「deploy」の検索結果

Deploy a production self-hosted n8n end-to-end to a fresh Linux VM over SSH, using Docker Compose behind a Caddy reverse proxy with automatic HTTPS. Use whenever the user wants to self-host, install, set up, provision, or deploy n8n on their own server/VPS/box (Hetzner, DigitalOcean, AWS EC2, bare metal, etc.) — in either single/regular mode or queue mode with workers — or to update, back up, restore, or harden such an instance. This is for SELF-HOSTED n8n (Docker), not n8n Cloud and not building workflows. The skill makes the agent ask single-vs-queue first, collect the domain/SSH/timezone inputs, generate fresh secrets on the box, and bring the stack up with TLS. Trigger on "deploy n8n", "self-host n8n", "install n8n on my server", "n8n docker compose", "n8n queue mode / workers / scaling", "n8n reverse proxy / SSL", "back up / update my n8n", or "we don't want to give every user the OAuth client secret" / "enable the Sign in with Google button" (credential overwrites).

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。

Designs, builds, and deploys AI agents or multi-agent systems on Google Cloud. Provides an interactive workflow to gather requirements, recommend a tailored architecture, and generate deployment instructions. Use when designing or implementing agentic systems on Google Cloud. Don't use for general Google Cloud solution architecture (use google-cloud-solution-architecture instead) or for narrow tasks targeting a single product without agent 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。

Deploys and optimizes AI/ML inference workloads on GKE, using GPUs, TPUs, and model servers. Use when deploying GKE inference servers, configuring GKE GPU resources for inference, or deploying LLMs on GKE. Don't use for migrating existing AI workloads to GKE (use google-cloud-solution-guided-gke-ai-migration), GKE RAG with Cloud SQL/AlloyDB (use google-cloud-solution-rag-enterprise-search-gke-sqldb), or batch/HPC (use gke-batch-hpc).

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。

Turn a Wren project's context layer into a shareable, browser-side GenBI web app and deploy it to the user's Vercel or Cloudflare account. Orchestrates the full flow: `wren genbi build` returns a project-hydrated build instruction, the agent authors the app from scratch into apps/<name>/, then register → verify → deploy produce a shareable URL. Use this skill whenever the user wants to: build a dashboard from their Wren project, make a shareable analytics app, deploy their context layer as a web app, host a GenBI app on Vercel or Cloudflare Pages, or asks for a 'genbi app'.

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 the QM package from an organization-owned deployment repository to Fly.io or AWS, onboard an administrator, configure connectors, and optionally activate Slack.

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, manage, inspect, and troubleshoot Vercel projects from the command line. Use for Vercel deployments, Vercel Toolbar comments, build failures, projects and teams, environment variables, domains and DNS, logs, metrics, Speed Insights, Core Web Vitals, request traces, usage, activity, alerts, firewall rules, cache, cron jobs, deploy hooks, Edge Config, feature flags (`vercel flags`), integrations, connectors, Blob storage, Container Registry (VCR), microfrontends, rolling releases, custom environments, Sandbox, agent/MCP setup, preview access, local development, or `vercel api` fallback.

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。

Classify + announce the target Convex deployment before any deployment-affecting command; fresh explicit consent for prod actions; session read-only mode.

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。

Run and verify ClawHub production deploys and stable ClawHub CLI npm releases. Use when deploying backend or frontend changes to clawhub.ai, dispatching the Deploy workflow, publishing a stable CLI tag, checking release prerequisites, or proving the exact production SHA and workflow outcome.

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, run, deploy, and evaluate OpenAI Agents SDK apps from Codex. Use when the user asks to create or adapt an Agents SDK app, build from a prompt or Codex thread, prepare a runnable agent prototype, add a focused eval harness, or deploy locally through the Agents SDK Deployment Manager.

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 asked to install, deploy, run, validate, troubleshoot, or stop NVIDIA AI-Q Blueprint 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。

Deploy a production self-hosted n8n end-to-end to a fresh Linux VM over SSH, using Docker Compose behind a Caddy reverse proxy with automatic HTTPS. Use whenever the user wants to self-host, install, provision, or deploy n8n on their own server/VPS (Hetzner, DigitalOcean, AWS EC2, bare metal) — single/regular mode or queue mode with workers — or to update, back up, restore, or harden such an instance, or make Python Code nodes run on it (task runners). For SELF-HOSTED n8n (Docker), not n8n Cloud and not building workflows. The skill makes the agent ask single-vs-queue first, collect domain/SSH/timezone inputs, generate fresh secrets on the box, and bring the stack up with TLS. Trigger on "deploy n8n", "self-host n8n", "n8n docker compose", "n8n queue mode / workers", "n8n reverse proxy / SSL", "back up / update my n8n", "Python runner unavailable" / "n8nio/runners sidecar", or "we don't want to give every user the OAuth client secret" / "enable Sign in with Google" (credential overwrites).

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。

This skill should be used when the user wants to "publish an agent", "publish my ADK agent", "register an agent with Gemini", "publish to Gemini" (formerly Gemini Enterprise), or needs guidance on the agents-cli publish gemini command. Also use when the user wants to "manage agents in Agent Registry", "list/update/delete registered agents", or "register an MCP server". Covers ADK vs A2A registration modes, programmatic and interactive usage, flag reference, auto-detection from deployment metadata, Agent Registry fleet management, and troubleshooting. Part of the agents-cli skills suite. Do NOT use for deployment (use google-agents-cli-deploy).

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。

This skill should be used when the user wants to "set up tracing", "monitor my agent", "configure logging", "add observability", "debug production traffic", or needs guidance on monitoring deployed agents, including ADK (Agent Development Kit) agents. Covers Cloud Trace, prompt-response logging, BigQuery Agent Analytics, third-party integrations (AgentOps, Phoenix, MLflow, etc.), and troubleshooting. Part of the agents-cli skills suite. Do NOT use for deployment setup (use google-agents-cli-deploy) or API code patterns (use google-agents-cli-adk-code).

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 a significant code change (backend, kernel, optimizer, data path) to the live hero run: relaunch it under a new run id from a permanent checkpoint, compare against the old run over a trial window, and roll back if the gate fails; use only when the user asks to deploy a change to the hero.

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 — deploy, configure, troubleshoot, and manage. Handles any RAG action: deploy, install, start, enable, disable, toggle, change, configure, troubleshoot, debug, fix, shutdown, stop, or tear down any RAG feature or service (Agentic RAG, VLM, guardrails, query rewriting, models, search, ingestion, observability, summarization, reasoning, and more).

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。

Audit, prepare, and deploy PAIDF Orchestration on a Kubernetes GPU cluster - single-GPU H100/L40S hosts, managed Kubernetes, kubeadm, and similar. Select for requests to set up, install, deploy, configure, or check a PAIDF Orchestration environment; run a workflow on a new or unverified GPU host; connect via kubeconfig; validate GPU compute; deploy the Airflow controller; or choose external versus in-cluster model services. A plain SSH host is not a supported backend.

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。

Select, validate, patch, and deploy existing NVIDIA Dynamo Kubernetes recipes. Use for model/backend/GPU/deployment-mode recipe bring-up; use router-starter for router-only mode work and troubleshoot for broken 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 this skill when the user is hands-on deploying an in-bundle DOCA service container (Argus, DMS, Firefly, or UROM service) on a BlueField — kubelet standalone watching a static-pod manifests directory, YAML pod-spec drop, kubelet status / ENTRYPOINT logs / per-service liveness, smoke-before-bulk, and the layered error taxonomy (pod-spec, scheduling, image pull, runtime, mount, network, version, host). Trigger even when the user does not say "container deployment" — typical implicit phrasings include "how do I run my built service on the BlueField?", "where do I drop the pod-spec YAML?", "pod stuck in Pending / ImagePullBackOff / CrashLoopBackOff", "container Running but service isn't ready", "pod restart-loops after edit", or "DMS and Firefly together". Refuse and route elsewhere for per-service config schemas, DOCA install, library-API questions, external NVIDIA services (BlueMan, HBN, SNAP, Virtio-net), or full Kubernetes-cluster ops — those belong to other skills.

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。

Create, build, deploy, and localize declarative agents for M365 Copilot and Teams. USE THIS SKILL for ANY task involving a declarative agent — including localization, scaffolding, editing manifests, adding capabilities, and deploying. Localization requires tokenized manifests and language files that only this skill knows how to produce. Triggers: "create agent", "create a declarative agent", "new declarative agent", "scaffold an agent", "new agent project", "add a capability", "add a plugin", "configure my agent", "deploy my agent", "fix my agent manifest", "edit my agent", "localize my agent", "add localization", "translate my agent", "multi-language agent", "add an API plugin", "add an MCP plugin", "add OAuth to my plugin", "review instructions", "improve instructions", "fix my instructions"

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 Python (Flask/Django/FastAPI) code to Azure App Service Linux. WHEN: "Flask App Service", "Django App Service", "FastAPI App Service", "deploy Python to App Service". DO NOT USE FOR: Container Apps, Functions, non-Python, Terraform/Bicep/IaC, full infra — use azure-prepare.

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。

Builds, tests, and deploys Microsoft 365 apps and agents for Teams and Copilot. Includes sub-skills for project creation, local testing, cloud deployment, troubleshooting, and Slack-to-Teams migration. USE FOR: Teams agent, bot, tab, message extension, Declarative Agents, Custom Engine Agents, local testing, Agents Playground, Azure resource provision, remote deployment, Slack to Teams migration, cross-platform bot development, Block Kit to Adaptive Cards conversion. DO NOT USE FOR: general web development, non-bot/non-Teams projects.

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。

Deploys a Regional REST API with a custom domain name, a Lambda backend function, and a request-based Lambda authorizer using AWS CLI. Covers ACM certificate provisioning, API Gateway REST API creation, Lambda function deployment, request authorizer setup, custom domain configuration, base path mapping, and Route 53 DNS record creation. Trigger keywords: custom domain, REST API, Lambda, Route 53, API Gateway, regional endpoint, request authorizer, base path mapping.

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。

Tests Amazon MWAA workflow execution end-to-end: trigger a run and monitor it to completion for Provisioned (Python DAG, via Airflow REST API) and Serverless (YAML workflow, via StartWorkflowRun). Verifies the artifact is deployed and parse-ready, triggers with confirmation, polls to terminal state, and on failure delegates diagnosis to debugging-mwaa-workflow and artifact/redeploy fixes to authoring-mwaa-workflow, then retests up to a capped number of attempts. Triggers on: test my DAG, test my workflow, run my DAG, trigger a test run, does my DAG work, smoke-test the pipeline, verify my workflow runs, execute my DAG to check it. Not applicable to writing or deploying a new workflow (handled by authoring-mwaa-workflow), or for diagnosing why a run failed or root-causing an error (handled by debugging-mwaa-workflow).

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。

Builds and deploys containerized workloads on Elastic Kubernetes Service (EKS), Elastic Container Service (ECS), Fargate, and ECR (Elastic Container Registry). Covers general EKS knowledge, Karpenter, AWS Load Balancer Controller and leveraging various open source Kubernetes projects with EKS. Covers general ECS knowledge, task definitions, Fargate services, ECS Exec, ECS Express Mode and ECS Managed Instances. Covers general Elastic Beanstalk knowledge, Elastic Beanstalk configuration and platforms supported by Elastic Beanstalk. Covers general ECR knowledge, ECR repository setup and lifecycle policies. Includes recommending, enabling, and reading Amazon ECS Action Logs to troubleshoot control-plane failures (deployment rollback/circuit-breaker, task placement, scaling, task replacement). Applies when deploying, debugging, or optimizing containers on AWS. Should be used instead of relying on internal knowledge for these services.

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。