3,032 件 ・ 関連度順
概要と使いどころ
Build and deploy applications when the user selects or invokes the Prisma plugin, including requests like "Build a simple Todo app and deploy it" that do not name a stack. Use Prisma Composer for new apps and Prisma Compute for deployment. Also use for explicit Composer/Compute requests, deployment diagnostics, and recovering Composer deployments. Does not cover unrelated ORM or database administration.
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 comprehensive preflight validation of Bicep deployments to Azure, including template syntax validation, what-if analysis, and permission checks. Use this skill before any deployment to Azure to preview changes, identify potential issues, and ensure the deployment will succeed. Activate when users mention deploying to Azure, validating Bicep files, checking deployment permissions, previewing infrastructure changes, running what-if, or preparing for azd provision.
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 and websites to Vercel. Use when the user requests deployment actions like "deploy my app", "deploy and give me the link", "push this live", or "create a preview deployment".
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。
Blue-green and canary deployment orchestrator with traffic shifting and automated rollback. Activate on: blue-green deployment, canary release, rolling deployment, traffic shifting, rollback automation, progressive delivery, Argo Rollouts, Flagger. NOT for: K8s manifest generation (use kubernetes-manifest-generator), CI/CD pipeline setup (use github-actions-pipeline-builder), monitoring (use monitoring-stack-deployer).
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。
医療アプリの公開前に、診療判断の支援ロジック、患者情報の漏えい、データ整合性などを検証するスキル。重大なテストの失敗でデプロイを止める仕組みを示します。
公開後のURLを点検し、ページやAPIの応答、配信ファイル、継続通信、表示速度の悪化を確認します。単発チェック、一定期間の監視、本番と検証環境の比較に対応します。
Phase 4 of building a Claude Managed Agent — make it run without you. Turn a graded agent into a recurring scheduled deployment (POSIX-cron), an event-driven curl trigger, or confirmed on-demand use, then finalize the versioned roadmap. Use when the user says "run it every morning", "put it on a schedule", "nightly", "weekly", "automate this", "make it recurring", or when the orchestrator routes phase=run-without-you. deployment_builder.py builds the POST /v1/deployments payload (initial_events must include user.message; optionally nests a user.define_outcome so each firing self-grades); cron_validator.py validates the 5-field cron + IANA timezone and prints the wall-clock DST note; next_directions_writer.py writes NEXT-DIRECTIONS.md. No tool makes API calls — the deployment is created via BYOK curl. Distinct from grade-iterate (the in-session loop) and wrap-up (closeout).
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。
Pick the right serving container for a SageMaker model deployment and find its current image URI. Use this skill whenever about to deploy a model to a SageMaker endpoint and an image URI needs to be chosen — including when the user says "deploy this LLM", "host this HuggingFace model", "serve this fine-tuned model", "deploy this embedding model", "host a reranker", "serve a sentence-transformers model", or when about to hardcode any container URI in deployment code. HuggingFace-curated Deep Learning Containers are ALWAYS preferred: HuggingFace vLLM (LLMs and generative rerankers), HuggingFace vLLM-Omni (multimodal), TEI (embeddings/cross-encoder rerankers), HF Inference Toolkit (other transformers). Generic images (AWS vLLM, DJL-LMI, SGLang) are used only when no HuggingFace image is compatible — never merely because they carry a newer version. Never hardcode a container URI from memory and never default to TGI. Prevents stale-image failures and wrong-region URIs.
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 web projects to Netlify using the Netlify CLI (`npx netlify`). Use when the user asks to deploy, host, publish, or link a site/repo on Netlify, including preview and production deploys. Do NOT use for deploying to Vercel, Cloudflare, or Render (use their respective 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。
Deploy status across all projects. Shows ECS service versions, Vercel deployments, recent deploys, pending deploys, and CI/CD pipeline state.
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。
[omh] App idea headed for launch: shape an app idea into decisions, delivery handoff, verification, release, and monitoring status. Use when the user says: idea-to-deploy, idea to deploy, from idea to deploy, plan to deploy, idea to launch, ship this idea, ship this feature, launch this feature.
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 static pages to nexu.space. Use when user says deploy, publish, ship, or go live with a static site/page. Uploads files from workspace to <project-slug>.nexu.space via Wrangler + Cloudflare Pages. Supports first deploy and redeploy.
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。
Intelligently deploys Azure OpenAI models to optimal regions by analyzing capacity across all available regions. Automatically checks current region first and shows alternatives if needed. USE FOR: quick deployment, optimal region, best region, automatic region selection, fast setup, multi-region capacity check, high availability deployment, deploy to best location. DO NOT USE FOR: custom SKU selection (use customize), specific version selection (use customize), custom capacity configuration (use customize), PTU deployments (use customize).
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。
Selects, deploys, and customizes AI models on Amazon SageMaker. Training or Processing jobs, fine-tuning (SFT/DPO/RLVR/RLAIF), model selection, dataset preparation, evaluation, SageMaker or Bedrock deployment, inference optimization and endpoint diagnostics. Covers the full lifecycle from planning through production. Use when fine-tuning models on SageMaker, choosing which base model to customize, fine-tune, or deploy from SageMaker JumpStart or Hub, SageMakerPublicHub, or the SageMaker public model catalog, transforming or validating training data, evaluating model quality, deploying or optimizing endpoints, configuring IAM/S3 for training, or managing SageMaker Managed MLflow. Use for endpoint health, failures, latency, logs, metrics, errors. Covers Serverless Model Customization, Nova and OSS deployment paths, and PySDK v3. NOT for Ground Truth labeling, Feature Store, or general-purpose AWS 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 Apify Actors and integrate scraping into external applications. Use when deploying an Actor to the platform, integrating Actor results into a web app (Next.js, Express), wiring webhooks, or scheduling scraping pipelines. Trigger with "deploy apify actor", "apify Vercel integration", "apify production deploy", "integrate apify results", "apify API endpoint".
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 an Alchemy-backed service through environment-scoped credentials, chain assertions, canaries, and rollback gates. Use when promoting an integration to hosted environments. Trigger with "deploy Alchemy integration", "Alchemy production deploy", or "promote an Alchemy service".
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 and orchestrate Vertex AI ADK agents using A2A protocol. Manages AgentCard discovery, task submission, Code Execution Sandbox, and Memory Bank. Use when asked to "deploy ADK agent" or "orchestrate agents". Trigger with phrases like 'deploy', 'infrastructure', or 'CI/CD'.
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 deployment engineer specializing in modern CI/CD pipelines, GitOps workflows, and advanced deployment automation. Masters GitHub Actions, ArgoCD/Flux, progressive delivery, container security, and platform engineering. Handles zero-downtime deployments, security scanning, and developer experience optimization. Use PROACTIVELY for CI/CD design, GitOps implementation, or deployment automation.
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。
Intelligently deploys Azure OpenAI models to optimal regions by analyzing capacity across all available regions. Automatically checks current region first and shows alternatives if needed. USE FOR: quick deployment, optimal region, best region, automatic region selection, fast setup, multi-region capacity check, high availability deployment, deploy to best location. DO NOT USE FOR: custom SKU selection (use customize), specific version selection (use customize), custom capacity configuration (use customize), PTU deployments (use customize).
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。
CloudBase Run backend development rules (Function mode/Container mode). Use this skill when deploying backend services that require long connections, multi-language support, custom environments, AI agent development, or migrating existing/GitHub apps that need VPC access to MySQL/PostgreSQL/Redis. Also use when diagnosing CloudRun container deploy failures (deploy_failed, readiness/probe failed, image won't start, docker.io pull loops) or a deploy stuck behind a running deploy task. For stateless HTTP services, prefer HTTP cloud functions.
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 the actual product — from landing page to deployed MVP with payment integration, QA testing, and post-deploy canary monitoring. Handles code generation, deployment, database setup, authentication, Stripe/payment integration, systematic QA protocol, and production health scoring. Use when the user needs to build something, deploy a product, set up payments, create a landing page, or says 'build this', 'deploy', 'create MVP', 'set up payments', or 'ship it'.
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 SAM and AWS CDK deployment for serverless applications. Triggers on phrases like: use SAM, SAM template, SAM init, SAM deploy, CDK serverless, CDK Lambda construct, NodejsFunction, PythonFunction, SAM and CDK together, serverless CI/CD pipeline. For general app deployment with service selection, use deploy-on-aws plugin instead.
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 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).
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。
Generate, simulate, audit, and deploy a Uniswap v4 hook + test pool from a brief, on any Uniswap v4 chain (every testnet and mainnet) - pre-audited templates or a from-scratch freeform hook (flags auto-derived; static audit + dangerous-pattern scan + a behavioral forge test + fork sim gate the deploy). Dry-run by default; explicit arm: to broadcast; testnet default, mainnet behind a double opt-in; records the deploy to main. Every deployed hook inherits a mandatory 10 bps AeonFee protocol fee.
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。