「cui」の検索結果

JLCPCB fabrication and assembly preparation, quoting, and constraints. Load the canonical Circuit Weaver skill from `skills/jlcpcb/SKILL.md`.

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。

Comprehensive expertise in Spanish cuisine, from traditional tapas to modern paella techniques, regional specialties, and authentic ingredient selection

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。

Master of Italian cuisine including pasta, risotto, regional specialties, and traditional techniques

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。

Authentic Mexican cooking expert covering moles, salsas, tacos, and regional Mexican dishes

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。

Expert in pan-Asian cuisines and modern fusion techniques across Chinese, Japanese, Thai, Vietnamese, and Korean cooking

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 robust error handling across TypeScript, Python, and Go. Covers typed errors, error boundaries, retries, circuit breakers, and user-facing error messages.

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。

Complete shadcn/ui component management for adding, searching, fixing, styling, and composing UI in any React project. Use this skill whenever the user mentions shadcn, shadcn/ui, adding UI components, building forms or dashboards with Radix, working with component registries (@shadcn, @magicui, @tailark), or any task involving npx shadcn CLI commands. Also trigger when the user asks to fix styling issues, build a settings page, dashboard, or modal flow in a project that uses shadcn/ui — even if they don't say "shadcn" explicitly. This skill manages the full component lifecycle: search, add, view, diff, smart-merge upstream updates, and enforce critical rules for forms, composition, styling, and icons.

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Drafts a Petition for Writ of Certiorari to the U.S. Supreme Court under Rules 10, 13, 14, and 33. Enforces Rule 10 criteria (circuit splits, precedent conflicts, important federal questions), jurisdictional verification under 28 U.S.C. §§ 1254/1257, timeliness, document structure, and booklet formatting. Use when seeking discretionary Supreme Court review, filing a cert petition, or drafting a writ of certiorari.

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。

Drafts a FRAP 26.1-compliant Certificate of Interested Persons identifying all parties, entities, and affiliates with financial or legal interests in a federal appeal. Adapts to circuit-specific local rules. Use when filing appeals, corporate disclosure statements, or conflict-of-interest certificates in U.S. Courts of Appeals.

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Generates appellate-filing-ready Tables of Contents, Tables of Authorities, and Certificates of Compliance under FRAP 32(g) and related rules. Produces defensible word-count calculations with transparent exclusions and anti-hallucination guardrails for pagination and citations. Covers federal circuits, state appellate courts, and U.S. Supreme Court variations. Use when building TOC, TOA, compliance certificates, formatting briefs for filing, or when the user mentions word count calculation, FRAP 32 compliance, appellate brief assembly, or page numbering.

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。

Tests genotype-environment associations and identifies adaptive loci while correcting for the four-confound landscape (structure, demography, background selection, sampling design) using LFMM2 with mandatory K via sNMF cross-entropy elbow (LEA 3), BayPass Core/AUX/C2/IS with Omega covariance matrix, RDA / pRDA for polygenic adaptation (Forester 2018; requires imputed genotypes), OutFLANK with trimmed FST null, pcadapt, gradient forests (Ellis-Smith-Pitcher 2012, NOT mis-cited Ellis-Manel), Capblancq & Forester 2021 RDA Swiss-army-knife, genomic-offset prediction with Lind & Lotterhos 2025 three-regime caveat, Lotterhos-Whitlock sampling optima, Wang & Bradburd 2014 IBD vs IBE, and Circuitscape + ResistanceGA. Use when identifying adaptive loci across gradients, choosing K for LFMM2, deciding among GEA methods, predicting maladaptation with the novel-environment caveat, distinguishing IBD vs IBE, or optimizing sampling design.

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Designs adaptive clinical trials including group-sequential (O'Brien-Fleming, Pocock, Lan-DeMets spending), sample-size re-estimation (blinded Friede-Kieser, unblinded Cui-Hung-Wang, Mehta-Pocock promising zone), seamless Phase 2/3 with treatment-arm selection, population enrichment, and response-adaptive randomisation. Covers FDA 2019 Final Adaptive Designs Guidance, FDA 2022 Master Protocols, and ICH E20 Step 2b/3 draft (June 2025, NOT final). Use when planning interim analyses, sample-size re-estimation, or master/platform-trial designs.

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。

Identifies super-enhancers from H3K27ac, MED1, or BRD4 ChIP-seq using ROSE, ROSE2, LILY, HOMER -style super, and ENCODE dELS cross-referencing. Handles peak stitching parameters, ranking choices, hockey-stick inflection, marker choice (H3K27ac vs MED1/BRD4), and cross-condition comparison with spike-in normalization. Constructs core regulatory circuitry (Saint-Andre 2016) from SE-encoded TFs. Use when identifying cell-identity / cancer-associated regulatory domains, comparing super-enhancers between conditions, identifying master transcription factor networks, or predicting BET-inhibitor responsiveness.

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。

Design error handling strategies for TypeScript and Python applications — exception hierarchies, Result/Either types, retry patterns, error boundaries, and structured error logging. Use when designing error handling architecture, choosing between exceptions and Result types, implementing retry logic, or building error recovery flows. Activate on "error handling", "exception hierarchy", "Result type", "retry pattern", "circuit breaker", "error boundary", "Pokemon exception". NOT for debugging specific runtime errors, logging infrastructure setup, or monitoring/alerting configuration.

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 introducing a feature flag system, choosing between release flags / experiment flags / operational flags / kill switches / permission flags, designing percentage rollouts, building kill-switch + circuit-breaker patterns, structuring evaluation contexts (user IDs, plan tier, region), or building flag-cleanup discipline. Triggers: LaunchDarkly / GrowthBook / Unleash / Flagsmith / OpenFeature, percentage ramp, sticky bucketing, kill switch wired to alerting, "we still have flags from 2 years ago", flag debt, OpenFeature provider, evaluation context, default value on provider failure. NOT for A/B test statistical analysis (separate skill), traffic-split routing at the edge, build-time bundler defines, or environment-based config.

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。

Classify the severity of hypercalcemia at the bedside — mild, moderate, or severe — by combining the corrected/ionized calcium level with the acuity and symptom pattern, and decide whether the patient needs urgent therapy. Trigger when a clinician asks "is this hypercalcemia severe", "how bad is this calcium level", "when to admit for hypercalcemia", "is this hypercalcemic crisis", "how to grade hypercalcemia", or shares a calcium level and asks how worried to be.

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。

Expert-level applied electromagnetics covering transmission lines, waveguides, antennas, microwave circuits, EMC, and computational EM methods.

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。

Expert-level neuroscience covering neuronal physiology, synaptic transmission, brain regions and circuits, neuroplasticity, neurodegenerative diseases, and computational neuroscience.

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。

Expert-level electromagnetism knowledge. Use when working with electric fields, magnetic fields, Maxwell's equations, electromagnetic waves, circuits, Coulomb's law, Gauss's law, Faraday's law, Ampere's law, or electromagnetic induction. Also use when the user mentions 'electric field', 'magnetic field', 'Maxwell', 'Gauss law', 'Faraday', 'Ampere', 'capacitor', 'inductor', 'electromagnetic wave', 'Lorentz force', 'Poynting vector', or 'dielectric'.

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。

Decompose systems into microservices and apply canonical distributed-systems patterns. Use this skill whenever the user wants to split a monolith into services, design service boundaries, choose between microservices and a modular monolith, implement sagas or the outbox pattern, set up CQRS or event sourcing, configure an API gateway or BFF, add resilience (circuit breakers, retries, timeouts), or implement distributed tracing with OpenTelemetry. Also trigger for distributed transactions, service discovery, event-driven architecture, contract testing with Pact, canary releases, and idempotent consumers.

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 picking a mobile E2E framework — Detox / Appium / Maestro / XCUITest / Espresso — or planning iOS Simulator / Android Emulator coverage in CI for RN, Expo, or native apps.

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 picking a failure-reporting strategy — exceptions vs Result types, recoverable vs not, retry / circuit-breaker / graceful degradation — decision framework only, catalogues externalized.

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。

Crie designs de websites premium com qualidade Awwwards como componentes React (.jsx) que pareçam ter sido construídos por uma agência de primeira linha cobrando R$250k+ por projeto. Use esta skill sempre que o usuário pedir um website, landing page, portfólio, ou componente web que deve parecer caro, premium, luxo, editorial, high-end, ou de qualidade agência. Também dispare quando o usuário mencionar Awwwards, FWA, design premiado, ou referenciar marcas como Apple, Aesop, Bottega Veneta, Stripe, ou qualquer marca luxury/fashion/design-forward. Dispare quando o usuário disser coisas como "faça parecer profissional", "faça parecer caro", "quero que fique realmente bom", "design de alta qualidade", "não genérico", ou expressar insatisfação com aesthetics típicos de websites gerados por IA. NÃO dispare para dashboards, admin panels, ferramentas internas, ou UI funcional onde aesthetics são secundárias à utilidade — a skill padrão frontend-design cuida disso.

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。

Fornece orientação para pesquisa de interpretabilidade mecanística usando TransformerLens para inspecionar e manipular internals de transformers via HookPoints e caching de ativações. Use ao fazer engenharia reversa de algoritmos de modelos, estudar padrões de atenção ou realizar experimentos de activation patching.

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。