986 件 ・ 関連度順
概要と使いどころ
Use when working with Dagster — dagster data orchestration platform management. Covers asset management, pipeline runs, sensor and schedule status, IO manager configuration, partition management, and resource health. Use when checking asset materialization status, investigating run failures, managing schedules/sensors, or analyzing Dagster deployments.
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 when working with Cloudformation — aWS CloudFormation stack management. Covers stack lifecycle, change sets, drift detection, template validation, nested stacks, stack sets, and event troubleshooting. Use when managing CloudFormation stacks, investigating deployment failures, detecting drift, or validating 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。
Use when working with Bitbucket Deep — bitbucket deep platform management covering repository inventory, Pipelines CI/CD monitoring, pull request analysis, branch permission auditing, deployment environment tracking, and workspace member management. Use when auditing repository configurations, investigating pipeline failures, reviewing branch restrictions, or managing workspace access.
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 when working with Cloudformation Deep — aWS CloudFormation deep stack management. Covers stack operations, change sets, drift detection, nested stacks, stack sets, resource imports, template analysis, and rollback troubleshooting. Use when managing complex CloudFormation deployments, debugging stack failures, detecting drift, operating stack sets, or analyzing template dependencies.
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 when working with Baseten — baseten ML deployment platform management covering model inventory, deployment status, autoscaling configuration, inference call history, GPU allocation, environment management, and performance metrics. Use for comprehensive Baseten workspace assessment and ML serving optimization.
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 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.
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 when working with Aws Sam — aWS Serverless Application Model (SAM) management. Covers template validation, local testing, build and packaging, deployment orchestration, stack inspection, log tailing, and API Gateway debugging. Use when building serverless applications with SAM, testing locally, deploying Lambda functions, or troubleshooting API Gateway endpoints.
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 when working with Banana Dev — banana.dev ML inference platform management covering model inventory, deployment status, API call history, GPU allocation, scaling configuration, build logs, and latency metrics. Use for comprehensive Banana.dev model deployment assessment and inference performance analysis.
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 when working with Anyscale — anyscale platform management covering Ray clusters, services, model deployments, jobs, and usage analytics. Use when monitoring deployed services, analyzing cluster utilization, reviewing job status, managing model endpoints, or troubleshooting Anyscale deployments.
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 for enterprise agent deployment tasks. Candidate synthesized from trend radar; awaiting trial promotion.
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 review of AI edge devices and lightweight CNN and LLM deployment — 21. Jan. 2025 · However, deploy GAP: OpenHands has a modular SDK design — our too
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 production Apache Airflow DAGs with best practices for operators, sensors, testing, and deployment. Use when creating data pipelines, orchestrating workflows, or scheduling batch jobs.
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。
NVIDIA RAG Blueprint deployment, configuration, troubleshooting, and shutdown guidance for Docker, Helm, and library-based RAG stacks.
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。
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.
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。
Kubernetes deployment workflow for container orchestration, Helm charts, service mesh, and production-ready K8s configurations.
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。
Docker, Kubernetes, container orchestration, and cloud-native deployment for data applications
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。
High-level SDK for Azure AI Foundry projects with agents, connections, deployments, and evaluations.
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。
Master local LLM inference, model selection, VRAM optimization, and local deployment using Ollama, llama.cpp, vLLM, and LM Studio. Expert in quantization formats (GGUF, EXL2) and local AI privacy.
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。
Large Language Model development, training, fine-tuning, and deployment 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。
Master local LLM inference, model selection, VRAM optimization, and local deployment using Ollama, llama.cpp, vLLM, and LM Studio. Expert in quantization formats (GGUF, EXL2) and local AI privacy.
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。
You are an advanced Docker containerization expert with comprehensive, practical knowledge of container optimization, security hardening, multi-stage builds, orchestration patterns, and production deployment strategies based on current 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。
Expert knowledge for deploying to Vercel with Next.js Use when: vercel, deploy, deployment, hosting, production.
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。
High-level SDK for Azure AI Foundry projects with agents, connections, deployments, and evaluations.
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。
Deploy containerized frontend + backend applications to Azure Container Apps with remote builds, managed identity, and idempotent infrastructure.
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。