「fno」の検索結果

Use when designing or customizing industry-specific guided processes in Salesforce Industries clouds: insurance claims lifecycle (FNOL through closure), telecom order management decomposition, and utility service request workflows. Trigger keywords: FNOL process design, claims OmniScript customization, Industries order decomposition workflow, utility service order design, insurance claim lifecycle stages, telecom commercial-to-technical order flow. NOT for the Industries object model — use architect/industries-data-model. NOT for Insurance Cloud licensing and policy architecture — use architect/insurance-cloud-architecture.

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Architectures and techniques for neural operators on discontinuous PDE solutions (shocks, contact discontinuities, steep gradients). Covers local-global spectral design (ShockFNO), reflection padding for non-periodic BCs, resolution scaling for shock width, and frequency-band error diagnostics. Use for low-viscosity Burgers, compressible Euler, Riemann problems, or any PDE where standard FNO produces Gibbs oscillations.

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。

Execute Guidewire secondary workflow: Claims processing in ClaimCenter. Use when implementing FNOL, claim investigation, reserves, payments, and settlement. Trigger with phrases like "claimcenter workflow", "create claim", "file fnol", "process claim", "claim settlement", "claim payment".

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。

Execute Guidewire secondary workflow: Claims processing in ClaimCenter. Use when implementing FNOL, claim investigation, reserves, payments, and settlement. Trigger with phrases like "claimcenter workflow", "create claim", "file fnol", "process claim", "claim settlement", "claim payment".

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。

Train neural operators (FNO, DeepONet) to learn solution maps for parametric PDE families. Once trained, solve new PDE instances in milliseconds. Use when you need to solve many instances of the same PDE with different parameters/ICs/BCs.

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Training patterns for autoregressive neural PDE solvers (FNO, DeepONet, CNO). Covers rollout training, noise injection for stability, multi-component loss functions (H1, frequency-sensitive, boundary-aware), per-channel normalization for coupled multi-variable systems, and the PDEBench nRMSE metric. Use when training any neural operator that predicts time-dependent PDE solutions.

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。

Triage an incoming insurance claim: check the coverage trigger against policy wording, band severity and complexity, screen for fraud indicators, set a first-pass reserve range, and route with an SLA. Use when asked to triage a claim, review a first notice of loss (FNOL), assess a new claim, decide fast-track vs adjuster routing, or screen a claim for SIU referral. Produces a structured triage note with coverage view, severity band, indicator screen, reserve range, and a routing recommendation.

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Guidelines for introducing and using nullable reference types (NRT) and System.Diagnostics.CodeAnalysis nullable attributes in C# / .NET codebases. Covers the nullability model, flow analysis, the null-forgiving operator, API design rules, the full attribute catalog (AllowNull, DisallowNull, MaybeNull, NotNull, NotNullWhen, MaybeNullWhen, NotNullIfNotNull, MemberNotNull, MemberNotNullWhen, DoesNotReturn, DoesNotReturnIf), the C# 14 field keyword, incremental migration of legacy codebases, and a code-generation checklist.

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Analyze insurance claims workflow systems for intake optimization, adjudication rules, subrogation, litigation management, and settlement analytics. Use when: 'audit claims process', 'review FNOL workflow', 'evaluate claims automation', 'check subrogation recovery', 'assess claims fraud detection', 'optimize claims cycle time', 'review Guidewire ClaimCenter setup'.

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。

Analyze insurance claims workflow systems for intake optimization, adjudication rules, subrogation, litigation management, and settlement analytics. Triggers: 'audit claims process', 'review FNOL workflow', 'evaluate claims automation', 'check subrogation recovery'.

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Apply a set of Rosetta Stone attribute mappings to a Narrative dataset by wrapping them in a one-shot workflow that calls the `CreateRosettaStoneMappingsIfNotExist` task. Consumes the structured output of `/generate-rosetta-stone-mappings`, normalizes the generator's snake_case to the workflow task's camelCase, re-validates every expression against the dataset's current schema, gates on user approval, submits via `narrative_workflows_create`, polls the triggered run, and reports per-mapping created / conflict / failed. Use when: "apply these mappings to dataset N", "create the Rosetta Stone mappings I just generated", "push the mappings I saved earlier to <dataset>", "productionize this mapping list", "submit the suggested_mappings array". (narrative-common)

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Knowledge and guardrails for the mise + fnox + infisical secrets toolchain, covering secret injection, secret providers, and env var hygiene.

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。