6,417 件 ・ 関連度順
概要と使いどころ
Define, validate, run, resume, and audit repo-local multi-step automations with current `asc workflow` and `.asc/workflow.json`, including step outputs and safe release/TestFlight workflows.
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。
Search the live web via Perplexity Search API. Use when you need current documentation, release notes, vendor pages, news, domain-constrained web search, or date/recency filtering. Not for local codebase search or stable docs already in context.
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。
Econometrics skill for OLS regression and linear models. Activates when the user asks about: "run OLS", "linear regression", "ordinary least squares", "interpret regression results", "heteroskedasticity", "multicollinearity", "regression assumptions", "robust standard errors", "GLS", "WLS", "fit a regression model", "check regression diagnostics", "OLS假设", "最小二乘法", "线性回归", "回归系数", "残差检验", "异方差", "多重共线性", "普通最小二乘", "稳健标准误", "回归诊断"
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。
Econometrics skill for instrumental variables and treatment effect estimation. Activates when the user asks about: "instrumental variables", "IV estimation", "2SLS", "two-stage least squares", "endogeneity", "weak instruments", "first stage", "Sargan test", "overidentification", "propensity score matching", "PSM", "average treatment effect", "ATT", "LATE", "local average treatment effect", "endogenous regressor", "instrument validity", "工具变量", "两阶段最小二乘", "内生性", "弱工具变量", "倾向得分匹配", "平均处理效应", "处理效应", "局部平均处理效应"
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。
Econometrics skill for time series analysis. Activates when the user asks about: "time series", "stationarity", "unit root test", "ADF test", "KPSS test", "ARIMA", "ARMA", "autocorrelation", "ACF", "PACF", "VAR model", "VECM", "Granger causality", "cointegration", "impulse response function", "forecast", "seasonal decomposition", "ARCH", "GARCH", "时间序列", "平稳性检验", "单位根", "自回归", "格兰杰因果", "协整", "脉冲响应", "预测", "向量自回归"
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 synthesized from the Guide to the Systems Engineering Body of Knowledge (SEBoK) v2.14 by the BKCASE Project. Use for the established systems-engineering canon: SE fundamentals and foundations (systems science, systems thinking, the systems approach, modelling/MBSE), the life cycle (ISO/IEC/IEEE 15288/24748/24774 process, development & agile approaches, model selection/tailoring), technical management, concept definition, architecture/realization (the Vee), V&V, maintenance/sustainment, SE standards, and the application contexts (product, service, enterprise, systems-of-systems, healthcare), plus enabling SE (business/team/individual), related disciplines, quality attributes/specialty engineering, implementation examples, and emerging knowledge. Good for grounding background, vocabulary, and prior-art positions across all eight SEBoK Parts. SCOPE LIMITS: this pack is SEBoK v2.14 only (released 2026-05-18); it synthesizes and signposts rather than reproducing the source — it does not reproduce ISO/IEC/IEEE standard text, is a curated guide (not a complete textbook), and is thin on AI-integration depth (Part 8 is a staging area, not settled practice). LICENCE: content is CC BY-NC-SA 3.0 — non-commercial use only, attribution to the BKCASE Project required, share-alike obligations carry forward.
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 NIST AI Risk Management Framework 1.0 (NIST AI 100-1). Use for AI risk management, trustworthiness characteristics, GOVERN/MAP/MEASURE/MANAGE functions, bias management, AI actor roles, TEVV, AI RMF profiles, and socio-technical risk framing. Does not cover sector-specific regulations, EU AI Act, ISO 42001 in depth, or the AI RMF Playbook tactical detail.
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。
Guidance on writing high-quality requirements: the requirement quality characteristics and the EARS (Easy Approach to Requirements Syntax) patterns — ubiquitous, event-driven, state-driven, optional-feature, unwanted-behaviour — for unambiguous, singular, verifiable requirements, plus defect/anti-pattern review and verification & traceability. Use when authoring or reviewing requirements. Original synthesis citing EARS (Mavin et al.) and NASA public-domain guidance; not a reproduction of ISO/IEC/IEEE 29148 or the EARS paper.
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 NASA NPR 7123.1D — Systems Engineering Processes and Requirements. Use for the 17 common SE processes (SE Engine), life-cycle reviews and entrance/success criteria, SEMP structure and content, ETA roles and tailoring/customizing rules, Technology Readiness Levels (TRL 1–9), SE work product maturity terminology, contracted project oversight, and MOE/MOP/TPM measurement frameworks. Does not cover NASA programme financial management, human spaceflight medical standards, or software engineering requirements in depth (see NPR 7150.2 for the latter).
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 OMB Circular A-94 (revised 2023-11-09) on guidelines and discount rates for benefit-cost and cost-effectiveness analysis of federal programs. Use for BCA principles, benefit/cost measurement, discount-rate policy, uncertainty/sensitivity, distributional incidence, and decision reporting. Rescoped at build to A-94 only (Army CBA Guide unreachable). Does not replace A-11 budget preparation detail, agency-specific statutory BCA rules, or Army CEAC process templates.
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 NASA Probabilistic Risk Assessment Procedures Guide (NASA/SP-2011-3421, 2nd ed.). Use for quantitative PRA of aerospace and safety-critical systems: the risk triplet and scenario logic stack (MLD, ESD, event trees, fault trees, minimal cut sets), Bayesian data collection and parameter estimation, aleatory/epistemic uncertainty modeling and common-cause failure (Alpha Factor, beta-factor, CCBE), human reliability analysis (THERP, CREAM, NARA, SPAR-H), context-based software risk (CSRM), physics-based and structural/phenomenological models (stress-strength, limit states, FORM/SORM, NASGRO, range safety), uncertainty propagation and importance measures (F-V, RAW, Birnbaum, DIM), and launch-abort modeling with worked PRA examples. This is the QUANTITATIVE engine beneath NASA's risk doctrine — for the qualitative RIDM/CRM decision framework use the nasa-risk pack. Thin on programme management, organisational risk governance, and non-aerospace regulatory contexts; aerospace-focused and anchored to the 2011 second edition.
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 MIL-STD-881F (13 May 2022), DoD Standard Practice for Work Breakdown Structures for Defense Materiel Items. Use for Program/Contract/Subcontract WBS construction, 100% rule and dictionary discipline, commodity appendices A–J, Common Elements (K), sustainment CES reporting (L), Government ST&E (M), and RFP/post-award implementation. Technical planning & WBS and PM/measurement focus—not a cost manual substitute for CAPE policy.
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 DoD Manufacturing Readiness Level (MRL) Deskbook (Version 2022, OSD ManTech / Joint Service–Industry MRL Working Group). Use for assessing and managing manufacturing maturity and producibility risk in defense acquisition: the 10-level MRL scale and its non-linear, target-not-grade nature; the five demonstration environments (laboratory → production-relevant → production-representative → pilot line → production line); the nine manufacturing-risk threads and sub-threads; how MRL targets map onto Adaptive Acquisition Framework milestones (MRL 4/6/7/8/9); conducting an MRL Assessment; building a Manufacturing Maturation Plan; putting MRL maturity into contract language (Section L/M, SOW, AS6500A, DIDs); tailoring criteria for S&T, single-system, ship, sustainment, and industry contexts; and screening operational-technology cybersecurity. Manufacturing-readiness focused — it measures producibility risk and frames mitigation; it is NOT a technology-readiness (TRL) guide, a full systems-engineering process model, a cost-estimating or schedule guide, or a deep cybersecurity audit method (see sibling packs for those).
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 NASA Cost Estimating Handbook (CEH) v4.0. Use for NASA's 12-step, three-part cost estimating process (Project Definition, Cost Methodology, Cost Estimate); the WBS, CADRe, and ONCE data backbone; estimating methodologies (analogy, parametric/CER, engineering build-up, EVM, Delphi); ground rules and assumptions, data normalization and inflation (BY/CY/RY); the point-estimate-to-S-curve cost-risk chain and Unallocated Future Expense (UFE) reserves; Joint Cost and Schedule Confidence Level (JCL) analysis via probabilistic cost-loaded schedules; decision-support analyses (sensitivity, trade studies, make/lease-vs-buy, affordability, CAIV); and economic analysis (discounting, NPV, OMB Circular A-94). The cost-estimating complement to NASA's SE and risk packs. Does NOT reproduce the handbook's tool-specific procedures, worked examples, or appendix-depth methods (it names the appendices), nor cover full DoD/GAO cost guides, schedule construction, or formal risk management beyond the cost interface.
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 NASA Digital Engineering Acquisition Framework Handbook (NASA-HDBK-1004). Use for acquiring digital/model-based data under contract: the Data Requirements Description (DRD) instrument and the Appendix A DRD suite (TDP, MBx, CDMP, CSA, CR, FCA/PCA, SDT, EMDAL, REF, MRI); the Data Type 1-5 control scale; SOW/RFP/Cost-Volume contract language and data rights (FAR/DFARS, Table 3 license categories); the Requirements Exchange Format (REF/ReqIF, UUID schema) and Master Records Index; model-based data definition (MBSE, CAD/PMI, M&S credibility, BOM, release-state mapping, PLM); digital data collaboration, the four owned architectures, and interoperability standards (MIL-STD-31000, STEP/ISO 10303, ASME Y14); the four MBE interoperability use cases; and MBE Plan development. Scope is the acquisition and contracting side of digital engineering. Does not reproduce ISO/STEP/ASME/MIL-STD/SAE standard text, and the companion NASA-HDBK-1009A carries the deeper digital-engineering practitioner detail this handbook defers to.
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 Schedule Assessment Guide (GAO-16-89G, 2015). Use for building, assessing, or auditing a program's integrated master schedule (IMS): the ten best practices of a reliable schedule (capture, sequence, resource, duration, traceability, valid critical path, reasonable total float, schedule risk analysis, updating, baseline), the four characteristics (comprehensive, well-constructed, credible, controlled), critical path method arithmetic (forward/backward pass, total float), schedule risk analysis (Monte Carlo, contingency), statusing and baseline control, and the Appendix II audit triad. Scheduling/time-management oriented: it is the authority on schedule reliability, but it is NOT a cost-estimating guide, a full SE process model, an agile guide, or a risk-management framework — see sibling packs (gao-cost, dau-se-guidebook, nasa-schedule, nasa-risk) for those.
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 NASA NPR 7150.2D — NASA Software Engineering Requirements. Use for NASA's Agency-wide software-engineering requirements: the software classification scheme (Classes A–F), the Requirements Mapping Matrix and class-driven applicability, tailoring and Technical Authority governance, the SWE-### 'shall' requirements across software management (Ch 3) and the engineering life cycle (Ch 4), the supporting disciplines (configuration management, risk, peer reviews/inspections, measurement, non-conformance — Ch 5), safety-critical and cybersecurity requirements, IV&V applicability, traceability, and the Appendix A definitions. Does not cover detailed software-assurance/safety procedures (see NASA-STD-8739.8), implementation how-to and document content (see NASA-HDBK-2203), the systems-engineering process framework (see NPR 7123.1), or program/project management (see NPR 7120.5).
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 Technology Readiness Assessment Guide (GAO-20-48G, 2020). Use for evaluating technology maturity in acquisition programs and projects: Technology Readiness Levels (TRL 1–9) and related scales (MRL, IRL), identifying Critical Technology Elements (CTEs), the five-step process for a high-quality TRA, the four characteristics of a reliable TRA (comprehensive, well-documented, credible, defensible), Technology Maturation Plans (TMPs), and using TRA results in knowledge-based, milestone decisions. Assessment/maturity oriented: it measures readiness and frames risk — it is not a full SE process model, cost-estimating, or schedule guide (see sibling packs for those).
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 DoD M&Q BoK (the Manufacturing and Quality Engineering Body of Knowledge), v3.0 of July 2025, published by OUSD(R&E). Use for the manufacturing-and-quality view of the defense acquisition life cycle: what M&Q engineers do across the Adaptive Acquisition Framework phases (Pre-MDD, Materiel Solution Analysis, Technology Maturation and Risk Reduction, Engineering and Manufacturing Development, Production and Deployment, Operations and Support); the twelve M&Q threads (A–L) built on the '5 Ms'; manufacturing feasibility and producibility; Manufacturing Readiness Levels and assessments; Key/Critical Characteristics and process capability (Cp/Cpk, SPC); the standards stack (AS6500, AS9100/ISO 9001, AS9103, AS9145); technical reviews (ASR/PDR/CDR/PRR) and milestone gates; DCMA surveillance; industrial base and supply chain; and sustainment (LCSP, IPS, ILA, ISR). Scope is DoD acquisition M&Q practice on the Major Capability Acquisition path — it is a best-practice compilation, not policy, and is thin on detailed shop-floor process engineering, contract law, and the full text of the cited industry standards (which it names but does not reproduce).
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 DoD Digital Engineering Strategy (2018, OUSD/ODASD(SE)). Use for the Department of Defense's five digital engineering goals — (1) formalize the development, integration, and use of models; (2) provide an enduring authoritative source of truth; (3) incorporate technological innovation; (4) establish supporting infrastructure and environments; (5) transform the culture and workforce — plus their focus areas, the document-to-model and design-build-test→model-analyze-build shifts, model formalisms/provenance, governance and access control of the authoritative source of truth, and the coordinate→plan→pilot→sustain rollout. A vision-and-policy strategy, deliberately non-prescriptive. Does NOT provide a how-to method, tool tutorials, MBSE/SysML mechanics, or implementation-plan content; thin on metrics, acquisition-policy detail, and step-by-step procedure. Excludes the CAC-gated Digital Engineering Body of Knowledge (DEBoK).
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 pack from 'Towards Digital Engineering: The Advent of Digital Systems Engineering' (Huang et al., Old Dominion University, arXiv:2002.11672, 2020). Use when reasoning about digital engineering, the DoD Digital Engineering Strategy and its five goals, the Authoritative Source of Truth, digitalization vs. digitization, digital augmentation, digitalized models, provenance, unique identification, and the four-level (vision/strategy/action/foundation) DSE research framework. Covers the conceptual/vision layer of the field. SCOPE LIMITS: this is a single ~28-page vision paper, not a methods handbook — it is thin on step-by-step procedures, tooling, case studies, and quantitative evaluation, and it predates 2020 so it does not cover later digital-engineering standards or recent AI/LLM advances. Use the sebok pack for established SE canon and MBSE depth.
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。
Resolución estructurada de problemas en 6 pasos: Definición del problema, objetivos, lluvia de ideas, evaluación, implementación y revisión.
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。
Procedimiento estándar para la incorporación de nuevos proyectos de software: análisis de funciones, revisión de calidad de código, lista de verificación e integración de tareas.
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。
Procedimiento estructurado de 6 pasos para mejorar, renovar o reconstruir pipelines existentes, carpetas de proyectos individuales, estructuras de documentación o stacks de software. Direccionable como "pipeline optimizer" (para pipelines temáticos completos, p. ej. de software, investigación o desarrollo de videojuegos) o "project-folder optimizer" (para carpetas de proyectos individuales dentro de un pipeline, p. ej. una herramienta de software o proyecto de artículo académico). Se activa ante tareas como "mejorar pipeline X", "optimizar el stack", "reconstruir Y", "renovación", "refactorización de pipeline", "limpiar carpeta de proyecto", "mejorar estructura de carpetas", "unificar convenciones", "consolidación de documentación", "integrar en sistema existente" o cualquier intervención sustancial en estructuras establecidas. Proporciona análisis del estado actual, aclaración del propósito, boceto del estado ideal, plan de brechas, identificación empírica de puntos de dolor y reevaluación con subagentes limpios. Previene estándares paralelos, duplicación y rupturas en el pipeline.
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。