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概要と使いどころ
Deploy a PortalJS portal to PortalJS Arc — Datopian-managed static hosting on Cloudflare. Builds a static export, uploads it, and returns a live SLUG.arc.portaljs.com URL. One command, one target. Use when a portal is ready to publish or redeploy to a live URL.
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。
Discovers available Azure OpenAI model capacity across regions and projects. Analyzes quota limits, compares availability, and recommends optimal deployment locations based on capacity requirements. USE FOR: find capacity, check quota, where can I deploy, capacity discovery, best region for capacity, multi-project capacity search, quota analysis, model availability, region comparison, check TPM availability. DO NOT USE FOR: actual deployment (hand off to preset or customize after discovery), quota increase requests (direct user to Azure Portal), listing existing 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。
Fine-tune models on Microsoft Foundry using SFT (supervised), DPO (preference), or RFT (reinforcement with graders). Covers dataset preparation, training job submission, deployment, and evaluation. USE FOR: fine-tune, SFT, DPO, RFT, training data, grader, distillation, fine-tuned model, training job, large file upload, calibrate grader, deploy fine-tuned model, evaluate fine-tuned model. DO NOT USE FOR: general model deployment without fine-tuning (use deploy-model), agent creation (use agents), prompt optimization without training (use prompt-optimizer).
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 AI models to embedded hardware using MathWorks tools (MATLAB, Simulink, Embedded Coder). Covers two workflow patterns: (1) MathWorks-native or imported models rebuilt as dlnetwork for lean hardware, (2) direct C/C++ code generation from PyTorch and LiteRT models. Both patterns support all targets (Cortex-M/A/R, x86, GPU). Trigger when: user wants to deploy AI to embedded targets; generate C/CUDA from neural networks; compress AI models for MCU; integrate AI in Simulink for system-level simulation; import PyTorch/ONNX/TensorFlow models for embedded deployment; optimize AI for resource-constrained hardware; or use loadPyTorchExportedProgram, loadLiteRTModel, importNetworkFromPyTorch, importNetworkFromONNX, importNetworkFromTensorFlow, importNetworkFromKeras, dlquantizer, exportNetworkToSimulink, or Embedded Coder with AI models.
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。
Configure zero-downtime deployment strategies including blue-green, canary, and rolling deployments. Implement traffic shifting, health checks, and rollback procedures. Use when implementing production deployment strategies or zero-downtime releases.
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。
Sets up a Power Platform Pipeline for automated Power Pages deployments. Power Platform Pipelines is Microsoft's native CI/CD tool built into the Power Platform — no external infrastructure required. Use when asked to: "set up ci/cd", "create pipeline", "setup pipeline", "set up power platform pipelines", "create power pipelines", "automate deployments", "set up automated deployment", "create deployment pipeline", "use power pipelines". Also handles: "set up github actions" or "set up azure devops pipeline" (shows coming-soon guidance for those platforms).
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 dispatcher PCF for a third-party `.ppmplugin` control to a Power Platform environment via `pac pcf push`. The dispatcher PCF is the Studio-side control that dispatches the composite key `<name>/<receiver>` over the wrap shell's `SendMessagePlugin` bridge to the control's native module. Verifies deploy prereqs (Node.js 20+ with npm, .NET SDK, and active `pac auth`), then three confirmation gates — publisher prefix (2–8 chars; defaults to `pamext` or the last-used value from `.extension-state.md`), version bump (patch / minor / major / no-bump), target environment URL (from `pac org who`). Builds the PCF if needed, then pushes. Decoupled from /generate-pcf-companion so the engineer can scaffold and customize locally, then deploy when ready. Updates `.extension-state.md` with deployment history (timestamp, env URL, version, prefix used).
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 a Power Apps code app to Power Platform. Use when deploying changes, redeploying an existing app, or pushing updates.
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 QA testing on a page, feature, or full site at one of three depth tiers (smoke, standard, full). Use this skill whenever the user asks to QA a page or site, run a smoke test after a deploy, verify a page before launch, or run a regression sweep. Triggers on QA, QA sweep, smoke test, regression test, post-deploy check, pre-launch check, verify the deploy, test this page, does the page render, broken link, 404, image not loading. Also triggers proactively after a deploy or a new page launch where verification matters. Covers accessibility, performance, and SEO at the surface-signal level only: deep audits belong to `accessibility-audit`, `performance-optimization`, and `seo-technical`, and code-level debugging to `code-review-web`.
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。
Merge PR, wait for deploy, verify production health. 10-step workflow: pre-flight, CI check, readiness gate, merge (respects merge queue), detect platform, wait for deploy, canary verification, revert option, deploy report. Auto-detects Fly, Render, Vercel, Netlify, Heroku, GitHub Actions.
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 applications to AWS. Triggers on phrases like: deploy to AWS, host on AWS, run this on AWS, AWS architecture, estimate AWS cost, generate infrastructure. Analyzes any codebase and deploys to optimal AWS 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。
Deploy or publish Python and R content to a Posit Connect server using rsconnect-python or the R rsconnect package. Handles interactive apps and dashboards, web APIs, rendered documents, and prepared bundles/manifests. Use whenever the user asks to deploy, publish, or redeploy content to Posit Connect, or mentions rsconnect. Consult this skill instead of guessing flags or commands.
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。
SST v4 (Ion) expert for managing AWS resources as code with the Pulumi-backed framework. Use when writing or editing sst.config.ts, building infra/ modules (sst.aws.Function/Bucket/Dynamo/Cron/Service/Router, sst.Secret, sst.Linkable, raw aws.* Pulumi resources), wiring resource links, scoping IAM, or running sst deploy/dev/diff/remove. Essential when the user mentions SST, sst.config.ts, $config, $transform, $interpolate, sst.aws.*, sst.Secret, Pulumi/Ion, "sst deploy", a failed SST deploy (ConflictException on a resource-type change, "Identifier '__filename' has already been declared", MalformedPolicyDocument on an Output<T>), or wants to scaffold/troubleshoot AWS infrastructure with SST. Also use when a request to "deploy my AWS stack" or "add a Lambda/bucket/table" is made in a repo that already contains an sst.config.ts (using $config) or an sst dependency. Do NOT use when the task is primarily AWS CDK, Terraform, raw CloudFormation, or SAM with no SST present — those have their own tooling.
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。
AWS Bedrock AgentCore comprehensive expert for deploying and managing AI agents at scale. Use when working with any AgentCore service including Gateway, Runtime, Memory, Identity, Code Interpreter, Browser, Observability, Agent Registry, or Evaluations. Covers agent deployment, MCP tool integration, credential management, agent discovery, governance workflows, and automated quality assessment. Essential when user mentions AgentCore, agent runtime, agent registry, agent evaluation, MCP gateway, deploy agent, register MCP server, discover agents, evaluate agent quality, agent credentials, or wants to build, deploy, catalog, or monitor AI agents on AWS.
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 AI models to embedded hardware using MathWorks tools (MATLAB, Simulink, Embedded Coder). Covers two workflow patterns: (1) MathWorks-native or 3P-imported models rebuilt as dlnetwork for lean hardware (Cortex-M, DSP), (2) direct C/C++ code generation from PyTorch and LiteRT models for high-performance hardware (Cortex-A, x86, GPU). Trigger when: user wants to deploy AI to embedded targets; generate C/CUDA from neural networks; compress AI models for MCU/DSP; integrate AI in Simulink for system-level simulation; import PyTorch/ONNX/TensorFlow models for embedded deployment; optimize AI for resource-constrained hardware; or use loadPyTorchExportedProgram, importNetworkFromPyTorch, dlquantizer, exportNetworkToSimulink, or Embedded Coder with AI models.
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。
Dokku is an open-source, single-server platform-as-a-service: it builds an app from a git push (buildpacks or a Dockerfile), runs it in Docker containers behind nginx, and manages domains, TLS, config and datastores through one CLI. Use when a user asks to "self-host a Heroku alternative", "set up git push deploys on a VPS", "deploy my app with Dokku", "add Postgres or Redis to a Dokku app", "get Let's Encrypt on Dokku", or "fix ports for a Dockerfile app on Dokku". Covers installation from the apt repository, deploys, environment variables, plugins, zero-downtime checks, storage and the k3s scheduler.
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 expert in Deno Deploy, the globally distributed serverless platform by Deno. You help developers deploy TypeScript/JavaScript applications to 35+ edge locations with zero cold starts, built-in KV storage, BroadcastChannel for real-time, cron scheduling, and npm compatibility — running code within milliseconds of users worldwide without managing 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。
World-class Kubernetes operations - deployments, debugging, Helm charts, and the battle scars from managing clusters that serve millions of requestsUse when "kubernetes, k8s, kubectl, helm, pod, deployment, service, ingress, configmap, secret, statefulset, daemonset, hpa, pvc, crashloopbackoff, imagepullbackoff, oomkilled, liveness probe, readiness probe, kubernetes, k8s, containers, docker, helm, deployment, devops, cloud-native" 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。
CI/CD is the backbone of modern software delivery. Continuous Integration catches bugs early. Continuous Deployment gets features to users fast. But poorly designed pipelines can be slow, flaky, and a source of constant frustration. This skill covers GitHub Actions (the most popular), GitLab CI, and general pipeline design principles. The focus is on pipelines that are fast, reliable, and maintainable. 2025 reality: Your pipeline is infrastructure code. It deserves the same care as your application. A flaky test in CI is worse than no test - it teaches developers to ignore failures. Use when "github actions, gitlab ci, ci cd, pipeline, workflow, deploy automation, continuous integration, continuous deployment, build pipeline, yaml workflow, ci-cd, github-actions, gitlab-ci, devops, automation, deployment, pipelines" 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。
Performs deployment with pre-flight checks, atomic replacement, post-deploy verification, provenance (proving the environment serves the exact commit you pushed), and automatic rollback. Use before any deployment to staging, preview, or 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。
arkcli +deploy：普通创建推理接入点（Endpoint）的统一首选入口。用户说『创建/新建/create 一个 endpoint/接入点』或『部署/上线/deploy 某模型』时优先走这里；**但脚本化 / CI / 无护栏 / 原始 raw CRUD 创建是唯一例外，必须改走 `arkcli-infer-endpoint`，不能由本 skill 截获**。只有不命中该例外的普通创建，才在模型尚未选择、只给品牌/家族名，或当前轮只查询候选时进入本 skill 完成模型澄清；候选要用实时 search 返回的 `name` 与 `primary_version` 组合成可直接传给 `--model` 的完整 ID。对**已有** Endpoint 做获取/列表/启停/更新等全生命周期管理也走 arkcli-infer-endpoint；本 skill 只负责带产品护栏的一键创建。创建成功后会自动把多语言调用示例渲染到 ./ark-examples/<ep-id>/。反触发：TTS/ASR/语音模型不能 +deploy，只能转 models search 说明广场可搜但 arkcli 不支持 Endpoint 创建。
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。
Skill Base server deployment guide. Covers starting the Skill Base server (npx skill-base), Docker configuration, port mapping, SQLite database backup, and platform capabilities (collections, tags, GitHub import). For deploying and operating the Skill Base platform itself only.
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 a backend or worker service needs a cloud deploy - container-first GitHub Actions deploys to Google Cloud Run (WIF) or AWS ECS/App Runner (OIDC), with migrate-before-deploy and a health-check gate
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 Coolify operator for self-hosted deployment platform. Use when the user mentions 'coolify', 'deploy on coolify', 'list/restart/redeploy applications', 'view coolify logs', 'coolify API/CLI', 'manage coolify servers/databases/apps', or 'coolify context'. Automates deployments and management via REST API or official CLI.
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。