'environment' 검색 결과

Expert knowledge for Azure Deployment Environments development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when defining environment.yaml, configuring dev centers/projects, using ADE/Dev CLI, custom images, or CI/CD deployments, and other Azure Deployment Environments related development tasks. Not for Azure DevTest Labs (use azure-devtest-labs), Azure Dev Box (use azure-dev-box), Azure Managed Applications (use azure-managed-applications), Azure Resource Manager (use azure-resource-manager).

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Comprehensive market environment analysis and reporting tool. Analyzes global markets including US, European, Asian markets, forex, commodities, and economic indicators. Provides risk-on/risk-off assessment, sector analysis, and technical indicator interpretation. Triggers on keywords like market analysis, market environment, global markets, trading environment, market conditions, investment climate, market sentiment, forex analysis, stock market analysis, 相場環境, 市場分析, マーケット状況, 投資環境.

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Evaluates environmental liability exposure with remediation cost estimation, regulatory compliance requirements, and insurance coverage assessment. Use when analyzing environmental liabilities, estimating cleanup costs, or assessing environmental risk.

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Evaluates environmental compliance requirements with permitting risk, mitigation obligations, and ESG assessment for infrastructure investments. Use when assessing environmental risk, evaluating permitting timelines, or analyzing environmental compliance.

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Evaluates environmental liability exposure with remediation cost estimation, regulatory compliance requirements, and insurance coverage assessment. Use when analyzing environmental liabilities, estimating cleanup costs, or assessing environmental risk.

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Comprehensive market environment analysis and reporting tool. Analyzes global markets including US, European, Asian markets, forex, commodities, and economic indicators. Provides risk-on/risk-off assessment, sector analysis, and technical indicator interpretation. Triggers on keywords like market analysis, market environment, global markets, trading environment, market conditions, investment climate, market sentiment, forex analysis, stock market analysis, 相場環境, 市場分析, マーケット状況, 投資環境.

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Use when a task needs the judgment of an environmental scientist — scoping or reviewing a Phase I/Phase II environmental site assessment, interpreting soil or groundwater sampling results against regulatory action levels, drafting an environmental impact statement or NEPA scoping document, running a contaminant fate-and-transport screening calc, or assessing ecological risk to a receptor.

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Use when authoring, reviewing, or maintaining Salesforce DevOps operational documents — runbooks, environment matrices, deployment guides, and post-deploy validation checklists. Triggers: 'runbook', 'environment matrix', 'deployment guide', 'pre-deploy checklist', 'post-deploy validation', 'how do I document a deployment', 'rollback procedure template'. NOT for what actually belongs on the pre-deploy gate — use devops/pre-deployment-checklist. NOT for release calendar, versioning, or go/no-go governance — use devops/release-management. Also covers: the devops-process YAML artefact (environment ladder, change-request state machine, deploy contract, deploy order, RACI), the deployOptions the runbook must record (checkOnly, testLevel, rollbackOnError, ignoreWarnings, purgeOnDelete, singlePackage, runTests), destructiveChangesPre/Post ordering, and the change-request emergency path.

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Expert-level environmental engineering covering water treatment, wastewater treatment, air quality, solid waste, site remediation, and environmental regulations.

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Guide for defining RL environments — the Env protocol, EnvGroupBuilder, RLDataset, and custom environment creation. Use when the user asks about RL environments, reward functions, or how to define custom tasks for RL training.

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Configure Adobe OAuth credentials and API access across development, staging, and production environments with separate Developer Console projects, secret managers, and environment-specific scoping. Trigger with phrases like "adobe environments", "adobe staging", "adobe dev prod", "adobe environment setup", "adobe config by env".

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Lists and toggles a map's static environment objects via get_environment_objects / enable_environment_objects — hide or show buildings, vegetation, poles, fences, traffic signs and other baked assets by CityObjectLabel (and optional name filter). Use when the user asks to "hide/remove the buildings", "turn off the trees/vegetation", "show only the roads", "disable the fences", or "list the environment objects". Affects rendering and collision; resets on map reload.

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[BETA] Expert assistance for the Adobe I/O CLI plugin `@adobe/aio-cli-plugin-aem-rde` — the `aio aem rde` / `aio aem:rde` command tree used to deploy, inspect, log-tail, snapshot, and troubleshoot AEM Rapid Development Environments (RDEs). Activate ONLY when the user explicitly references RDE concepts: 'AEM RDE', 'Rapid Development Environment', `aio aem rde`, `aio aem:rde`, `aem-rde`, RDE snapshots, RDE deploy/install, `rde install`, `rde inspect`, `rde status`, `rde history`, `rde reset`, the `@adobe/aio-cli-plugin-aem-rde` package, or Cloud Manager program/environment configuration that is specifically for an RDE environment. Do NOT activate on generic AEMaaCS phrases like 'deploy to AEM Cloud', 'push my bundle', 'tail the publish log', 'cloud sandbox', or unqualified 'dispatcher-config / frontend / env-config deployments' — those belong to Cloud Manager pipelines, not RDE. This skill is in beta. Verify all outputs before applying them to production projects.

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[BETA] Expert assistance for the Adobe I/O CLI plugin `@adobe/aio-cli-plugin-aem-rde` — the `aio aem rde` / `aio aem:rde` command tree used to deploy, inspect, log-tail, snapshot, and troubleshoot AEM Rapid Development Environments (RDEs). Activate ONLY when the user explicitly references RDE concepts: 'AEM RDE', 'Rapid Development Environment', `aio aem rde`, `aio aem:rde`, `aem-rde`, RDE snapshots, RDE deploy/install, `rde install`, `rde inspect`, `rde status`, `rde history`, `rde reset`, the `@adobe/aio-cli-plugin-aem-rde` package, or Cloud Manager program/environment configuration that is specifically for an RDE environment. Do NOT activate on generic AEMaaCS phrases like 'deploy to AEM Cloud', 'push my bundle', 'tail the publish log', 'cloud sandbox', or unqualified 'dispatcher-config / frontend / env-config deployments' — those belong to Cloud Manager pipelines, not RDE. This skill is in beta. Verify all outputs before applying them to production projects.

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Standardizes development environment setup across machines by generating tool version configs (Node, Python, Ruby), package manager configs (pnpm, Volta, asdf, mise), environment variable templates, and setup scripts with onboarding documentation. Use when users need to "setup dev environment", "standardize tooling", "configure version managers", or "create onboarding scripts".

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원격 환경이나 Docker에서 설치, 연동, 실제 사용자 작업을 테스트합니다. 코드 신뢰도에 따른 환경 선택부터 장애 조사와 실행 후 정리까지 다룹니다.

• 깨끗한 환경에서 패키지 설치 테스트
• 실제 서비스와의 연동 검증
• macOS, Windows, WSL2 동작 확인

Train and evaluate RuView models using Wi-Fi measurements for pose estimation, retrieval, and adaptation to different environments. Follow laptop or GCloud GPU workflows, then package and publish trained models to Hugging Face.

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• Train pose estimation without cameras or labels
• Train pose estimation with paired camera and Wi-Fi data
• Adapt models to different rooms and environments

Set up OmniRoute and manage its command-line configuration. Inspect and validate tool settings, manage environment variables, check for updates, and control automatic startup using commands that support both interactive and unattended setup.

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• Setting up a provider and default model
• Inspecting and validating CLI tool settings
• Checking or changing environment variables

Create and improve GitHub Actions workflows that automate building, testing, and deployment. The skill covers testing across multiple environments, dependency caching, build output management, and security settings such as limited permissions and protected deployment environments.

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• Create a build, test, and deployment workflow
• Run automated tests on pull requests
• Configure tests across multiple environments

Build editable Three.js worlds from text or structured briefs, with terrain, distinct regions, landmarks, vegetation, and camera paths. Review the environment from multiple viewpoints and refine selected areas while preserving the overall layout and reproducible settings.

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• Build a continuous 3D landscape from a written brief
• Plan a camera fly-through across distinct regions
• Refine one area's terrain or vegetation placement

Set up lighting and shadows in Three.js scenes. Choose light types, create outdoor or studio lighting, use environment images to illuminate materials, and tune shadow settings to balance appearance with rendering performance.

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• Create sunlight and sky lighting outdoors
• Set up studio or three-point lighting
• Use HDR images for environment lighting

Configure images and videos as surface textures in Three.js, and use environment images for backgrounds and reflections. Adjust placement, color settings, repetition, and filtering, then manage texture memory through compression, reuse, and disposal.

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• Adjust image placement and tiling on 3D surfaces
• Use HDR images for backgrounds and reflections
• Apply video or Canvas content to a 3D surface

Understand how Codex Rules adds project instructions and file-specific rules to agent context. Check supported rule locations, when rules are applied, and environment settings for modes, sources, and text limits.

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• Checking supported locations for rule files
• Understanding when file-specific rules are applied
• Configuring rule modes and text limits

Build and evaluate single-agent reinforcement learning experiments, where an agent learns actions from rewards. Use Stable Baselines3 to choose algorithms, create environments, monitor training, and save models with the normalization data needed for consistent evaluation.

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• Set up a reproducible reinforcement learning experiment
• Create and validate a custom Gymnasium environment
• Monitor training and save checkpoints