2.1천건 · 관련도순
Use when working with Balena — balena IoT fleet management including applications, devices, releases, services, environment variables, and device diagnostics. Covers fleet deployment status, device connectivity, container health, update progress, and resource utilization across edge devices.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when working with Atlantis — atlantis pull request automation for Terraform. Covers plan/apply via PR comments, workspace management, project configuration, server status, lock management, and repository configuration. Use when managing Atlantis deployments, debugging PR plan/apply issues, or configuring project workflows.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when working with Appwrite — appwrite backend platform management covering project inventory, database collections, user authentication stats, storage bucket usage, function deployments, webhook configuration, and platform health. Use for comprehensive Appwrite project assessment and resource monitoring.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when working with Appsmith — appsmith low-code platform management covering application inventory, workspace organization, datasource health, page and widget analysis, and user access auditing. Use when reviewing internal tool configurations, investigating datasource connectivity, monitoring application deployments, or auditing workspace permissions.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when working with Aws Cdk Deep — aWS CDK application and stack management. Covers stack synthesis, CloudFormation template inspection, diff analysis, deployment orchestration, context values, asset management, and construct library usage. Use when managing CDK apps, debugging synthesis errors, reviewing stack diffs, or analyzing deployed CloudFormation resources.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when working with Apigee — apigee API platform management - API proxy deployment, environment configuration, analytics and traffic analysis, developer and app management. Use when managing Apigee-based API infrastructure, deploying proxies, analyzing API traffic patterns, or managing developer portal resources.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when working with Aws Cdk — aWS CDK infrastructure-as-code management. Covers synth, diff, deploy, bootstrap, context values, construct inspection, and stack dependencies. Use when managing CDK applications, reviewing synthesized templates, comparing deployments, or debugging construct issues.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when working with Azure App Service — azure App Service web app health monitoring, deployment slot management, scaling configuration, and application settings analysis via Azure CLI.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when working with Gcp Cloud Deploy — google Cloud Deploy delivery pipeline management, release tracking, rollout status monitoring, approval workflows, and deployment diagnostics via gcloud CLI.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when working with Azure Functions — azure Functions app analysis, execution metrics, consumption plan monitoring, scaling configuration, and deployment management via Azure CLI.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when working with Aws Lambda — aWS Lambda function analysis, invocation metrics, cold start analysis, layer management, and concurrency tracking. Covers function configuration review, error rate analysis, duration percentiles, provisioned concurrency utilization, and deployment package optimization.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when working with Aws Ecs — aWS ECS cluster health, service status, task analysis, capacity provider management, and deployment tracking. Covers service stability, task failure investigation, container insights, resource utilization, and scaling analysis.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Build production Apache Airflow DAGs with best practices for operators, sensors, testing, and deployment. Use when creating data pipelines, orchestrating workflows, or scheduling batch jobs.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Create and manage Netlify deploys — Git continuous deployment, CLI manual/anonymous deploys, Deploy to Netlify buttons, drag-and-drop, and per-context netlify.toml build settings. Use when linking a repo, deploying from the CLI, setting up Deploy Previews or branch deploys, configuring deploy contexts, adding skew protection, fixing a failed or secrets-flagged deploy, or wiring build hooks and Deploy to Netlify buttons.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
NVIDIA NeMo Retriever deployment and usage guidance for local retrieval services, corpus ingestion, and grounded question-answering workflows.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Set up an isolated Python environment for SageMaker / AWS work, with the right Python version and current boto3. Use this skill whenever Python code will be executed for a SageMaker deployment, training job, or any AWS automation — including when about to run `pip install`, when about to invoke `boto3`, when creating or activating a virtualenv, or when the user asks to "set up the environment". Never use system Python and never `pip install` into it. Always isolate. This skill prevents the most common failure modes: wrong Python version, dependency conflicts, and stale SDKs.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Ensure a usable SageMaker execution role exists before deploying or training. Use this skill whenever about to create a SageMaker endpoint, model, training job, or any resource that requires an execution role. Use it especially when the user has not provided a role ARN explicitly, when scripts are about to call `iam:CreateRole`, or when an AccessDenied error mentions an IAM action. Never blindly call `iam:CreateRole` — always check for existing roles first. This skill prevents the most common SageMaker deployment failure: trying to create IAM resources from an SSO principal that has no IAM write permissions.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Train or fine-tune language and vision models using TRL (Transformer Reinforcement Learning) or Unsloth with Hugging Face Jobs infrastructure. Covers SFT, DPO, GRPO and reward modeling training methods, plus GGUF conversion for local deployment. Includes guidance on the TRL Jobs package, UV scripts with PEP 723 format, dataset preparation and validation, hardware selection, cost estimation, Trackio monitoring, Hub authentication, model selection/leaderboards and model persistence. Use for tasks involving cloud GPU training, GGUF conversion, or when users mention training on Hugging Face Jobs without local GPU setup.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Azure deployment for web apps — Static Web Apps, App Service, Blob Storage, Bicep/ARM, GitHub Actions CI/CD. Use when deploying Next.js/Vite to Azure or configuring Azure resources for full-stack apps.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Expert en IA embarquée (TensorFlow Lite, ONNX, quantization, deployment on edge devices)
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Computer Vision v2 IA — Expert en vision par ordinateur (détection d'objets, segmentation, OCR, vidéo, déploiement edge)
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Expert en Coolify avancé (self-hosting, Docker, déploiements, SSL, backups, gestion des ressources, multi-serveurs)
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Coolify Specialist IA — Expert en Coolify (self-hosting, Docker, SSL, backups, one-click apps, migration)
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Expert en pipelines CI/CD (GitHub Actions, GitLab CI, Jenkins, ArgoCD, progressive delivery)
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.