theming, anti-patterns, typography, fonts, spacing, layout, alignment, color, motion, micro-interactions, UX copy, error states, edge cases, i18n, and reusable design systems or tokens. Also use for bland designs that need to become bolder or more delightful, loud designs that should become quieter, live browser iteration on UI elements, or ambitious visual effects that should feel technically extraordinary. Not for backend-only or non-UI tasks.

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Use when the user explicitly asks for the Inngest REST API v2, raw HTTP, OpenAPI, API docs, API authentication, or an endpoint that the Inngest CLI does not expose. Covers api-docs.inngest.com, llms.txt, the OpenAPI v2 spec, Bearer authentication with API keys or signing keys, production and local base URLs, raw curl/fetch requests, request-shape discovery, pagination, secret redaction, and when to prefer the `inngest-api-cli` 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。

Audit and fix accessibility in Core UI. Use when changing or reviewing buttons, links, forms, validation, dialogs, menus, tabs, tables, keyboard interactions, focus management, accessible names, announcements, contrast, touch targets, reduced motion, or WCAG/a11y behavior.

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Design engineering principles and patterns for building polished, accessible web interfaces. Use this skill when building UI components, reviewing frontend code, implementing forms, handling touch interactions, optimizing performance, or creating marketing pages. Triggers on: design engineering, UI polish, input fields, form validation, button states, touch devices, mobile UX, accessibility, a11y, keyboard navigation, aria labels, font rendering, typography, layout shift, z-index, animations, transitions, easing, hover effects, tap targets, iOS Safari, prefers-reduced-motion, marketing pages, landing pages, dark mode, theme switching, scrollbars, gradients, shadows, virtualization, preloading.

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Deploy applications and websites to Vercel. Use when the user requests deployment actions like "deploy my app", "deploy and give me the link", "push this live", or "create a preview deployment".

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Use when building email features, emails going to spam, high bounce rates, setting up SPF/DKIM/DMARC authentication, implementing email capture, ensuring compliance (CAN-SPAM, GDPR, CASL), handling webhooks, retry logic, making emails accessible (alt text, headings, contrast, screen readers), or deciding transactional vs marketing.

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Apple's approach to interface design and fluid, physical motion, translated for the web. Use when building or reviewing gesture-driven UI, spring animations, drag/swipe/sheet interactions, momentum and interruptible transitions, translucent materials and depth, typography (optical sizing, tracking, leading), reduced-motion, or the design foundations (feedback, spatial consistency, restraint) behind Apple-style interfaces.

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Use when building any system where email content triggers actions — AI agent inboxes, automated support handlers, email-to-task pipelines, or any workflow processing untrusted inbound email. Always use this skill when the user wants to receive emails and act on them programmatically, even if they don't mention "agent" — the skill contains critical security patterns (sender allowlists, content filtering, sandboxed processing) that prevent untrusted email from controlling your system.

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Maintain the canonical TASKS.csv with permanent IDs, provenance and lifecycle history; list, add, update, complete, cancel or import selected actions.

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Extract sourced FF&E specifications from supplied product PDFs into reviewable structured data. Use for fact sheets, spec sheets and price books; not web-page capture, EPD extraction or automatic schedule adoption.

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Turn supplied meeting notes or transcripts into sourced minutes, keeping discussion, statements, confirmed/proposed decisions and proposed actions distinct.

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Look up NYC ACRIS deeds, mortgages, liens, sales, and ownership history. Use for property transactions or recorded ownership; not permits or violations.

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Inspect or save reusable FF&E library products through reviewed, guarded transactions. Use for explicit library imports, corrections or recovery; adopted selections and workbook reconciliation belong to master-schedule.

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NCBI Gene via E-utilities: curated records across 1M+ taxa. Official symbols, aliases, RefSeq IDs, summaries, coordinates, GO, interactions. Use for gene ID resolution and cross-species function queries. For sequences use Ensembl; for expression use geo-database.

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CLI for VCF/BCF: filter, merge, annotate, query, normalize, compute stats. Core post-variant-calling: quality filtering, multi-sample merging, rsID annotation, genotype extraction. Samtools companion in HTSlib. Use GATK for complex indel realignment during calling; use VCFtools for population genetics stats.

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Cancer genomics (TCGA et al.) via cBioPortal REST API. Retrieve somatic mutations, CNAs, expression, clinical data (survival/stage/treatment) across thousands of studies. Use for TMB, oncoprints, survival analysis. For population frequencies use gnomad-database; for drug-gene interactions use opentargets-database.

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Build, read, validate, modify SBML biological network models via the libSBML Python API. SBML Levels 1–3, reactions/kinetic laws, species, rules, FBC extension for flux balance, conversion. Interoperates with COBRApy, Tellurium/RoadRunner, COPASI. Use when programmatically constructing ODE or constraint-based metabolic/signaling models in SBML.

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Parse local DrugBank XML for drug info, interactions, targets, and properties. Search by ID/name/CAS, extract DDIs with severity, map targets/enzymes/transporters, compute SMILES similarity. Primary via local XML; REST API rate-limited (3k/month dev). For live bioactivity use chembl-database-bioactivity; for compound properties use pubchem-compound-search.

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Query FDA drug labels (DailyMed) via REST API. Search structured product labels (SPLs) by name, NDC, set ID, or RxCUI; get indications, dosage, warnings, adverse reactions, packaging. No auth. For adverse events use fda-database; for DDIs use ddinter-database.

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PyTorch-based ML platform for drug discovery: graph molecular representation learning, property prediction (ADMET, activity), retrosynthesis, drug-target interaction (DTI), and pretraining on large molecular datasets. Provides GNN layers (GraphConv, GAT, MPNN), pretrained models, and benchmark datasets.

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Conducting systematic, scoping, and narrative literature reviews. Covers PRISMA/PRISMA-ScR protocols, search strategy (Boolean, MeSH), database selection (PubMed, Scopus, Web of Science, Embase), screening, data extraction, evidence synthesis (narrative, meta-analysis, thematic), and reporting. Use when planning or executing a formal literature 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。