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Build in-game changelogs that connect release notes and displayed versions to successful production deployments. Reconstruct supported history, integrate menu navigation and accessible controls, and verify version consistency and behavior on desktop and mobile.
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Launch a new OpenClaw, PicoClaw, or Ottie deployment through Cloud Claw using the local CLI. Configure the agent type, deployment name, model, and Telegram settings according to the supported launch options and requirements.
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Monitor a live application after deployment and compare it with a saved baseline, a record of its earlier state. Check page responses, loading times, and new errors, flag persistent changes, and produce a per-page health report.
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Take an existing pull request through automated check results, user approval, merging, deployment verification, and a production health check. Report outcomes and timings, with investigation or rollback options when deployment problems appear.
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Running an in-bundle DOCA service as a container on a BlueField (Argus, DMS, Firefly, or UROM service): kubelet standalone watching a static-pod manifests directory, YAML pod-spec drop, kubelet status / ENTRYPOINT logs / per-service liveness. Trigger on "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", even without naming container deployment. A DOCA-linked binary launched directly on the host or Arm belongs to doca-bare-metal-deployment. Covers smoke-before-bulk and the layered error taxonomy (pod-spec, scheduling, image pull, runtime, mount, network, version, host). 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.
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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).
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Generates code that deploys fine-tuned models from SageMaker Serverless Model Customization to SageMaker endpoints or Bedrock. Use when the user says "deploy my model", "create an endpoint", "make it available", or asks about deployment options. Identifies the correct deployment pathway (Nova vs OSS), generates deployment code, and handles endpoint configuration.
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Deploy and manage applications, databases, and services on Coolify. Use when a user asks to deploy an app to Coolify, manage Coolify deployments, check deployment logs, sync environment variables, create or manage databases on Coolify, trigger a deployment, manage Coolify servers, or troubleshoot Coolify issues. Covers the Coolify CLI and REST API. For infrastructure provisioning on Hetzner, see hetzner-cloud.
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Production deployment patterns for MCP servers including Docker, cloud platforms, monitoring, and scalabilityUse when "mcp deployment, deploy mcp server, mcp docker, mcp production, mcp monitoring, mcp, deployment, docker, production, monitoring, scaling" mentioned.
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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.
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Azure Developer CLI (azd) Container Apps Deployment workflow skill. Use this skill when the user needs Deploy containerized frontend + backend applications to Azure Container Apps with remote builds, managed identity, and idempotent infrastructure and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
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Azure Developer CLI (azd) Container Apps Deployment workflow skill. Use this skill when the user needs Deploy containerized frontend + backend applications to Azure Container Apps with remote builds, managed identity, and idempotent infrastructure and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
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Deploy and operate the vehicle insurance data analysis platform. Use when user asks about local development setup, production deployment, server configuration, build process, service management, or troubleshooting deployment issues. Focuses on the project's actual simple deployment model using start_server.sh, not complex enterprise setups.
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Deploy Shiny applications to shinyapps.io, Posit Connect, or Docker containers. Covers rsconnect configuration, manifest generation, Dockerfile creation, and deployment verification. Use when publishing a Shiny app for external or internal users, moving from local development to a hosted environment, containerizing a Shiny app for Kubernetes or Docker deployment, or setting up automated deployment pipelines.
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Deploy applications to Kubernetes clusters using kubectl manifests for Deployments, Services, ConfigMaps, Secrets, and Ingress resources. Implement health checks, resource limits, rolling updates, and Helm chart packaging for production deployments. Use when deploying new applications to EKS, GKE, AKS, or self-hosted clusters, migrating from Docker Compose to container orchestration, implementing zero-downtime rolling updates, or setting up multi-environment deployments across dev, staging, and production.
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Use when deploying an Agentforce agent to a channel surface: Embedded Service web chat, Slack, Experience Cloud, Agent REST API, or Salesforce Mobile. Triggers: 'deploy agent to website', 'add Agentforce to Slack', 'enable agent for mobile app', 'configure embedded service for agent', 'agent API for custom integration', 'multi-channel agent deployment'. NOT for agent logic design — use agentforce/agent-topic-design or agentforce/agent-actions for topics, instructions, and action contracts.
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Design deployment-focused distillation systems that balance model size, accuracy, calibration, and cascade escalation under real resource limits. Best for teacher-student compression, threshold design, and failure-aware deployment. Activate on "model compression", "teacher- student", "distillation score", "cascade model", "edge deployment", or "model calibration". NOT for generic deep-learning overviews, prompt optimization, or training work without a concrete distillation objective.
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Build production CI/CD pipelines with GitHub Actions. Implements matrix builds, caching, deployments, testing, security scanning. Use for automated testing, deployments, release workflows. Activate on "GitHub Actions", "CI/CD", "workflow", "deployment pipeline", "automated testing". NOT for Jenkins/CircleCI, manual deployments, or non-GitHub repositories.
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Plan and implement infrastructure, containers, CI/CD, deployment safety, observability, and rollback changes. Use for Docker, IaC, cloud runtime, Kubernetes, pipeline, deployment, or operational reliability work. Does not read dotenv files or approve/release deployments.
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Validates Azure deployments before they run: Bicep syntax, what-if previews, and permission checks. Use before azd up, azd provision, or az deployment, when preparing or reviewing Bicep files, or to preview what a deployment will change.
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Create a SageMaker endpoint (real-time, real-time scale-to-zero, or async) with autoscaling, CloudWatch alarms, and tagging enabled by default. Use this skill whenever about to create a SageMaker endpoint, write deployment code that calls `create_endpoint`, or finalize a deployment after the image URI and IAM role are known. Provides deploy.py for real-time endpoints, deploy_ic.py for real-time endpoints that scale to zero instances via inference components, and deploy_async.py for async endpoints (also scale-to-zero). This is the last step in the SageMaker deployment workflow. Never generate a bare `create_endpoint` call without these defaults — endpoints without autoscaling or alarms are demos, not deployments.
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通过测试创建、修订和发布前验证 AI 智能体技能，比较使用指令前后的行为，找出遗漏或误解，并调整文档结构、搜索说明和措辞。
为Web应用正式上线准备Docker配置、自动化测试与部署流程，比较更新方式，规划运行检查和版本回退，并通过清单核查上线前的配置与运维准备。
梳理持续运行的 AI 智能体的启停、日志监控、权限管理、分阶段更新与回滚流程，支持故障响应，以及成功率、重试次数、恢复时间和成本评估。