「culture」の検索結果

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 base from the FAA System Safety Handbook (Dec 30, 2000). Use for system safety as a specialty within systems engineering: the five-step safety risk management process (mandated by FAA Order 8040.4 and the AMS), the severity-by-likelihood risk matrix and acceptance bands, the Safety Order of Precedence, system-description models (5M, SHEL(L)), the hazard-analysis activities (PHL/PHA/RHA/SSHA/SHA/O&SHA/HHA), integrated system hazard analysis, the analysis techniques (FTA, FMEA/FMECA, Fault Hazard, Common Cause, Sneak Circuit, Energy Trace), closed-loop hazard tracking (Safety Action Record), and the specialty domains the handbook covers — acquisition-lifecycle safety and contracting (OSA/CSA/SSPP/ISSPP), software safety (control-capability ranking, SHCM, DO-178B), test & evaluation safety, facilities safety, commercial launch safety (AST licensing, expected casualty, MPL/DAMP), system safety training, and Operational Risk Management (ORM) with human/organizational factors. Scope limits: this is a year-2000 FAA how-to; it predates the modern FAA/ICAO Safety Management System (SMS) and Just Culture regulatory frameworks, and many cited orders, advisory circulars, and launch-licensing rules have since been restructured. It is FAA/NAS-centric, leans on MIL-STD-882C, and does not define the SE life-cycle processes themselves (use nasa-npr-7123 / dau-se-guidebook) or reproduce third-party standard text (MIL-STD-882, DO-178B, ISO/IEC/IEEE).

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。

AgriTech Specialist IA — Expert en technologies agricoles (IoT sensors, weather APIs, crop monitoring, supply chain DZ)

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 en technologies agricoles avancées (precision farming, IoT sensors, satellite imagery, yield prediction, DZ 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。

AgriTech Algeria v2 — OAIC, conditions météo, irrigation, sahel, precision farming DZ

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。

A/B Testing Specialist IA — Expert en tests A/B (hypothèses, significativité, multivarié, culture d'expérimentation).

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 en localisation d'agents IA (i18n, l10n, RTL, adaptation culturelle, arabe DZ/darija)

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 taste as a competitive moat and business advantage. In the AI and vibe-coding era, execution is commoditized. Taste is the defensible edge. Use when advising founders on product differentiation, building product culture, evaluating why some products win despite fewer features, or understanding taste as a strategic asset.

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。

Structured exposure habits for building taste through relentless, intentional consumption. Not passive scrolling. Active intake across design, architecture, film, music, food, and culture. Use when building a taste practice, advising on professional development, or diagnosing why someone's design range feels narrow.

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 guidance on iOS accessibility best practices, patterns, and implementation. Use when developers mention: (1) iOS accessibility, VoiceOver, Dynamic Type, or assistive technologies, (2) accessibility labels, traits, hints, or values, (3) automated accessibility testing, auditing, or manual testing, (4) Switch Control, Voice Control, or Full Keyboard Access, (5) inclusive design or accessibility culture, (6) making apps work for users with disabilities.

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 biological experiments so inference is valid by construction, covering Fisher's principles (randomization, replication, local control), the experimental-vs-observational unit distinction and pseudoreplication (Hurlbert 1984; Lazic 2018), randomization mechanics (complete, restricted, stratified, rerandomization, run-order), blocking layouts (randomized complete block, Latin square, incomplete block), factorial designs and interactions, and the split-plot/nested error strata hidden inside multi-batch genomics. Use when deciding the experimental unit and what counts as a replicate, planning randomization and run order, choosing a blocked/factorial/split-plot/nested layout, avoiding pseudoreplication in cell-culture or animal studies, or specifying the random-effects structure of the analysis model. For assigning samples to sequencing batches/lanes/plates and batch-effect correction see experimental-design/batch-design; for regulated clinical-trial randomization see clinical-biostatistics.

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 biological experiments so inference is valid by construction, covering Fisher's principles (randomization, replication, local control), the experimental-vs-observational unit distinction and pseudoreplication (Hurlbert 1984; Lazic 2018), randomization mechanics (complete, restricted, stratified, rerandomization, run-order), blocking layouts (randomized complete block, Latin square, incomplete block), factorial designs and interactions, and the split-plot/nested error strata hidden inside multi-batch genomics. Use when deciding the experimental unit and what counts as a replicate, planning randomization and run order, choosing a blocked/factorial/split-plot/nested layout, avoiding pseudoreplication in cell-culture or animal studies, or specifying the random-effects structure of the analysis model. For assigning samples to sequencing batches/lanes/plates and batch-effect correction see experimental-design/batch-design; for regulated clinical-trial randomization see clinical-biostatistics.

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。

Apply continuous improvement principles to iteratively enhance processes, code quality, team practices, and system performance through small, incremental changes. Use when improving development processes, optimizing workflows, reducing waste, enhancing code quality over time, implementing retrospective learnings, or fostering a culture of continuous improvement.

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。

Build localized client XAML controls with correct lifecycle, culture refresh, resource references, ownership, and validation.

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 biological experiments so inference is valid by construction, covering Fisher's principles (randomization, replication, local control), the experimental-vs-observational unit distinction and pseudoreplication (Hurlbert 1984; Lazic 2018), randomization mechanics (complete, restricted, stratified, rerandomization, run-order), blocking layouts (randomized complete block, Latin square, incomplete block), factorial designs and interactions, and the split-plot/nested error strata hidden inside multi-batch genomics. Use when deciding the experimental unit and what counts as a replicate, planning randomization and run order, choosing a blocked/factorial/split-plot/nested layout, avoiding pseudoreplication in cell-culture or animal studies, or specifying the random-effects structure of the analysis model. For assigning samples to sequencing batches/lanes/plates and batch-effect correction see experimental-design/batch-design; for regulated clinical-trial randomization see clinical-biostatistics.

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 this skill when the user asks about CDL driver wellness, retention strategies, what causes driver turnover, common health issues (obesity, hypertension, sleep apnea, mental health), benefits packages, home-time policies, and how to build a culture that keeps drivers. Reference ATA Workforce Development + FMCSA Wellness Initiative.

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 this skill when the user asks how to investigate a CMV accident — what to do at the scene, the first 24 hours, evidence preservation, who to call, preventability determination, ELD data preservation, dash cam review, witness interviews, working with insurance + attorney, and how to build a learning culture from incidents. Reference 49 CFR 392.7 + insurance industry best practices.

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。

The science of learning through controlled experimentation. A/B testing isn't about picking winners—it's about building a culture of validated learning and reducing the cost of being wrong. This skill covers experiment design, statistical rigor, feature flagging, analysis, and building experimentation into product development. The best experimenters know that every test, positive or negative, teaches something valuable. Use when "a/b test, experiment, hypothesis, statistical significance, sample size, feature flag, variant, control, treatment, p-value, conversion rate, test winner, split test, experimentation, testing, statistics, feature-flags, hypothesis, growth, optimization, learning, validation" mentioned.

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。

Google SRE (Site Reliability Engineering) practices distilled from the SRE book series and real Google Brain/Meta/ByteDance production experience. Covers SLI/SLO/Error Budget design, incident response, capacity planning, toil automation, monitoring golden signals, and chaos engineering principles. USE WHEN: designing reliability targets, setting up monitoring/alerting, planning capacity, reducing operational load, building incident response runbooks, implementing chaos engineering, defining error budgets, or establishing reliability culture that balances velocity with stability.

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。

Architecture Decision Records (ADR) — a lightweight method for capturing important architectural decisions with context, trade-offs, and rationale. Based on Michael Nygard's ADR pattern and Google/Meta/ByteDance's design documentation culture. USE WHEN: deciding between technology choices, designing new architecture, changing existing architecture, documenting why a decision was made, onboarding new team members, or preserving design context for future maintainers. Triggered by terms like "tech choice", "architecture decision", "why did we choose", "design rationale", "ADR", or "architectural decision record".

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。