「value」の検索結果

価値概念スコアの検証を行う時の観点を説明する

価値分析モデルを作る時の観点を説明する

価値デザインモデルを作る時の観点を説明する

価値記述を作る時の観点を説明する

Use when a learner needs a structured review session prioritizing high-value topics by recency, error history, and upcoming deadlines.

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。

Translate a fuzzy analytical question into a rigorous investigation plan. Interrogates the ask, grounds the plan in the available data dictionary, applies analytical best practices, and produces a structured brief of query specifications for a downstream query-writing skill. Plans, does not write SQL. Use when: "why did X drop", "is there a relationship between A and B", "who are our highest-value customers", "what's driving the change in Y", "investigate this trend", "design an analysis for", "scope this analytical question". (narrative-common)

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Produce a coverage & quality profile of a Narrative dataset (or access rule): row count, per-column null/fill rate, cardinality, ranges, top-values, inferred column shape, and quality flags. Reads bundled stats + sample first, recovers missing/stale stats by configuring and recalculating them, and escalates to a cheap `/write-nql` query only for a measure no stat can provide. Descriptive, not prescriptive. Use when: "profile dataset N", "what does dataset N look like", "coverage and quality of <dataset>", "what id types does N emit", "null rates / cardinality for <dataset>", "is this dataset's stats fresh". (narrative-common)

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Audit a dataset or access rule before it joins an identity-graph build (access rules behave like datasets in NQL here). Enumerates failure modes (hub identifiers, high-degree nodes, suspicious values, over-connected identifiers), tests hypotheses in parallel, quantifies damage by rows / edges / entities, and proposes minimal filters ranked by severity. When issues are found, returns a validated `CREATE MATERIALIZED VIEW` NQL the caller can run to produce a graph-ready clean source; if the data passes, says so and recommends it unchanged. Plans and authors the clean-view NQL; does not execute it. Use when: "audit this dataset before the graph build", "find bad edges in <source>", "check identity data quality", "recommend filters for the graph build", "quantify damage from <identifier_type>", "pre-graph DQ". (narrative-identity)

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。

컬렉션과 그에 붙은 로직(필터·합계·검증)을 전용 클래스로 추출하여 First Class Collection 생성. "일급 컬렉션", "List<Order> 감싸는 클래스", "컬렉션 로직 모아", "/first-class-collection" 요청 시 사용. 단, getter 노출 방지만 필요하면 /encapsulate-collection, 단일 값 타입은 /discover-value-object가 적합. /first-class-collection [commit-ref]로 호출.

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。

반복되는 파라미터 그룹을 객체로 치환(IPO)하거나, 객체에서 꺼낸 값 대신 객체 자체를 전달(Preserve Whole Object). "파라미터 객체", "인자가 너무 많아", "start·end 대신 Range로", "/introduce-parameter-object" 요청 시 사용. 단, 결과 객체가 검증·연산을 가진 도메인 값이면 /discover-value-object가 적합. /introduce-parameter-object [commit-ref]로 호출.

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。

Refactor Swift code toward declarative functional programming with immutability, value types, pure functions, transformations, and closure-based APIs. Use when asked for functional refactoring or `/refactor:functional`.

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Break an application or feature into thin vertical user-story slices with clear acceptance criteria and user-observable value. Use when planning a backlog, decomposing a feature, writing implementation-ready stories, or turning requirements into deliverable slices.

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。

Refactor test files to improve behavioral coverage, clarity, structure, assertions, fixture setup, mocking strategy, and remove implementation-detail or low-value tests. Use when asked to clean up, declutter, consolidate, or improve tests.

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。

Load when evaluating an Islamic software feature, provider policy, AI behavior, moderation, monetization, privacy, defaults, ranking, self-hosting, or deployment model for provider responsibility and Islamic value-sensitive design; not for generic fiqh rulings or ordinary technical implementation.

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Load for cross-layer refactors, record/class or value-object migrations, new services/interfaces/repositories, project references, dependency-direction failures, or deciding whether code belongs in Domain, Application, Persistence/Infrastructure, API, or Blazor; not for a local edit wholly inside an established layer pattern.

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Load for EF Core entities/configurations, semantic value persistence, portable constraints, DbContext/query filters, repositories/specifications, migrations, model snapshots, transactions, or seeded lookup tables; use query-optimization specifically for N+1 or slow SQL diagnosis.

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Full type inference philosophy (never cast, never annotate inferred values), Register module declaration, from narrowing on hooks and Link, strict:false for shared components, getRouteApi for code-split typed access, addChildren with object syntax for TS perf, LinkProps and ValidateLinkOptions type utilities, as const satisfies pattern.

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。

Knowledge base from the FAA Requirements Engineering Management Handbook (DOT/FAA/AR-08/32). Use for writing requirements for real-time, embedded control systems — the avionics/medical slant — via the Handbook's eleven recommended practices: system overview and boundary (monitored/controlled variables), operational concepts (use cases), environmental assumptions, change-tolerant functional architecture (dependency diagrams), safety-driven architecture revision (FHA/PSSA/fault tree), system modes, detailed behavior and performance requirements (ideal value + tolerance + latency), the four-variable model bridge to software requirements (MON/CON/NAT/REQ, IN'/REQ'/OUT', DO-178B mapping), subsystem allocation, and rationale. Carries the Isolette Thermostat and Flight Control System worked examples. Slanted toward control systems with sensors and actuators; thin on information-system / enterprise / agile requirements, on requirements-management tooling, and on the formal-methods internals of SCR/RSML/SpecTRM (it names them, does not re-teach them). Does not reproduce the IEEE/DO-178B/ARP standards it cites.

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。

Knowledge base from the GAO Cost Estimating and Assessment Guide (GAO-20-195G). Use for program cost estimating: the four characteristics of a reliable estimate (comprehensive, well documented, accurate, credible) and the 18 best practices; the 12-step cost estimating process (purpose, plan, technical baseline, WBS, ground rules and assumptions, data, point estimate, sensitivity, risk/uncertainty, document, present, update); estimating methods (analogy, parametric, engineering build-up, learning curves); Monte Carlo risk/uncertainty analysis, confidence levels and contingency; auditing and validating an estimate against the characteristics; earned value management (EIA-748, BCWS/BCWP/ACWP, CPI/SPI/TCPI, EAC, PMB, IBR); and specialized techniques (software cost estimating, learning curves, Analysis of Alternatives, WBS templates, the Green Book internal-control framework). This is the cost-estimating companion to the GAO Schedule and Technology Readiness Assessment guides. Does not cover schedule estimating/risk in depth (see the GAO Schedule Assessment Guide), agency-specific cost models, or the full text of referenced standards (EIA-748, MIL-STD-881D, the Green Book) — these are named, not reproduced.

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Knowledge base from EU Artificial Intelligence Act — Regulation (EU) 2024/1689. Use for AI system classification (high-risk vs prohibited vs general), conformity assessment procedures, GPAI model obligations, transparency and disclosure requirements, governance structure (AI Office / AI Board), penalty tiers, value-chain operator obligations, post-market monitoring, and Annex III use-case analysis. Does not cover EU liability rules for AI (separate proposed directive) or implementation guidance documents issued after July 2024.

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针对抑郁症的行为激活疗法：打破恶性循环、活动监测、周计划制定及基于价值观的活动选择。

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Activación conductual para la depresión: romper el círculo vicioso, registro de actividades, planificación semanal y actividades basadas en valores.

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Поведенческая активация при депрессии: разорвать порочный круг, мониторинг активности, планирование недели и действия на основе ценностей.

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Терапия принятия и ответственности (ACT) Стивенса Хейса: модель Гексафлекс с шестью ключевыми процессами психологической гибкости.

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。