「claims」の検索結果

Database of insurance claim precedents and comparable settlements for evaluating claim values

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。

当用户需要梳理保险理赔案件的事实时间线、重建出险到结案的事件顺序、整合病历报案材料审核记录中的时间信息、识别时间冲突或生成适合理赔审核调查复核客服使用的标准化时间轴时使用本 skill。适用于汇总理赔系统案件记录、报案记录、出险说明、门急诊记录、住院记录、手术记录、检查检验报告、病历首页、出院小结、发票或费用清单、补件记录、审核记录、调查记录、结案记录、客服工单记录、沟通备注、OCR 文本、PDF 文档和截图转写内容，提炼关键时间节点、阶段划分、时间间隔、时间冲突和后续核查建议。

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。

Schadenfall-Guide und Entscheidungslogik. Leitet Schritt fuer Schritt durch jeden Schadenfall (Unfall, Krankheit, Haftpflicht, Hausrat, Auto, Rechtsschutz, Todesfall, Invaliditaet). Liefert Self-Service-Checklisten, klaert wann und wie melden, welche Nachweise noetig sind, und wie Fristen und Kommunikation laufen. Schweizer Kontext als Standard. Triggert bei Fragen zu Schadenfall, Schadenmeldung, Unfall melden, Versicherung melden, was tun bei Schaden, Schadenformular, Versicherungsfall, Schadenanspruch, Frist versaeumt, Versicherung zahlt nicht, Leistungsablehnung, Einsprache, Schadenregulierung.

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。

当用户需要检查理赔材料是否齐全时使用此 skill。适用于理赔申请前材料预审、缺失材料提醒、材料规范性检查等场景。

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。

当用户需要查找与理赔案件相关的保险条款、定位责任定义和免责条文、根据案件事实匹配相关保险条款、提取影响理赔判断的关键条文或输出适合理赔审核、复核、客服支持和调查使用的理赔条款定位结果时使用本 skill。适用于分析理赔系统案件记录、报案信息、门诊或住院病历、诊断证明、重疾身故事故相关资料、费用清单与发票、调查记录、客服或审核备注、OCR 文本、PDF 文档、截图转写内容以及产品条款全文、特别约定、责任免除条款、等待期约定、多次赔付规则、医院范围和条款版本信息，并形成结构化条款索引与摘要。

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。

当用户需要对重疾险理赔申请资料进行结构化受理检查时使用本 skill。它用于识别理赔申请书、身份证明、银行卡信息、诊断证明、出院记录、住院病历、病理报告、手术记录、影像报告、专项检查报告等与重大疾病责任认定相关的关键材料是否齐全、清晰、有效、相互一致，并输出补件清单、受理前检查结果和跟进建议。

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。

当用户需要对医疗险理赔申请资料进行结构化受理检查时使用本 skill。它用于识别理赔申请书、身份证明、银行卡信息、病历、出院记录、费用清单、发票、结算单、检查报告、诊断证明等材料是否齐全、清晰、有效、相互一致，并输出补件清单、受理前检查结果和跟进建议。

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。

Structures cyber insurance evaluation with coverage assessment, limit adequacy, and claims scenario analysis. Use when evaluating cyber coverage, analyzing policy terms, or assessing cyber insurance adequacy.

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。

Evaluates insurtech business models with distribution innovation, underwriting technology, and claims automation. Use when analyzing insurtech, evaluating digital insurance, or assessing insurance technology.

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。

Query the Bear EHR database using the semantic layer. Use when asked about clients, billing, appointments, insurance, clinical data, procedures, scheduling, programs, staff, vitals, diagnoses, revenue, claims, or any data question about the Bear application.

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。

Configures JWT Bearer authentication for .NET APIs. Includes token generation, validation, refresh tokens, and user context extraction from claims.

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。

Traditional Chinese internal arts — tai chi chuan and qigong — as transmitted by classical lineages (Chen, Yang, Wu, Wu/Hao, Sun for tai chi; Ba Duan Jin, Yi Jin Jing, Liu Zi Jue, Wu Qin Xi, and other named sets for qigong) with an emphasis on alignment, rooted stance, breath-posture-mind coordination (the three harmonies), and the traditional lineage voice that Dr. Yang Jwing-Ming's YMAA line preserves. Also covers the safety boundary between traditional practice and clinical "medical qigong" claims. Use for queries about tai chi form learning, qigong set selection, internal-arts pedagogy, and the practical question of where to start.

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。

Climate physics, forcing, feedback, and attribution. Covers the greenhouse effect and radiative forcing, climate sensitivity, fast and slow feedbacks, the paleoclimate record, detection and attribution science, regional climate impacts, and the distinction between weather, climate variability, and forced climate change. Use when reasoning about global warming mechanisms, interpreting temperature or CO2 records, evaluating attribution claims for extreme events, or distinguishing signal from natural variability.

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A/B testing, randomization, sample size calculation, confounding control, and causal inference for data science. Covers the full experimental lifecycle from hypothesis formulation through power analysis, randomization strategies, blocking, factorial designs, sequential testing, and the potential outcomes framework for causal claims. Use when designing experiments, planning A/B tests, calculating sample sizes, or reasoning about causation from data.

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。

Adversarial spec-compliance PR review — cross-references diffs against approved specs, verifies runtime claims against source, detects competing PRs, audits scope/convention compliance. Use before merging.

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 a GitHub issue filed against skillsaw — classify it (bug, feature, docs, question, other), verify its claims against the codebase, enrich it with missing details, and post one structured triage comment. Use when triaging and labeling incoming issues, not to fix them.

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 the primary outcome is creating publishable source-attributed content from URLs or supplied material, including research summaries, articles, briefs, and posts whose factual claims and quotations need reader-visible, verifiable citations.

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。

Rigorous self-assessment checklist before marking any task as complete. Use when about to claim task completion, before final commit, when user asks "is it done?", or when transitioning from implementation to reporting. Prevents premature completion claims by requiring evidence for every assertion.

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 documentation claims against implementation and authoritative repository evidence. Use when checking whether README, architecture, configuration, API, usage, or feature documentation is stale, incomplete, contradicted by code, missing implemented behavior, or inconsistent with a normative contract.

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Runs several language models as independent coders on one labeling or discovery task and treats disagreement as data. Covers diverse panel assembly, independent voting, consensus rules, Cohen’s and Fleiss kappa, Krippendorff’s alpha, correlated-error limits on validity claims, human validation, and reporting. Use when the user asks about a council, panel, ensemble, or jury of models, how to combine model labels, or what kappa or alpha to report for model coders. Single-model codebook and validation work goes to text-classification, per-item confidence to llm-calibration-logprobs.

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 competing products and turn the market gap into credible positioning, messaging angles, and social campaign claims. Use when comparing competitors, writing alternative pages, sharpening product positioning, or planning campaigns against a category narrative.

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。

Fact-check manuscript claims against their cited sources in a per-source Markdown knowledge base. Use to audit claim support, overclaiming, direction, scope, and misattribution after source intake is complete.

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。

Product-only Red Team audit of the FMT/IWE platform: source, manifests, setup/update paths, CI and release provenance, agent permissions and memory, hooks, runtime, roles, schedulers, Day Open/Close, and regression claims. Use before merging or publishing a release, after changes to updater/runtime/security behavior, or when verifying a reported platform defect. Excludes real consumer workspaces and personal customizations; use repository-owned code and synthetic disposable fixtures only.

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 after an Anabasis v2 comparison, checkpoint or system change, and before any sentence claims that a harness, a model or a patch improved something. States what one reading may be said to show: the identities it was made under, the counts kept apart, whether it was repeated, which part of a harness it belongs to, whether a mechanism was live in the run, and whether the measurement is valid as distinct from whether it was exploited. Ends in one fixed report.

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。